Feb 21 2026

How AI Is Reshaping the Future of Cyber Risk Governance

“Balancing the Scales: What AI Teaches Us About the Future of Cyber Risk Governance”


1. The AI Opportunity and Challenge
Artificial intelligence is rapidly transforming how organizations function and innovate, offering immense opportunity while also introducing significant uncertainty. Leaders increasingly face a central question: How can AI risks be governed without stifling innovation? This issue is a recurring theme in boardrooms and risk committees, especially as enterprises prepare for major industry events like the ISACA Conference North America 2026.

2. Rethinking AI Risk Through Established Lenses
Instead of treating AI as an entirely unprecedented threat, the author suggests applying quantitative governance—a disciplined, measurement-focused approach previously used in other domains—to AI. Grounding our understanding of AI risks in familiar frameworks allows organizations to manage them as they would other complex, uncertain risk profiles.

3. Familiar Risk Categories in New Forms
Though AI may seem novel, the harms it creates—like data poisoning, misleading outputs (hallucinations), and deepfakes—map onto traditional operational risk categories defined decades ago, such as fraud, disruptions to business operations, regulatory penalties, and damage to trust and reputation. This connection is important because it suggests existing governance doctrines can still serve us.

4. New Causes, Familiar Consequences
Where AI differs is in why the risks happen. The article mentions a taxonomy of 13 AI-specific triggers—including things like model drift, lack of explainability, or robustness failures—that drive those familiar risk outcomes. By breaking down these root causes, risk leaders can shift from broad fear of AI to measurable scenarios that can be prioritized and governed.

5. Governance Structures Are Lagging
AI is evolving faster than many governance systems can respond, meaning organizations risk falling behind if their oversight practices remain static. But the author argues that this lag isn’t an inevitability. By combining the discipline of operational risk management, rigorous model validation, and quantitative analysis, governance can be scalable and effective for AI systems.

6. Continuity Over Reinvention
A key theme is continuity: AI doesn’t require entirely new governance frameworks but rather an extension of what already exists, adapted to account for AI’s unique behaviors. This reduces the need to reinvent the wheel and gives risk practitioners concrete starting points rooted in established practice.

7. Reinforcing the Role of Governance
Ultimately, the article emphasizes that AI doesn’t diminish the need for strong governance—it amplifies it. Organizations that integrate traditional risk management methods with AI-specific insights can oversee AI responsibly without overly restricting its potential to drive innovation.


My Opinion

This article strikes a sensible balance between AI optimism and risk realism. Too often, AI is treated as either a magical solution that solves every problem or an existential threat requiring entirely new paradigms. Grounding AI risk in established governance frameworks is pragmatic and empowers most organizations to act now rather than wait for perfect AI-specific standards. The suggestion to incorporate quantitative risk approaches is especially useful—if done well, it makes AI oversight measurable and actionable rather than vague.

However, the reality is that AI’s rapid evolution may still outpace some traditional controls, especially in areas like explainability, bias, and autonomous decision-making. So while extending existing governance frameworks is a solid starting point, organizations should also invest in developing deeper AI fluency internally, including cross-functional teams that merge risk, data science, and ethical perspectives.

Source: What AI Teaches Us About the Future of Cyber Risk Governance

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Risk


Feb 17 2026

NIST CSF and ISO 27001: Reducing Security Chaos Through Layered Frameworks

Category: Information Security,ISO 27k,NIST CSFdisc7 @ 9:42 am

Security frameworks exist to reduce chaos in how organizations manage risk. Without a shared structure, every company invents its own way of “doing security,” which leads to inconsistent controls, unclear responsibilities, and hidden blind spots. This post illustrates how two major frameworks — National Institute of Standards and Technology’s Cybersecurity Framework (NIST CSF) and International Organization for Standardization’s ISO/IEC 27001 — approach this challenge from complementary angles. Together, they bring order to everyday security operations by defining both what to protect and how to manage protection over time.

The NIST CSF acts like a master technical architect. It provides a practical blueprint for implementing safeguards: identifying assets, protecting systems, detecting threats, responding to incidents, and recovering from disruptions. Its strength lies in being implementation-focused and highly actionable. Organizations use NIST to harden their environment, close technical gaps, and standardize best practices. By offering a common language and structured set of controls, NIST reduces operational confusion, aligns teams around clear priorities, and makes day-to-day risk management more predictable and measurable.

ISO/IEC 27001, on the other hand, focuses on governance and sustainability. Rather than concentrating on specific technical controls, it builds a management system — an Information Security Management System (ISMS) — that ensures security processes are repeatable, accountable, and continuously improved. It defines roles, policies, oversight mechanisms, and audit structures that keep security running as a disciplined business function. Certification under ISO 27001 signals assurance and trust to customers and stakeholders. In practical terms, ISO reduces chaos by embedding security into organizational routines, clarifying ownership, and ensuring that protections don’t fade over time.

When layered together, these frameworks create a powerful system. NIST provides the technical depth to design and operationalize safeguards, while ISO 27001 supplies the governance engine that sustains them. Mature organizations rarely treat this as an either-or decision. They use NIST to shape their technical security architecture and ISO 27001 to institutionalize it through management processes and external assurance. This layered approach addresses both technical risk and trust risk — the need to protect systems and the need to prove that protection is consistently maintained.

From my perspective, asking whether we need both frameworks is really a question about organizational maturity and goals. If a company is struggling with technical implementation, NIST offers immediate practical guidance. If it needs to demonstrate credibility and long-term governance, ISO 27001 becomes essential. In reality, most organizations benefit from combining them: NIST drives effective execution, and ISO ensures durability and trust. Together, they transform security from a reactive set of tasks into a structured, sustainable discipline that meaningfully reduces everyday operational chaos.

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | AIMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: iso 27001, NIST CSF


Feb 14 2026

Understanding Blockchain: A Visual Walkthrough of the Technology

Category: Crypto,Information Securitydisc7 @ 9:22 am

Blockchain 101: Understanding the Basics Through a Visual

Think of cryptocurrency as a new kind of digital money that exists only on the internet and doesn’t rely on banks or governments to run it.

A good way to understand it is by starting with the most famous example: Bitcoin.


What is cryptocurrency?

Cryptocurrency is digital money secured by cryptography (advanced math used to protect information). Instead of a bank keeping track of who owns what, transactions are recorded on a public digital ledger called a blockchain.

You can imagine blockchain as a shared Google Sheet that thousands of computers around the world constantly verify and update. No single company controls it.

Key features:

  • 💻 Digital only – no physical coins or bills
  • 🌍 Decentralized – not controlled by one government or bank
  • 🔒 Secure – protected by cryptography
  • 📜 Transparent – transactions are recorded publicly

How does cryptocurrency work?

Most cryptocurrencies run on a blockchain network.

Here’s a simplified flow:

  1. You create a wallet
    A crypto wallet is like a digital bank account. It has:
    • a public address (like your email you can share)
    • a private key (like your password — keep it secret)
  2. You send a transaction
    When you send crypto, your wallet signs the transaction with your private key.
  3. The network verifies it
    Thousands of computers (called nodes or miners/validators) check that:
    • you actually own the funds
    • you aren’t spending the same money twice
  4. The transaction is added to the blockchain
    Once verified, it’s grouped with others into a “block” and permanently recorded.

After that, the transaction can’t easily be changed.


Benefits of cryptocurrency

1. Faster global payments

You can send money anywhere in the world in minutes, often cheaper than banks.

2. No middleman required

You don’t need a bank or payment company to approve transactions.

3. Financial access

Anyone with internet access can use crypto — helpful in places with weak banking systems.

4. Transparency and security

Transactions are public and hard to tamper with.

5. Programmable money

Some cryptocurrencies (like Ethereum) allow smart contracts — programs that automatically execute agreements.


Example: A simple crypto transaction

Let’s walk through a real-world style example.

Scenario:
Alice wants to send $20 worth of Bitcoin to Bob for helping with a project.

Step-by-step:

  1. Alice opens her wallet app and enters Bob’s public address.
  2. She types in the amount and presses Send.
  3. Her wallet signs the transaction with her private key.
  4. The Bitcoin network checks that Alice has enough funds.
  5. The transaction is added to the blockchain.
  6. Bob sees the payment appear in his wallet.

Time: ~10 minutes (depending on network traffic)
No bank involved.

It’s similar to handing someone cash — but done digitally and verified by a global network.


Simple analogy

Think of cryptocurrency like:

Email for money

Before email, sending letters took days and required postal systems.
Crypto lets you send money across the internet as easily as sending an email.


Important things to know (balanced view)

While crypto has benefits, it also has challenges:

  • ⚠️ Prices can be very volatile
  • 🔐 If you lose your private key, you may lose your funds
  • 🧾 Regulations are still evolving
  • 🧠 It has a learning curve

let’s walk through the diagram step by step in plain language, like you would in a classroom.

This diagram is showing how a blockchain records a transaction (like sending money using Bitcoin).


Step 1: New transactions are created

On the left side, you see a list of new transactions (for example: Alice sends money to Bob).

Think of this as:

👉 People requesting to send digital money to each other.

At this stage, the transactions are waiting to be verified.


Step 2: Transactions are grouped into a block

In the next section, those transactions are packed into a block.

A block is like a container or page in a notebook that stores:

  • A list of transactions
  • A timestamp (when it happened)
  • A unique security code (called a hash)

This security code links the block to the previous block — like a chain link.


Step 3: The network of computers verifies the block

In the middle of the diagram, you see many connected computers.

These computers form a global network that checks:

  • Are the transactions valid?
  • Does the sender actually have the funds?
  • Is anyone trying to cheat?

If most computers agree the transactions are valid, the block is approved.

Think of it like a group of students checking each other’s math homework to make sure it’s correct.


Step 4: The block is added to the chain

Once approved, the block is attached to previous blocks, forming a chain of blocks — this is the blockchain.

Each new block connects to the one before it using cryptographic links.

This makes it very hard to change past records, because you would have to change every block after it.


Step 5: Permanent record stored everywhere

On the far right, the diagram shows a secure folder.

This represents the permanent record:

  • The transaction is now finalized
  • It’s copied and stored across thousands of computers
  • It cannot easily be altered

This is what makes blockchain secure and transparent.


Big picture summary

The diagram shows this simple flow:

👉 Transaction → Block → Verification → Chain → Permanent record

In other words:

Someone sends crypto → it gets verified by many computers → it becomes a permanent part of a shared digital ledger.


Here is another digital transaction exmple: in this example Sam want to send digital asset ($$) to Mark

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | AIMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: blockchain, cryptocurrency


Feb 13 2026

Securing Web3: A Practical Guide to the OWASP Smart Contract Top 10 (2026)

Category: Information Security,Smart Contractdisc7 @ 8:51 am


📘 What Is the OWASP Smart Contract Top 10?

The OWASP Smart Contract Top 10 is an industry-standard awareness and guidance document for Web3 developers and security teams detailing the most critical classes of vulnerabilities in smart contracts. It’s based on real attacks and expert analysis and serves as both a checklist for secure design and an audit reference to help reduce risk before deployment.


🔍 The 2026 Smart Contract Top 10 (Rephrased & Explained)

SC01 – Access Control Vulnerabilities

What it is: Happens when a contract fails to restrict who can call sensitive functions (like minting, admin changes, pausing, or upgrades).
Why it matters: Without proper permission checks, attackers can take over critical actions, change ownership, steal funds, or manipulate state.
Mitigation: Use well-tested access control libraries (e.g., Ownable, RBAC), apply permissions modifiers, and ensure admin/initialization functions are restricted to trusted roles.
👉 Ensures only authorized actors can invoke critical logic.


SC02 – Business Logic Vulnerabilities

What it is: Flaws in how contract logic is designed, not just coded (e.g., incorrect accounting, faulty rewards, broken lending logic).
Why it matters: Even if code is syntactically correct, logic errors can be exploited to drain funds or warp protocol economics.
Mitigation: Thoroughly define intended behavior, write comprehensive tests, and undergo peer reviews and professional audits.
👉 Helps verify that the contract does what it should, not just compiles.


SC03 – Price Oracle Manipulation

What it is: Contracts often rely on external price feeds (“oracles”). If those feeds can be tampered with or spoofed, protocol logic behaves incorrectly.
Why it matters: Manipulated price data can trigger unfair liquidations, bad trades, or exploit chains that profit the attacker.
Mitigation: Use decentralized or robust oracle networks with slippage limits, price aggregation, and sanity checks.
👉 Prevents external data from being a weak link in internal calculations.


SC04 – Flash Loan–Facilitated Attacks

What it is: Flash loans let attackers borrow large amounts with no collateral within one transaction and manipulate a protocol.
Why it matters: Small vulnerabilities in pricing or logic can be leveraged with borrowed capital to cause big economic damage.
Mitigation: Include checks that prevent manipulations during a single transaction (e.g., TWAP pricing, re-pricing guards, invariants).
👉 Stops attackers from using borrowed capital as an offensive weapon.


SC05 – Lack of Input Validation

What it is: A contract accepts values (addresses, amounts, parameters) without checking they are valid or within expected ranges.
Why it matters: Bad input can lead to malformed state, unexpected behavior, or exploitable conditions.
Mitigation: Validate and sanitize all inputs — reject zero addresses, negative amounts, out-of-range values, and unexpected data shapes.
👉 Reduces the risk of attackers “feeding” bad data into sensitive functions.


SC06 – Unchecked External Calls

What it is: The contract calls external code but doesn’t check if those calls succeed or how they influence its state.
Why it matters: A failing external call can leave a contract in an inconsistent state and expose it to exploits.
Mitigation: Always check return values or use Solidity patterns that handle call failures explicitly (e.g., require).
👉 Ensures your logic doesn’t blindly trust other contracts or addresses.


SC07 – Arithmetic Errors (Rounding & Precision)

What it is: Mistakes in math operations — rounding, scaling, and precision errors — especially around decimals or shares.
Why it matters: In DeFi, small arithmetic mistakes can be exploited repeatedly or magnified with flash loans.
Mitigation: Use safe math libraries and clearly define how rounding/truncation should work. Consider fixed-point libraries with clear precision rules.
👉 Avoids subtle calculation bugs that can siphon value over time.


SC08 – Reentrancy Attacks

What it is: A contract calls an external contract before updating its own state. A malicious callee re-enters and manipulates state repeatedly.
Why it matters: This classic attack can drain funds, corrupt internal accounting, or turn single actions into repeated ones.
Mitigation: Update state before external calls, use reentrancy guards, and follow established secure patterns.
👉 Prevents an external party from interrupting your logic in a harmful order.


SC09 – Integer Overflow and Underflow

What it is: Arithmetic exceeds the maximum or minimum representable integer value, causing wrap-around behavior.
Why it matters: Attackers can exploit wrapped values to inflate balances or break invariants.
Mitigation: Use Solidity’s built-in checked arithmetic (since 0.8.x) or libraries that revert on overflow/underflow.
👉 Stops attackers from exploiting unexpected number behavior.


SC10 – Proxy & Upgradeability Vulnerabilities

What it is: Misconfigured upgrade mechanisms or proxy patterns let attackers take over contract logic or state.
Why it matters: Many modern protocols support upgrades; an insecure path can allow malicious re-deployments, unauthorized initialization, or bypass of intended permissions.
Mitigation: Secure admin keys, guard initializer functions, and use time-locked governance for upgrades.
👉 Ensures upgrade patterns do not become new attack surfaces.


💡 How the Top 10 Helps Build Better Smart Contracts

  • Security baseline: Provides a structured checklist for teams to review and assess risk throughout development and before deployment.
  • Risk prioritization: Highlights the most exploited or impactful vulnerabilities seen in real attacks, not just academic theory.
  • Design guidance: Encourages developers to bake security into requirements, design, testing, and deployment — not just fix bugs reactively.
  • Audit support: Auditors and reviewers can use the Top 10 as a framework to validate coverage and threat modeling.

🧠 Feedback Summary

The OWASP Smart Contract Top 10 is valuable because it combines empirical data and expert consensus to pinpoint where real smart contract breaches occur. It moves beyond generic lists to specific classes tailored for blockchain platforms. As a result:

  • It helps developers avoid repeat mistakes made by others.
  • It provides practical remediations rather than abstract guidance.
  • It supports continuous improvement in smart contract practices as the threat landscape evolves.

Using this list early in design (not just before audits) can elevate security hygiene and reduce costly exploits.


Below are practical Solidity defense patterns and code snippets mapped to each item in the OWASP Smart Contract Top 10 (2026). These are simplified examples meant to illustrate secure design patterns, not production-ready contracts.


SC01 — Access Control Vulnerabilities

Defense pattern: Role-based access control + modifiers

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;

contract AccessControlled {
    address public owner;

    modifier onlyOwner() {
        require(msg.sender == owner, "Not authorized");
        _;
    }

    constructor() {
        owner = msg.sender;
    }

    function updateOwner(address newOwner) external onlyOwner {
        require(newOwner != address(0), "Invalid address");
        owner = newOwner;
    }
}

Key idea: Always gate sensitive functions with explicit permission checks.


SC02 — Business Logic Vulnerabilities

Defense pattern: Invariant checks + sanity validation

contract Vault {
    mapping(address => uint256) public balances;
    uint256 public totalDeposits;

    function deposit() external payable {
        balances[msg.sender] += msg.value;
        totalDeposits += msg.value;

        // Invariant check
        assert(address(this).balance == totalDeposits);
    }
}

Key idea: Encode assumptions as invariants and assertions to catch logic flaws.


SC03 — Price Oracle Manipulation

Defense pattern: Use time-weighted average price (TWAP) checks

interface IOracle {
    function getTWAP() external view returns (uint256);
}

contract PriceConsumer {
    IOracle public oracle;
    uint256 public maxDeviation = 5; // %

    function validatePrice(uint256 marketPrice) public view returns (bool) {
        uint256 oraclePrice = oracle.getTWAP();
        uint256 diff = (oraclePrice * maxDeviation) / 100;

        return (
            marketPrice >= oraclePrice - diff &&
            marketPrice <= oraclePrice + diff
        );
    }
}

Key idea: Don’t trust a single spot price; add bounds and sanity checks.


SC04 — Flash Loan–Facilitated Attacks

Defense pattern: Transaction-level guardrails

contract FlashLoanGuard {
    uint256 public lastActionBlock;

    modifier noSameBlock() {
        require(block.number > lastActionBlock, "Flash attack blocked");
        _;
        lastActionBlock = block.number;
    }

    function sensitiveOperation() external noSameBlock {
        // critical logic
    }
}

Key idea: Prevent atomic manipulation by adding timing/state constraints.


SC05 — Lack of Input Validation

Defense pattern: Strict parameter validation

function transfer(address to, uint256 amount) external {
    require(to != address(0), "Zero address");
    require(amount > 0, "Invalid amount");
    require(balances[msg.sender] >= amount, "Insufficient balance");

    balances[msg.sender] -= amount;
    balances[to] += amount;
}

Key idea: Validate all external inputs before state changes.


SC06 — Unchecked External Calls

Defense pattern: Check call results explicitly

function safeCall(address target, bytes calldata data) external {
    (bool success, bytes memory result) = target.call(data);
    require(success, "External call failed");

    // Optionally decode and validate result
}

Key idea: Never ignore return values from external calls.


SC07 — Arithmetic Errors (Precision/Rounding)

Defense pattern: Fixed-point math discipline

uint256 constant PRECISION = 1e18;

function calculateShare(uint256 amount, uint256 ratio)
    public
    pure
    returns (uint256)
{
    return (amount * ratio) / PRECISION;
}

Key idea: Use consistent scaling factors to control rounding behavior.


SC08 — Reentrancy Attacks

Defense pattern: Checks-Effects-Interactions + guard

contract ReentrancySafe {
    mapping(address => uint256) public balances;
    bool private locked;

    modifier nonReentrant() {
        require(!locked, "Reentrant call");
        locked = true;
        _;
        locked = false;
    }

    function withdraw(uint256 amount) external nonReentrant {
        require(balances[msg.sender] >= amount);

        // Effects first
        balances[msg.sender] -= amount;

        // Interaction last
        payable(msg.sender).transfer(amount);
    }
}

Key idea: Update internal state before external calls.


SC09 — Integer Overflow & Underflow

Defense pattern: Use Solidity ≥0.8 checked math

function safeAdd(uint256 a, uint256 b)
    public
    pure
    returns (uint256)
{
    return a + b; // auto-reverts on overflow in Solidity 0.8+
}

Key idea: Rely on compiler protections and avoid unchecked unless justified.


SC10 — Proxy & Upgradeability Vulnerabilities

Defense pattern: Secure initializer + upgrade restriction

contract Upgradeable {
    address public admin;
    bool private initialized;

    modifier onlyAdmin() {
        require(msg.sender == admin, "Not admin");
        _;
    }

    function initialize(address _admin) external {
        require(!initialized, "Already initialized");
        require(_admin != address(0));
        admin = _admin;
        initialized = true;
    }

    function upgrade(address newImpl) external onlyAdmin {
        require(newImpl != address(0));
        // upgrade logic
    }
}

Key idea: Prevent re-initialization and tightly control upgrade authority.


Practical Takeaway

These patterns collectively enforce a secure smart contract lifecycle:

  • Restrict authority (who can act)
  • Validate assumptions (what is allowed)
  • Protect math and logic (how it behaves)
  • Guard external interactions (who you trust)
  • Secure upgrades (how it evolves)

They translate abstract vulnerability categories into repeatable engineering habits.


Here’s a practical mapping of the OWASP Smart Contract Top 10 (2026) to a real-world smart contract audit workflow — structured the way professional auditors actually run engagements.

I’ll show:

👉 Audit phase → What auditors do → Which Top 10 risks are checked → Tools & techniques


Smart Contract Audit Workflow Mapped to OWASP Top 10

1. Scope Definition & Threat Modeling

Goal: Understand architecture, trust boundaries, and attack surface before touching code.

What auditors do

  • Review protocol architecture diagrams
  • Identify privileged roles and external dependencies
  • Map trust assumptions (oracles, bridges, governance)
  • Define attacker models

Top 10 focus

  • SC01 — Access Control
  • SC02 — Business Logic
  • SC03 — Oracle Risks
  • SC10 — Upgradeability

Key audit questions

  • Who controls admin keys?
  • What happens if an privileged actor is compromised?
  • Can economic incentives be abused?

Output

  • Threat model document
  • Attack surface map
  • Risk prioritization matrix


2. Architecture & Design Review

Goal: Validate that the protocol design itself is secure.

This happens before deep code inspection.

What auditors do

  • Review system invariants
  • Analyze economic assumptions
  • Evaluate upgrade mechanisms
  • Review oracle integration design

Top 10 focus

  • SC02 — Business Logic
  • SC03 — Oracle Manipulation
  • SC04 — Flash Loan Attacks
  • SC10 — Proxy/Upgradeability

Techniques

  • Economic modeling
  • Scenario walkthroughs
  • Failure mode analysis

Output

  • Design weaknesses list
  • Architecture recommendations


3. Automated Static Analysis

Goal: Catch common coding mistakes quickly.

What auditors do

Run automated scanners to detect:

  • Reentrancy risks
  • Arithmetic errors
  • Unchecked calls
  • Input validation issues

Top 10 focus

  • SC05 — Input Validation
  • SC06 — Unchecked External Calls
  • SC07 — Arithmetic Errors
  • SC08 — Reentrancy
  • SC09 — Overflow/Underflow

Common tools

  • Slither
  • Mythril
  • Foundry fuzzing
  • Echidna

Output

  • Machine-generated vulnerability list
  • False-positive triage


4. Manual Code Review (Deep Dive)

Goal: Find subtle vulnerabilities automation misses.

This is the core of a professional audit.

What auditors do

Line-by-line review of:

  • Permission checks
  • State transitions
  • External call patterns
  • Edge cases

Top 10 focus

👉 All categories, especially:

  • SC01 — Access Control
  • SC02 — Business Logic
  • SC08 — Reentrancy
  • SC10 — Upgradeability

Techniques

  • Adversarial reasoning
  • Attack simulation
  • Logic tracing

Output

  • Detailed vulnerability report
  • Severity classification


5. Dynamic Testing & Fuzzing

Goal: Stress test the contract under adversarial conditions.

What auditors do

  • Fuzz inputs
  • Simulate flash loan attacks
  • Test extreme edge cases
  • Validate invariants

Top 10 focus

  • SC04 — Flash Loan Attacks
  • SC07 — Arithmetic Errors
  • SC08 — Reentrancy
  • SC02 — Business Logic

Output

  • Exploit reproducibility evidence
  • Proof-of-concept attack cases


6. Economic Attack Simulation

Goal: Evaluate real-world exploitability.

This is crucial for DeFi protocols.

What auditors do

  • Simulate price manipulation
  • Test liquidity attacks
  • Analyze arbitrage vectors

Top 10 focus

  • SC03 — Oracle Manipulation
  • SC04 — Flash Loan Attacks
  • SC02 — Business Logic

Output

  • Attack scenarios
  • Economic impact assessment


7. Upgrade & Governance Security Review

Goal: Prevent takeover or governance abuse.

What auditors do

  • Inspect proxy patterns
  • Review admin privileges
  • Evaluate governance safeguards

Top 10 focus

  • SC01 — Access Control
  • SC10 — Upgradeability

Output

  • Governance risk assessment
  • Key management recommendations


8. Reporting & Remediation Guidance

Goal: Deliver actionable fixes.

What auditors provide

  • Severity-ranked findings
  • Code patch recommendations
  • Secure design patterns
  • Retest verification

Top 10 coverage

Each finding is mapped to a Top 10 category to ensure full coverage.


How This Workflow Improves Smart Contract Security

Mapping audits to the OWASP Top 10 creates:

✅ Structured coverage

No major risk category gets overlooked.

✅ Repeatable methodology

Teams can standardize audit practices.

✅ Measasurable security maturity

Organizations can track improvements over time.

✅ Faster remediation

Developers understand root causes, not just symptoms.


Practical Audit Checklist (Condensed)

Here’s a field-ready checklist auditors often use:

  • Access roles verified and minimized
  • Business logic invariants documented
  • Oracle dependencies stress-tested
  • Flash loan attack scenarios simulated
  • Input validation enforced everywhere
  • External calls checked and guarded
  • Arithmetic precision validated
  • Reentrancy protections implemented
  • Overflow protections confirmed
  • Upgrade paths locked down

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot | Comprehensive vCISO Services | ISMS Services | AIMS Services | Security Risk Assessment Services | Mergers and Acquisition Security

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

 

Tags: OWASP Smart Contract Top 10


Feb 10 2026

From Ethics to Enforcement: The AI Governance Shift No One Can Ignore

Category: AI,AI Governance,Information Security,ISO 42001disc7 @ 1:24 pm

AI Governance Defined
AI governance is the framework of rules, controls, and accountability that ensures AI systems behave safely, ethically, transparently, and in compliance with law and business objectives. It goes beyond principles to include operational evidence — inventories, risk assessments, audit logs, human oversight, continuous monitoring, and documented decision ownership. In 2026, governance has moved from aspirational policy to mission-critical operational discipline that reduces enterprise risk and enables scalable, responsible AI adoption.


1. From Model Outputs → System Actions

What’s Changing:
Traditionally, risk focus centered on the outputs models produce — e.g., biased text or inaccurate predictions. But as AI systems become agentic (capable of acting autonomously in the world), the real risks lie in actions taken, not just outputs. That means governance must now cover runtime behaviour, include real-time monitoring, automated guardrails, and defined escalation paths.

My Perspective:
This shift recognizes that AI isn’t just a prediction engine — it can initiate transactions, schedule activities, and make decisions with real consequences. Governance must evolve accordingly, embedding control closer to execution and amplifying responsibilities around when and how the system interacts with people, data, and money. It’s a maturity leap from “what did the model say?” to “what did the system do?” — and that’s critical for legal defensibility and trust.


2. Enforcement Scales Beyond Pilots

What’s Changing:
What was voluntary guidance has become enforceable regulation. The EU AI Act’s high-risk rules kick in fully in 2026, and U.S. states are applying consumer protection and discrimination laws to AI behaviours. Regulators are even flagging documentation gaps as violations. Compliance can no longer be a single milestone; it must be a continuous operational capability similar to cybersecurity controls.

My Perspective:
This shift is seismic: AI governance now carries real legal and financial consequences. Organizations can’t rely on static policies or annual audits — they need ongoing evidence of how models are monitored, updated, and risk-assessed. Treating governance like a continuous control discipline closes the gap between intention and compliance, and is essential for risk-aware, evidence-ready AI adoption at scale.


3. Healthcare AI Signals Broader Direction

What’s Changing:
Regulated sectors like healthcare are pushing transparency, accountability, explainability, and documented risk assessments to the forefront. “Black-box” clinical algorithms are increasingly unacceptable; models must justify decisions before being trusted or deployed. What happens in healthcare is a leading indicator of where other regulated industries — finance, government, critical infrastructure — will head.

My Perspective:
Healthcare is a proving ground for accountable AI because the stakes are human lives. Requiring explainability artifacts and documented risk mitigation before deployment sets a new bar for governance maturity that others will inevitably follow. This trend accelerates the demise of opaque, undocumented AI practices and reinforces governance not as overhead, but as a deployment prerequisite.


4. Governance Moves Into Executive Accountability

What’s Changing:
AI governance is no longer siloed in IT or ethics committees — it’s now a board-level concern. Leaders are asking not just about technology but about risk exposure, audit readiness, and whether governance can withstand regulatory scrutiny. “Governance debt” (inconsistent, siloed, undocumented oversight) becomes visible at the highest levels and carries cost — through fines, forced system rollbacks, or reputational damage.

My Perspective:
This shift elevates governance from a back-office activity to a strategic enterprise risk function. When executives are accountable for AI risk, governance becomes integrated with legal, compliance, finance, and business strategy, not just technical operations. That integration is what makes governance resilient, auditable, and aligned with enterprise risk tolerance — and it signals that responsible AI adoption is a competitive differentiator, not just a compliance checkbox.


In Summary: The 2026 AI Governance Reality

AI governance in 2026 isn’t about writing policies — it’s about operationalizing controls, documenting evidence, and embedding accountability into AI lifecycles. These four shifts reflect the move from static principles to dynamic, enterprise-grade governance that manages risk proactively, satisfies regulators, and builds trust with stakeholders. Organizations that embrace this shift will not only reduce risk but unlock AI’s value responsibly and sustainably.


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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Governance


Feb 09 2026

Understanding the Real Difference Between ISO 42001 and the EU AI Act

Category: AI,AI Governance,Information Security,ISO 42001disc7 @ 8:41 am

Certified ≠ Compliant

1. The big picture
The image makes one thing very clear: ISO/IEC 42001 and the EU AI Act are related, but they are not the same thing. They overlap in intent—safe, responsible, and trustworthy AI—but they come from two very different worlds. One is a global management standard; the other is binding law.

2. What ISO/IEC 42001 really is
ISO/IEC 42001 is an international, voluntary standard for establishing an AI Management System (AIMS). It focuses on how an organization governs AI—policies, processes, roles, risk management, and continuous improvement. Being certified means you have a structured system to manage AI risks, not that your AI systems are legally approved for use in every jurisdiction.

3. What the EU AI Act actually does
The EU AI Act is a legal and regulatory framework specific to the European Union. It defines what is allowed, restricted, high-risk, or outright prohibited in AI systems. Compliance is mandatory, enforceable by regulators, and tied directly to penalties, market access, and legal exposure.

4. The shared principles that cause confusion
The overlap is real and meaningful. Both ISO 42001 and the EU AI Act emphasize transparency and accountability, risk management and safety, governance and ethics, documentation and reporting, data quality, human oversight, and trustworthy AI outcomes. This shared language often leads companies to assume one equals the other.

5. Where ISO 42001 stops short
ISO 42001 does not classify AI systems by risk level. It does not tell you whether your system is “high-risk,” “limited-risk,” or prohibited. Without that classification, organizations may build solid governance processes—while still governing the wrong risk category.

6. Conformity versus certification
ISO 42001 certification is voluntary and typically audited by certification bodies against management system requirements. The EU AI Act, however, can require formal conformity assessments, sometimes involving notified third parties, especially for high-risk systems. These are different auditors, different criteria, and very different consequences.

7. The blind spot around prohibited AI practices
ISO 42001 contains no explicit list of banned AI use cases. The EU AI Act does. Practices like social scoring, certain emotion recognition in workplaces, or real-time biometric identification may be illegal regardless of how mature your management system is. A well-run AIMS will not automatically flag illegality.

8. Enforcement and penalties change everything
Failing an ISO audit might mean corrective actions or losing a certificate. Failing the EU AI Act can mean fines of up to €35 million or 7% of global annual turnover, plus reputational and operational damage. The risk profiles are not even in the same league.

9. Certified does not mean compliant
This is the core message in the image and the text: ISO 42001 certification proves governance maturity, not legal compliance. The EU AI Act qualification proves regulatory alignment, not management system excellence. One cannot substitute for the other.

10. My perspective
Having both ISO 42001 certification and EU AI Act qualification exposes a hard truth many consultants gloss over: compliance frameworks do not stack automatically. ISO 42001 is a strong foundation—but it is not the finish line. Your certificate shows you are organized; it does not prove you are lawful. In AI governance, certified ≠ compliant, and knowing that difference is where real expertise begins.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: EU AI Act, ISO 42001


Feb 06 2026

A Practical Guide to Security Risk Assessments That Actually Matter

Category: Information Security,Security Risk Assessmentdisc7 @ 8:59 am


Security Risk Assessments: Choosing the Right Test at the Right Time

Cybersecurity isn’t about running every assessment available—it’s about selecting the right assessment based on your organization’s risk, maturity, and business context. Each security assessment answers a different question across people, process, and technology. When used correctly, they improve resilience, reduce waste, and deliver measurable ROI.

Below is a practical breakdown of the 10 key types of security assessments, their purpose, and when to use them.


Enterprise Risk Assessment

An enterprise risk assessment provides an organization-wide view of critical assets, threats, and potential business impact.
Purpose: To help executives and boards understand cyber risk in business terms.
When to use: When establishing a security baseline, prioritizing investments, or aligning security strategy with business objectives.


Gap Assessment

A gap assessment compares current controls against frameworks like ISO 27001, SOC 2, PCI DSS, HIPAA, or GDPR.
Purpose: To identify compliance and control gaps.
When to use: When preparing for audits, certifications, customer due diligence, or regulatory reviews.


Vulnerability Assessment

This assessment uses automated scanning and validation to identify known technical weaknesses.
Purpose: To uncover exploitable vulnerabilities and hygiene issues.
When to use: On a recurring basis (monthly or quarterly) to guide patching and configuration management.


Network Penetration Test

A human-led attack simulation focused on networks and hosts.
Purpose: To test how real attackers could compromise systems and move laterally.
When to use: For new environments, after major infrastructure changes, or annually for deep testing.


Application Security Test

This assessment targets applications and APIs for authentication, input validation, business logic, and data handling flaws.
Purpose: To reduce application-layer risk and prevent data breaches.
When to use: Before major releases or for applications handling sensitive data or payments.


Red Team Exercise

A stealthy, goal-driven adversary simulation spanning people, process, and technology.
Purpose: To test detection, response, and organizational readiness—not just prevention.
When to use: When baseline security hygiene is strong and you want to validate end-to-end defenses.


Cloud Security Assessment

A review of cloud configurations, IAM, logging, network design, and security posture.
Purpose: To reduce misconfigurations and cloud-native risks.
When to use: If you’re cloud-first, multi-cloud, or scaling rapidly.


Architecture Review

A forward-looking assessment focused on threat modeling and secure design.
Purpose: To prevent risk before systems are built.
When to use: When designing, replatforming, or integrating major applications or APIs.


Phishing Assessment

Controlled phishing and social engineering simulations targeting users.
Purpose: To measure human risk and security awareness effectiveness.
When to use: When improving security culture or validating training programs with real data.


Incident Response Readiness

Scenario-based exercises that test incident response plans and coordination.
Purpose: To ensure teams can respond effectively under pressure.
When to use: Annually, after major changes, or following a real incident.


Key Takeaway

Security risk assessments are not interchangeable—and they are not checkboxes. Organizations that align assessments to risk maturity, business growth, and regulatory pressure consistently outperform those that test blindly.

  • Maturity-driven security beats checkbox security
  • Smart assessment selection improves resilience and ROI
  • The right test, at the right time, makes security defensible and scalable

A well-designed assessment strategy turns security from a cost center into a risk management advantage.

💡 The real question: Which assessment has delivered the most value in your organization—and why?

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Security Risk Assessment


Feb 03 2026

The Invisible Workforce: How Unmonitored AI Agents Are Becoming the Next Major Enterprise Security Risk

Category: AI,AI Governance,AI Guardrails,Information Securitydisc7 @ 3:30 pm

How Unmonitored AI agents are becoming the next major enterprise security risk

1. A rapidly growing “invisible workforce.”
Enterprises in the U.S. and U.K. have deployed an estimated 3 million autonomous AI agents into corporate environments. These digital agents are designed to perform tasks independently, but almost half—about 1.5 million—are operating without active governance or security oversight. (Security Boulevard)

2. Productivity vs. control.
While businesses are embracing these agents for efficiency gains, their adoption is outpacing security teams’ ability to manage them effectively. A survey of technology leaders found that roughly 47 % of AI agents are ungoverned, creating fertile ground for unintended or chaotic behavior.

3. What makes an agent “rogue”?
In this context, a rogue agent refers to one acting outside of its intended parameters—making unauthorized decisions, exposing sensitive data, or triggering significant security breaches. Because they act autonomously and at machine speed, such agents can quickly elevate risks if not properly restrained.

4. Real-world impacts already happening.
The research revealed that 88 % of firms have experienced or suspect incidents involving AI agents in the past year. These include agents using outdated information, leaking confidential data, or even deleting entire datasets without authorization.

5. The readiness gap.
As organizations prepare to deploy millions more agents in 2026, security teams feel increasingly overwhelmed. According to industry reports, while nearly all professionals acknowledge AI’s efficiency benefits, nearly half feel unprepared to defend against AI-driven threats.

6. Call for better governance.
Experts argue that the same discipline applied to traditional software and APIs must be extended to autonomous agents. Without governance frameworks, audit trails, access control, and real-time monitoring, these systems can become liabilities rather than assets.

7. Security friction with innovation.
The core tension is clear: organizations want the productivity promises of agentic AI, but security and operational controls lag far behind adoption, risking data breaches, compliance failures, and system outages if this gap isn’t closed.


My Perspective

The article highlights a central tension in modern AI adoption: speed of innovation vs. maturity of security practices. Autonomous AI agents are unlike traditional software assets—they operate with a degree of unpredictability, act on behalf of humans, and often wield broad access privileges that traditional identity and access management tools were never designed to handle. Without comprehensive governance frameworks, real-time monitoring, and rigorous identity controls, these agents can easily turn into insider threats, amplified by their speed and autonomy (a theme echoed across broader industry reporting).

From a security and compliance viewpoint, this demands a shift in how organizations think about non-human actors: they should be treated with the same rigor as privileged human users—including onboarding/offboarding workflows, continuous risk assessment, and least-privilege access models. Ignoring this is likely to result in not if but when incidents with serious operational and reputational consequences occur. In short, governance needs to catch up with innovation—or the invisible workforce could become the source of visible harm.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Agents, The Invisible workforce


Feb 02 2026

AutoPentestX: Automating End-to-End Penetration Testing for Modern Security Teams

Category: Information Security,Pen Testdisc7 @ 2:46 pm

AutoPentestX is an open-source automated penetration testing framework that brings multiple security testing capabilities into a single, unified platform for Linux environments. Designed for ethical hacking and security auditing, it aims to simplify and accelerate penetration testing by removing much of the manual setup traditionally required.

Created by security researcher Gowtham-Darkseid, AutoPentestX orchestrates reconnaissance, scanning, exploitation, and reporting through a centralized interface. Instead of forcing security teams to manually chain together multiple tools, the framework automates the end-to-end workflow, allowing comprehensive vulnerability assessments to run with minimal ongoing operator involvement.

A key strength of AutoPentestX is how it addresses inefficiencies in traditional penetration testing processes. By automating reconnaissance and vulnerability discovery across target systems, it reduces operational overhead while preserving the depth and coverage expected in enterprise-grade security assessments.

The framework follows a modular architecture that integrates well-known security tools into coordinated testing workflows. It performs network enumeration, service discovery, and vulnerability identification, then generates structured reports detailing findings, attempted exploitations, and overall security posture.

AutoPentestX supports both command-line execution and Python-based automation, giving security professionals flexibility to integrate it into different environments and CI/CD or testing pipelines. All activities are automatically logged with timestamps and stored in organized directories, creating a clear audit trail that supports compliance, internal reviews, and post-engagement analysis.

Built using Python 3.x and Bash, the framework runs natively on Linux distributions such as Kali Linux, Ubuntu, and Debian-based systems. Installation is handled via an install script that manages dependencies and prepares the required directory structure.

Configuration is driven through a central JSON file, allowing users to fine-tune scan intensity, targets, and reporting behavior. Its structured layout—separating exploits, modules, and reports—also makes it easy to extend the framework with custom modules or integrate additional external tools.


My Perspective

AutoPentestX reflects a broader shift toward AI-adjacent and automation-first security operations, where efficiency and repeatability are becoming just as important as technical depth. For modern security teams—especially those operating under compliance pressure—automation like this can significantly improve coverage and consistency.

However, tools like AutoPentestX should be viewed as force multipliers, not replacements for skilled testers. Automated frameworks excel at scale, baseline assessments, and documentation, but human expertise is still critical for contextual risk analysis, business impact evaluation, and creative attack paths. Used correctly, AutoPentestX fits well into a continuous security testing and risk-driven assessment model, especially for organizations embracing DevSecOps and ongoing assurance rather than point-in-time pentests.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AutoPentestX


Feb 02 2026

AI Has Joined the Attacker Team: An Executive Wake-Up Call for Cyber Risk Leaders

AI Has Joined the Attacker Team

The threat landscape is entering a new phase with the rise of AI-assisted malware. What once required well-funded teams and months of development can now be created by a single individual in days using AI. This dramatically lowers the barrier to entry for advanced cyberattacks.

This shift means attackers can scale faster, adapt quicker, and deliver higher-quality attacks with fewer resources. As a result, smaller and mid-sized organizations are no longer “too small to matter” and are increasingly attractive targets.

Emerging malware frameworks are more modular, stealthy, and cloud-aware, designed to persist, evade detection, and blend into modern IT environments. Traditional signature-based defenses and slow response models are struggling to keep pace with this speed and sophistication.

Critically, this is no longer just a technical problem — it is a business risk. AI-enabled attacks increase the likelihood of operational disruption, regulatory exposure, financial loss, and reputational damage, often faster than organizations can react.

Organizations that will remain resilient are not those chasing the latest tools, but those making strategic security decisions. This includes treating cybersecurity as a core element of business resilience, not an IT afterthought.

Key priorities include moving toward Zero Trust and behavior-based detection, maintaining strong asset visibility and patch hygiene, investing in practical security awareness, and establishing clear governance around internal AI usage.


The cybersecurity landscape is undergoing a fundamental shift with the emergence of a new class of malware that is largely created using artificial intelligence (AI) rather than traditional development teams. Recent reporting shows that advanced malware frameworks once requiring months of collaborative effort can now be developed in days with AI’s help.

The most prominent example prompting this concern is the discovery of the VoidLink malware framework — an AI-driven, cloud-native Linux malware platform uncovered by security researchers. Rather than being a simple script or proof-of-concept, VoidLink appears to be a full, modular framework with sophisticated stealth and persistence capabilities.

What makes this remarkable isn’t just the malware itself, but how it was developed: evidence points to a single individual using AI tools to generate and assemble most of the code, something that previously would have required a well-coordinated team of experts.

This capability accelerates threat development dramatically. Where malware used to take months to design, code, test, iterate, and refine, AI assistance can collapse that timeline to days or weeks, enabling adversaries with limited personnel and resources to produce highly capable threats.

The practical implications are significant. Advanced malware frameworks like VoidLink are being engineered to operate stealthily within cloud and container environments, adapt to target systems, evade detection, and maintain long-term footholds. They’re not throwaway tools — they’re designed for persistent, strategic compromise.

This isn’t an abstract future problem. Already, there are real examples of AI-assisted malware research showing how AI can be used to create more evasive and adaptable malicious code — from polymorphic ransomware that sidesteps detection to automated worms that spread faster than defenders can respond.

The rise of AI-generated malware fundamentally challenges traditional defenses. Signature-based detection, static analysis, and manual response processes struggle when threats are both novel and rapidly evolving. The attack surface expands when bad actors leverage the same AI innovation that defenders use.

For security leaders, this means rethinking strategies: investing in behavior-based detection, threat hunting, cloud-native security controls, and real-time monitoring rather than relying solely on legacy defenses. Organizations must assume that future threats may be authored as much by machines as by humans.

In my view, this transition marks one of the first true inflection points in cyber risk: AI has joined the attacker team not just as a helper, but as a core part of the offensive playbook. This amplifies both the pace and quality of attacks and underscores the urgency of evolving our defensive posture from reactive to anticipatory. We’re not just defending against more attacks — we’re defending against self-evolving, machine-assisted adversaries.

Perspective:
AI has permanently altered the economics of cybercrime. The question for leadership is no longer “Are we secure today?” but “Are we adapting fast enough for what’s already here?” Organizations that fail to evolve their security strategy at the speed of AI will find themselves defending yesterday’s risks against tomorrow’s attackers.


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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Attacker Team, Attacker Team, Cyber Risk Leaders


Jan 29 2026

🔐 What the OWASP Top 10 Is and Why It Matters

Category: Information Security,owaspdisc7 @ 1:18 pm


🔐 What the OWASP Top 10 Is and Why It Matters

The OWASP Top 10 remains one of the most widely respected, community-driven lists of critical application security risks. Its purpose is to spotlight where most serious vulnerabilities occur so development teams can prioritize mitigation. The 2025 edition reinforces that many vulnerabilities aren’t just coding mistakes — they stem from design flaws, architectural decisions, dependency weaknesses, and misconfigurations.

🎯 Insecure Design and Misconfiguration Stay Central

Insecure design and weak configurations continue to top the risk landscape, especially as apps become more complex and distributed. Even with AI tools helping write code or templates, if foundational security thinking is missing early, these tools can unintentionally embed insecure patterns at scale.

📦 Third-Party Dependencies Expand Attack Surface

Modern software isn’t just code you write — it’s an ecosystem of open-source libraries, services, infrastructure components, and AI models. The Top 10 now reflects how vulnerable elements in this wider ecosystem frequently introduce weaknesses long before deployment. Without visibility into every component your software relies on, you’re effectively blind to many major risks.

🤖 AI Accelerates Both Innovation and Risk

AI tools — including code generators and helpers — accelerate development but don’t automatically improve security. They can reproduce insecure patterns, suggest outdated APIs, or introduce unvetted components. As a result, traditional OWASP concerns like authentication failures and injection risks can be amplified in AI-augmented workflows.

🧠 Supply Chains Now Include AI Artifacts

The definition of a “component” in application security now includes datasets, pretrained models, plugins, and other AI artifacts. These parts often lack mature governance, standardized versioning, and reliable vulnerability disclosures. This broadening of scope means that software supply chains — especially when AI is involved — demand deeper inspection and continuous monitoring.

🔎 Trust Boundaries and Data Exposure Expand

AI-enabled systems often interact dynamically with internal and external data sources. If trust boundaries aren’t clearly defined or enforced — e.g., through access controls, validation rules, or output filtering — sensitive data can leak or be manipulated. Many traditional vulnerabilities resurface in this context, just with AI-flavored twists.

🛠 Automation Must Be Paired With Guardrails

Automation — whether CI/CD pipelines or AI-assisted code completion — speeds delivery. But without policy-driven controls that enforce security tests and approvals at the same velocity, vulnerabilities can propagate fast and wide. Proactive, automated governance is essential to prevent insecure components from reaching production.

📊 Sonatype’s Focus: Visibility and Policy

Sonatype’s argument in the article is that the foundational practices used to secure traditional application security risks (inventorying dependencies, enforcing policy, continuous visibility) also apply to AI-driven risks. Better visibility into components — including models and datasets — plus enforceable policies helps organizations balance speed and security. (Sonatype)


🧠 My Perspective

The Sonatype article doesn’t reinvent OWASP’s Top 10, but instead bridges the gap between traditional application security and emerging AI-enabled risk vectors. What’s clear from the latest OWASP work and related research is that:

  • AI doesn’t create wholly new vulnerabilities; it magnifies existing ones (insecure design, misconfiguration, supply chain gaps) while adding its own nuances like model artefacts, prompt risks, and dynamic data flows.
  • Effective security in the AI era still boils down to proactive controls — visibility, validation, governance, and human oversight — but applied across a broader ecosystem that now includes models, datasets, and AI-augmented pipelines.
  • Organizations tend to treat AI as a productivity tool, not a risk domain; aligning AI risk management with established frameworks like OWASP helps anchor security in well-tested principles even as threats evolve.

In short: OWASP’s Top 10 remains highly relevant, but teams must think beyond code alone — to components, AI behaviors, and trust boundaries — to secure modern applications effectively.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: OWASP Top 10


Jan 26 2026

Cybersecurity Frameworks Explained: Choosing the Right Standard for Risk, Compliance, and Business Value


NIST Cybersecurity Framework (CSF)

The NIST Cybersecurity Framework provides a flexible, risk-based approach to managing cybersecurity using five core functions: Identify, Protect, Detect, Respond, and Recover. It is widely adopted by both government and private organizations to understand current security posture, prioritize risks, and improve resilience over time. NIST CSF is particularly strong as a communication tool between technical teams and business leadership because it focuses on outcomes rather than prescriptive controls.


ISO/IEC 27001

ISO/IEC 27001 is an international standard for establishing, implementing, and maintaining an Information Security Management System (ISMS). It emphasizes governance, risk assessment, policies, audits, and continuous improvement. Unlike NIST, ISO 27001 is certifiable, making it valuable for organizations that need formal assurance, regulatory credibility, or customer trust across global markets.


CIS Critical Security Controls

The CIS Controls are a prioritized set of practical, technical security best practices designed to reduce the most common cyber risks. They focus on actionable safeguards such as system hardening, access control, monitoring, and incident detection. CIS is highly effective for organizations that want fast, measurable security improvements without the overhead of full governance frameworks.


PCI DSS

PCI DSS is a mandatory compliance standard for organizations that store, process, or transmit payment card data. It focuses on securing cardholder data through access control, monitoring, encryption, and vulnerability management. PCI DSS is narrowly scoped but very detailed, making it essential for payment security but insufficient as a standalone enterprise security framework.


COBIT

COBIT is an IT governance and management framework that aligns IT processes with business objectives, risk management, and compliance requirements. It is less about technical security controls and more about decision-making, accountability, performance measurement, and process maturity. COBIT is commonly used by large enterprises, auditors, and boards to ensure IT delivers business value while managing risk.


GDPR

GDPR is a data protection regulation focused on privacy rights, lawful data processing, and accountability for personal data handling within the EU (and beyond). It requires organizations to implement strong data protection controls, transparency mechanisms, and breach response processes. GDPR is regulatory in nature, with significant penalties for non-compliance, and places individuals’ rights at the center of security and governance efforts.


Opinion: When and How to Apply These Frameworks

In practice, no single framework is sufficient on its own. The most effective security programs intentionally combine frameworks based on business context, risk exposure, and regulatory pressure.

  • Use NIST CSF when you need a strategic, flexible starting point to assess risk, communicate with leadership, or build a roadmap without jumping straight into certification.
  • Adopt ISO/IEC 27001 when you need formal governance, customer assurance, or regulatory credibility, especially for SaaS, global operations, or enterprise clients.
  • Implement CIS Controls when your priority is rapid risk reduction, technical hardening, and improving day-to-day security operations.
  • Apply PCI DSS only when payment data is involved—treat it as a mandatory baseline, not a full security program.
  • Use COBIT when security must be tightly integrated with enterprise governance, audit expectations, and board oversight.
  • Comply with GDPR whenever personal data of EU residents is processed, and use it to strengthen privacy-by-design practices globally.

How Do You Know Which Framework Is Relevant?

You know a framework is relevant when it clearly answers one or more of these questions for your organization:

  • What regulatory or contractual obligations do we have?
  • What risks matter most to our business model?
  • Who needs assurance—customers, regulators, auditors, or the board?
  • Do we need outcomes, controls, certification, or governance?

The right framework is the one that reduces real risk, supports business goals, and can actually be operationalized by your organization—not the one that simply looks good on paper. Mature security programs evolve by layering frameworks, not replacing them.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Cybersecurity Frameworks


Jan 24 2026

Smart Contract Security: Why Audits Matter Before Deployment

Category: Information Security,Internal Audit,Smart Contractdisc7 @ 12:57 pm

Smart Contracts: Overview and Example

What is a Smart Contract?

A smart contract is a self-executing program deployed on a blockchain that automatically enforces the terms of an agreement when predefined conditions are met. Once deployed, the code is immutable and executes deterministically – the same inputs always produce the same outputs, and execution is verified by the blockchain network.

Potential Use Case

Escrow for Freelance Payments: A client deposits funds into a smart contract when hiring a freelancer. When the freelancer submits deliverables and the client approves (or after a timeout period), the contract automatically releases payment. No intermediary needed, and both parties can trust the transparent code logic.

Example Smart Contract

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;

contract SimpleEscrow {
    address public client;
    address public freelancer;
    uint256 public amount;
    bool public workCompleted;
    bool public fundsReleased;

    constructor(address _freelancer) payable {
        client = msg.sender;
        freelancer = _freelancer;
        amount = msg.value;
        workCompleted = false;
        fundsReleased = false;
    }

    function releasePayment() external {
        require(msg.sender == client, "Only client can release payment");
        require(!fundsReleased, "Funds already released");
        require(amount > 0, "No funds to release");
        
        fundsReleased = true;
        payable(freelancer).transfer(amount);
    }
}

Fuzz Testing with Foundry

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;

import "forge-std/Test.sol";
import "../src/SimpleEscrow.sol";

contract SimpleEscrowFuzzTest is Test {
    SimpleEscrow public escrow;
    address client = address(0x1);
    address freelancer = address(0x2);

    function setUp() public {
        vm.deal(client, 100 ether);
    }

    function testFuzz_ReleasePayment(uint256 depositAmount) public {
        // Bound the fuzz input to reasonable values
        depositAmount = bound(depositAmount, 0.01 ether, 10 ether);
        
        // Deploy contract with fuzzed amount
        vm.prank(client);
        escrow = new SimpleEscrow{value: depositAmount}(freelancer);
        
        uint256 freelancerBalanceBefore = freelancer.balance;
        
        // Client releases payment
        vm.prank(client);
        escrow.releasePayment();
        
        // Assertions
        assertEq(escrow.fundsReleased(), true);
        assertEq(freelancer.balance, freelancerBalanceBefore + depositAmount);
        assertEq(address(escrow).balance, 0);
    }

    function testFuzz_OnlyClientCanRelease(address randomCaller) public {
        vm.assume(randomCaller != client);
        
        vm.prank(client);
        escrow = new SimpleEscrow{value: 1 ether}(freelancer);
        
        // Random address tries to release
        vm.prank(randomCaller);
        vm.expectRevert("Only client can release payment");
        escrow.releasePayment();
    }

    function testFuzz_CannotReleaseMultipleTimes(uint8 attempts) public {
        attempts = uint8(bound(attempts, 2, 10));
        
        vm.prank(client);
        escrow = new SimpleEscrow{value: 1 ether}(freelancer);
        
        // First release succeeds
        vm.prank(client);
        escrow.releasePayment();
        
        // Subsequent attempts fail
        for (uint8 i = 1; i < attempts; i++) {
            vm.prank(client);
            vm.expectRevert("Funds already released");
            escrow.releasePayment();
        }
    }
}

Run the fuzz tests:

forge test --match-contract SimpleEscrowFuzzTest -vvv

Configure fuzz runs in foundry.toml:

[fuzz]
runs = 10000
max_test_rejects = 100000

Benefits of Smart Contract Audits

Security Assurance: Auditors identify vulnerabilities like reentrancy attacks, integer overflows, access control flaws, and logic errors before deployment. Since contracts are immutable, catching bugs pre-deployment is critical.

Economic Protection: Bugs in smart contracts have led to hundreds of millions in losses. An audit protects both project funds and user assets from exploitation.

Compliance & Trust: For regulated industries or institutional adoption, third-party audits provide documented due diligence that security best practices were followed.

Gas Optimization: Auditors often identify inefficient code patterns that unnecessarily increase transaction costs for users.

Best Practice Validation: Audits verify adherence to standards like OpenZeppelin patterns, proper event emission, secure randomness generation, and appropriate use of libraries.

Reputation & Adoption: Projects with reputable audit reports (Trail of Bits, OpenZeppelin, Consensys Diligence) gain user confidence and are more likely to attract partnerships and investment.

Given our work at DISC InfoSec implementing governance frameworks, smart contract audits parallel traditional security assessments – they’re about risk identification, control validation, and providing assurance that systems behave as intended under both normal and adversarial conditions.

DISC InfoSec: Smart Contract Audits with Governance Expertise

DISC InfoSec brings a unique advantage to smart contract security: we don’t just audit code, we understand the governance frameworks that give blockchain projects credibility and staying power. As pioneer-practitioners implementing ISO 42001 AI governance and ISO 27001 information security at ShareVault while consulting across regulated industries, we recognize that smart contract audits aren’t just technical exercises—they’re risk management foundations for projects handling real assets and user trust. Our team combines deep Solidity expertise with enterprise compliance experience, delivering comprehensive security assessments that identify vulnerabilities like reentrancy, access control flaws, and logic errors while documenting findings in formats that satisfy both technical teams and regulatory stakeholders. Whether you’re launching a DeFi protocol, NFT marketplace, or tokenized asset platform, DISC InfoSec provides the security assurance and governance documentation needed to protect your users, meet institutional due diligence requirements, and build lasting credibility in the blockchain ecosystem. Contact us at deurainfosec.com to secure your smart contracts before deployment.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Smart Contract Audit


Jan 21 2026

How AI Evolves: A Layered Path from Automation to Autonomy

Category: AI,AI Governance,AI Guardrails,Information Securitydisc7 @ 11:47 am


Understanding the Layers of AI

The “Layers of AI” model helps explain how artificial intelligence evolves from simple rule-based logic into autonomous, goal-driven systems. Each layer builds on the capabilities of the one beneath it, adding complexity, adaptability, and decision-making power. Understanding these layers is essential for grasping not just how AI works technically, but also where risks, governance needs, and human oversight must be applied as systems move closer to autonomy.


Classical AI: The Rule-Based Foundation

Classical AI represents the earliest form of artificial intelligence, relying on explicit rules, logic, and symbolic representations of knowledge. Systems such as expert systems and logic-based reasoning engines operate deterministically, meaning they behave exactly as programmed. While limited in flexibility, Classical AI laid the groundwork for structured reasoning, decision trees, and formal problem-solving that still influence modern systems.


Machine Learning: Learning from Data

Machine Learning marked a shift from hard-coded rules to systems that learn patterns from data. Techniques such as supervised, unsupervised, and reinforcement learning allow models to improve performance over time without explicit reprogramming. Tasks like classification, regression, and prediction became scalable, enabling AI to adapt to real-world variability rather than relying solely on predefined logic.


Neural Networks: Mimicking the Brain

Neural Networks introduced architectures inspired by the human brain, using interconnected layers of artificial neurons. Concepts such as perceptrons, activation functions, cost functions, and backpropagation allow these systems to learn complex representations. This layer enables non-linear problem solving and forms the structural backbone for more advanced AI capabilities.


Deep Learning: Scaling Intelligence

Deep Learning extends neural networks by stacking many hidden layers, allowing models to extract increasingly abstract features from raw data. Architectures such as CNNs, RNNs, LSTMs, transformers, and autoencoders power breakthroughs in vision, speech, language, and pattern recognition. This layer made AI practical at scale, especially with large datasets and high-performance computing.


Generative AI: Creating New Content

Generative AI focuses on producing new data rather than simply analyzing existing information. Large Language Models (LLMs), diffusion models, VAEs, and multimodal systems can generate text, images, audio, video, and code. This layer introduces creativity, probabilistic reasoning, and uncertainty, but also raises concerns around hallucinations, bias, intellectual property, and trustworthiness.


Agentic AI: Acting with Purpose

Agentic AI adds decision-making and goal-oriented behavior on top of generative models. These systems can plan tasks, retain memory, use tools, and take actions autonomously across environments. Rather than responding to a single prompt, agentic systems operate continuously, making them powerful—but also significantly more complex to govern, audit, and control.


Autonomous Execution: AI Without Constant Human Input

At the highest layer, AI systems can execute tasks independently with minimal human intervention. Autonomous execution combines planning, tool use, feedback loops, and adaptive behavior to operate in real-world conditions. This layer blurs the line between software and decision-maker, raising critical questions about accountability, safety, alignment, and ethical boundaries.


My Opinion: From Foundations to Autonomy

The layered model of AI is useful because it makes one thing clear: autonomy is not a single leap—it is an accumulation of capabilities. Each layer introduces new power and new risk. While organizations are eager to adopt agentic and autonomous AI, many still lack maturity in governing the foundational layers beneath them. In my view, responsible AI adoption must follow the same layered discipline—strong foundations, clear controls at each level, and escalating governance as systems gain autonomy. Skipping layers in governance while accelerating layers in capability is where most AI risk emerges.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Layers, Automation, Layered AI


Jan 21 2026

The Hidden Cyber Risks of AI Adoption No One Is Managing

Category: AI,AI Governance,Information Security,ISO 42001disc7 @ 9:47 am

“Why AI adoption requires a dedicated approach to cyber governance”


1. Rapid AI Adoption and Rising Risks
AI tools are being adopted at an extraordinary pace across businesses, offering clear benefits like efficiency, reduced errors, and increased revenue. However, this rapid uptake also dramatically expands the enterprise attack surface. Each AI model, prompt, plugin, API connection, training dataset, or dependency introduces new vulnerability points, requiring stronger and continuous security measures than traditional SaaS governance frameworks were designed to handle.

2. Traditional Governance Falls Short for AI
Many security teams simply repurpose existing governance approaches designed for SaaS vendors when evaluating AI tools. This is problematic because data fed into AI systems can be exposed far more widely and may even be retained permanently by the AI provider—something that most conventional governance models don’t account for.

3. Explainability and Trust Issues
AI outputs can be opaque due to black-box models and phenomena like “hallucinations,” where the system generates confident but incorrect information. These characteristics make verification difficult and can introduce false data into important business decisions—another challenge existing governance frameworks weren’t built to manage.

4. Pressure to Move Fast
Business units are pushing for rapid AI adoption to stay competitive, which puts security teams in a bind. Existing third-party risk processes are slow, manual, and rigid, creating bottlenecks that force organizations to choose between agility and safety. Modern governance must be agile and scalable to match the pace of AI integration.

5. Gaps in Current Cyber Governance
Governance and Risk Compliance (GRC) programs commonly monitor direct vendors but often fail to extend visibility far enough into fourth or Nth-party risks. Even when organizations are compliant with regulations like DORA or NIS2, they may still face significant vulnerabilities because compliance checks only provide snapshots in time, missing dynamic risks across complex supply chains.

6. Limited Tool Effectiveness and Emerging Solutions
Most organizations acknowledge that current GRC tools are inadequate for managing AI risks. In response, many CISOs are turning to AI-based vendor risk assessment solutions that can monitor dependencies and interactions continuously rather than relying solely on point-in-time assessments. However, these tools must themselves be trustworthy and validated to avoid generating misleading results.

7. Practical Risk-Reduction Strategies
Effective governance requires proactive strategies like mapping data flows to uncover blind spots, enforcing output traceability, keeping humans in the oversight loop, and replacing one-off questionnaires with continuous monitoring. These measures help identify and mitigate risks earlier and more reliably.

8. Safe AI Management Is Possible
Deploying AI securely is achievable, but only with robust, AI-adapted governance—dynamic vendor onboarding, automated monitoring, continuous risk evaluation, and policies tailored to the unique nature of AI tools. Security teams must evolve their practices and frameworks to ensure AI is both adopted responsibly and aligned with business goals.


My Opinion

The article makes a compelling case that treating AI like traditional software or SaaS tools is a governance mistake. AI’s dynamic nature—its opaque decision processes, broad data exposure, and rapid proliferation via APIs and plugins—demands purpose-built governance mechanisms that are continuous, adaptive, and integrated with how organizations actually operate, not just how they report. This aligns with broader industry observations that shadow AI and decentralized AI use (e.g., “bring your own AI”) create blind spots that static governance models can’t handle.

In short, cybersecurity leaders should move beyond check-the-box compliance and toward risk-based, real-time oversight that embraces human-AI collaboration, leverages AI for risk monitoring, and embeds governance throughout the AI lifecycle. Done well, this strengthens security and unlocks AI’s value; done poorly, it exposes organizations to unnecessary harm.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Cyber Governance Model


Jan 19 2026

Cyber Resilience by Design: Why the EU CRA Is a Leadership Test, Not Just a Regulation

The EU Cyber Resilience Act (CRA) marks a significant shift in how cybersecurity is viewed across digital products and services. Rather than treating security as a post-development compliance task, the Act emphasizes embedding cybersecurity into products from the design stage and maintaining it throughout their entire lifecycle. This approach reframes cyber resilience as an ongoing responsibility that blends technical safeguards with organizational discipline.

At its core, the CRA reinforces the idea that resilience is not achieved through tools alone. Secure-by-design principles require coordinated processes, clear ownership, and accountability across product development, operations, and incident response. By aligning with lifecycle thinking—similar to disaster recovery planning—the Act pushes organizations to anticipate failure, prepare for disruption, and recover quickly when incidents occur.

Leadership plays a decisive role in making this shift effective. True cyber resilience demands a top-down commitment where executives actively prioritize security in strategic planning and resource allocation. When leaders set expectations that security is integral to innovation, teams are empowered to build resilient systems without viewing cybersecurity as a barrier to progress.

When organizations treat cybersecurity as a business enabler rather than a cost center, the benefits extend beyond compliance. They gain stronger risk management, greater operational continuity, and increased trust from customers and partners. In this way, the EU CRA aligns closely with disaster recovery principles—prepare early, plan holistically, and lead decisively—to create sustainable cyber resilience in an increasingly complex digital landscape.

My opinion:

The EU Cyber Resilience Act is one of the most pragmatic cybersecurity regulations to date because it shifts the conversation from after-the-fact compliance to engineering discipline and leadership accountability. That change is long overdue. Cybersecurity failures rarely happen because controls were unknown—they happen because security was deprioritized during design, delivery, or scaling.

What I particularly agree with is the implicit alignment between cyber resilience and disaster recovery thinking. Both accept that failure is inevitable and focus instead on preparedness, impact reduction, and rapid recovery. This mindset is far more realistic than the traditional “prevent everything” security narrative, especially in complex software supply chains.

However, regulation alone will not create resilience. Organizations that approach the CRA as a documentation exercise will miss its real value. The winners will be those whose leadership genuinely internalizes security as a strategic capability—one that protects innovation, brand trust, and long-term revenue. In that sense, the CRA is less a technical mandate and more a leadership test.

Cyber Resilience Act

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: EU CRA


Jan 16 2026

AI Cybersecurity and Standardisation: Bridging the Gap Between ISO Standards and the EU AI Act

Summary of Sections 2.0 to 5.2 from the ENISA report Cybersecurity of AI and Standardisation, followed by my opinion.


2. Scope: Defining AI and Cybersecurity of AI

The report highlights that defining AI remains challenging due to evolving technology and inconsistent usage of the term. To stay practical, ENISA focuses mainly on machine learning (ML), as it dominates current AI deployments and introduces unique security vulnerabilities. AI is considered across its entire lifecycle, from data collection and model training to deployment and operation, recognizing that risks can emerge at any stage.

Cybersecurity of AI is framed in two ways. The narrow view focuses on protecting confidentiality, integrity, and availability (CIA) of AI systems, data, and processes. The broader view expands this to include trustworthiness attributes such as robustness, explainability, transparency, and data quality. ENISA adopts the narrow definition but acknowledges that trustworthiness and cybersecurity are tightly interconnected and cannot be treated independently.


3. Standardisation Supporting AI Cybersecurity

Standardisation bodies are actively adapting existing frameworks and developing new ones to address AI-related risks. The report emphasizes ISO/IEC, CEN-CENELEC, and ETSI as the most relevant organisations due to their role in harmonised standards. A key assumption is that AI is fundamentally software, meaning traditional information security and quality standards can often be extended to AI with proper guidance.

CEN-CENELEC separates responsibilities between cybersecurity-focused committees and AI-focused ones, while ETSI takes a more technical, threat-driven approach through its Security of AI (SAI) group. ISO/IEC SC 42 plays a central role globally by developing AI-specific standards for terminology, lifecycle management, risk management, and governance. Despite this activity, the landscape remains fragmented and difficult to navigate.


4. Analysis of Coverage – Narrow Cybersecurity Sense

When viewed through the CIA lens, AI systems face distinct threats such as model theft, data poisoning, adversarial inputs, and denial-of-service via computational abuse. The report argues that existing standards like ISO/IEC 27001, ISO/IEC 27002, ISO 42001, and ISO 9001 can mitigate many of these risks if adapted correctly to AI contexts.

However, limitations exist. Most standards operate at an organisational level, while AI risks are often system-specific. Challenges such as opaque ML models, evolving attack techniques, continuous learning, and immature defensive research reduce the effectiveness of static standards. Major gaps remain around data and model traceability, metrics for robustness, and runtime monitoring, all of which are critical for AI security.


4.2 Coverage – Trustworthiness Perspective

The report explains that cybersecurity both enables and depends on AI trustworthiness. Requirements from the draft AI Act—such as data governance, logging, transparency, human oversight, risk management, and robustness—are all supported by cybersecurity controls. Standards like ISO 9001 and ISO/IEC 31000 indirectly strengthen trustworthiness by enforcing disciplined governance and quality practices.

Yet, ENISA warns of a growing risk: parallel standardisation tracks for cybersecurity and AI trustworthiness may lead to duplication, inconsistency, and confusion—especially in areas like conformity assessment and robustness evaluation. A coordinated, unified approach is strongly recommended to ensure coherence and regulatory usability.


5. Conclusions and Recommendations (5.1–5.2)

The report concludes that while many relevant standards already exist, AI-specific guidance, integration, and maturity are still lacking. Organisations should not wait for perfect AI standards but instead adapt current cybersecurity, quality, and risk frameworks to AI use cases. Standards bodies are encouraged to close gaps around lifecycle traceability, continuous learning, and AI-specific metrics.

In preparation for the AI Act, ENISA recommends better alignment between AI governance and cybersecurity governance frameworks to avoid overlapping compliance efforts. The report stresses that some gaps will only become visible as AI technologies and attack methods continue to evolve.


My Opinion

This report gets one critical thing right: AI security is not a brand-new problem—it is a complex extension of existing cybersecurity and governance challenges. Treating AI as “just another system” under ISO 27001 without AI-specific interpretation is dangerous, but reinventing security from scratch for AI is equally inefficient.

From a practical vCISO and governance perspective, the real gap is not standards—it is operationalisation. Organisations struggle to translate abstract AI trustworthiness principles into enforceable controls, metrics, and assurance evidence. Until standards converge into a clear, unified control model (especially aligned with ISO 27001, ISO 42001, and the NIST AI RMF), AI security will remain fragmented and audit-driven rather than risk-driven.

In short: AI cybersecurity maturity will lag unless governance, security, and trustworthiness are treated as one integrated discipline—not three separate conversations.

Source: ENISA – Cybersecurity of AI and Standardisation

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: AI Cybersecurity, EU AI Act, ISO standards


Jan 13 2026

Beyond Technical Excellence: How CISOs Will Lead in the Age of AI

Category: CISO,Information Security,vCISOdisc7 @ 1:56 pm

AI’s impact on the CISO role:


The CISO role is evolving rapidly between now and 2035. Traditional security responsibilities—like managing firewalls and monitoring networks—are only part of the picture. CISOs must increasingly operate as strategic business leaders, integrating security into enterprise-wide decision-making and aligning risk management with business objectives.

Boards and CEOs will have higher expectations for security leaders in the next decade. They will look for CISOs who can clearly communicate risks in business terms, drive organizational resilience, and contribute to strategic initiatives rather than just react to incidents. Leadership influence will matter as much as technical expertise.

Technical excellence alone is no longer enough. While deep security knowledge remains critical, modern CISOs must combine it with business acumen, emotional intelligence, and the ability to navigate complex organizational dynamics. The most successful security leaders bridge the gap between technology and business impact.

World-class CISOs are building leadership capabilities today that go beyond technology management. This includes shaping corporate culture around security, influencing cross-functional decisions, mentoring teams, and advocating for proactive risk governance. These skills ensure they remain central to enterprise success.

Common traps quietly derail otherwise strong CISOs. Focusing too narrowly on technical issues, failing to communicate effectively with executives, or neglecting stakeholder relationships can limit influence and career growth. Awareness of these pitfalls allows security leaders to avoid them and maintain credibility.

Future-proofing your role and influence is now essential. AI is transforming the security landscape. For CISOs, AI means automated threat detection, predictive risk analytics, and new ethical and regulatory considerations. Responsibilities like routine monitoring may fade, while oversight of AI-driven systems, data governance, and strategic security leadership will intensify. The question is no longer whether CISOs understand AI—it’s whether they are prepared to lead in an AI-driven organization, ensuring security remains a core enabler of business objectives.

Data Security in the Age of AI: A Guide to Protecting Data and Reducing Risk in an AI-Driven World


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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Age of AI, CISO


Jan 13 2026

When Identity Meets the Browser: How CrowdStrike Is Closing a Critical Enterprise Security Blind Spot


Summary: to Address a Security Blind Spot

CrowdStrike recently announced an agreement to acquire Seraphic Security, a browser-centric security company, in a deal valued at roughly $420 million. This move, coming shortly after CrowdStrike’s acquisition of identity authorization firm SGNL, highlights a strategic effort to eliminate one of the most persistent gaps in enterprise cybersecurity: visibility and control inside the browser — where modern work actually happens.


Why Identity and Browser Security Converge

Modern attackers don’t respect traditional boundaries between systems — they exploit weaknesses wherever they find them, often inside authenticated sessions in browsers. Identity security tells you who should have access, while browser security shows what they’re actually doing once authenticated.

CrowdStrike’s CEO, George Kurtz, emphasized that attackers increasingly bypass malware installation entirely by hijacking sessions or exploiting credentials. Once an attacker has valid access, static authentication — like a single login check — quickly becomes ineffective. This means security teams need continuous evaluation of both identity behavior and browser activity to detect anomalies in real time.

In essence, identity and browser security can’t be siloed anymore: to stop modern attacks, security systems must treat access and usage as joined data streams, continuously monitoring both who is logged in and what the session is doing.


AI Raises the Stakes — and the Signal Value

The rise of AI doesn’t create new vulnerabilities per se, but it amplifies existing blind spots and creates new patterns of activity that traditional tools can easily miss. AI tools — from generative assistants to autonomous agents — are heavily used through browsers or browser-like applications. Without visibility at that layer, AI interactions can bypass controls, leak sensitive data, or facilitate automated attacks without triggering legacy endpoint defenses.

Instead of trying to ban AI tools — a losing battle — CrowdStrike aims to observe and control AI usage within the browser itself. In this context, AI usage becomes a high-value signal that acts as a proxy for risky behavior: what data is being queried, where it’s being sent, and whether it aligns with policy. This greatly enhances threat detection and risk scoring when combined with identity and endpoint telemetry.


The Bigger Pattern

Taken together, the Seraphic and SGNL acquisitions reflect a broader architectural shift at CrowdStrike: expanding telemetry and intelligence not just on endpoints but across identity systems and browser sessions. By aggregating these signals, the Falcon platform can trace entire attack chains — from initial access through credential use, in-session behavior, and data exfiltration — rather than reacting piecemeal to isolated alerts.

This pattern mirrors the reality that attack surfaces are fluid and exist wherever users interact with systems, whether on a laptop endpoint or inside an authenticated browser session. The goal is not just prevention, but continuous understanding and control of risk across a human or machine’s entire digital journey.


Addressing an Enterprise Security Blind Spot

The browser is arguably the new front door of enterprise IT: it’s where SaaS apps live, where data flows, and — increasingly — where AI tools operate. Because traditional security technologies were built around endpoints and network edges, developers often overlooked the runtime behavior of browsers — until now. CrowdStrike’s acquisition of Seraphic directly addresses this blind spot by embedding security inside the browser environment itself.

This approach extends beyond snippet-based URL filtering or restricting corporate browsers: it provides runtime visibility and policy enforcement in any browser across managed and unmanaged devices. By correlating this with identity and endpoint data, security teams gain unprecedented context for detecting session-based threats like hijacks, credential abuse, or misuse of AI tools.

Source: to Address a Security Blind Spot


My Opinion

This strategic push makes a lot of sense. For too long, security architectures treated the browser as a perimeter, rather than as a core execution environment where work and risk converge. As enterprises embrace SaaS, remote work, and AI-driven workflows, attackers have naturally gravitated to these unmonitored entry points. CrowdStrike’s focus on continuous identity evaluation plus in-session browser telemetry is a pragmatic evolution of zero-trust principles — not just guarding entry points, but consistently watching how access is used. Combining identity, endpoint, and browser signals moves defenders closer to true context-aware security, where decisions adapt in real time based on actual behavior, not just static policies.

However, executing this effectively at scale — across diverse browser types, BYOD environments, and AI applications — will be complex. The industry will be watching closely to see whether this translates into tangible reductions in breaches or just a marketing narrative about data correlation. But as attackers continue to blur boundaries between identity abuse and session exploitation, this direction seems not only logical but necessary.


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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Blind Spot, browser security, Critical Enterprise Security


Jan 13 2026

Ransomware Explained: How Attacks Happen and How SMBs Can Defend Themselves

Category: Cyber Attack,Information Security,Ransomwaredisc7 @ 10:15 am

What Is a Ransomware Attack?

A ransomware attack is a type of cyberattack where attackers encrypt an organization’s files or systems and demand payment—usually in cryptocurrency—to restore access. Once infected, critical data becomes unusable, operations can grind to a halt, and organizations are forced into high-pressure decisions with financial, legal, and reputational consequences.

Why People Are Falling for Ransomware Attacks

Ransomware works because it exploits human behavior as much as technical gaps. Attackers craft emails, messages, and websites that look legitimate and urgent, tricking users into clicking links or opening attachments. Weak passwords, reused credentials, unpatched systems, and lack of awareness training make it easy for attackers to gain initial access. As attacks become more polished and automated, even cautious users and small businesses fall victim.

Why It’s a Major Threat Today

Ransomware attacks are increasing rapidly, especially against organizations with limited security resources. Small mistakes—such as clicking a malicious link—can completely shut down business operations, making ransomware a serious operational and financial risk.

Who Gets Targeted the Most

Small and mid-sized businesses are frequent targets because they often lack mature security controls. Hospitals, schools, startups, and freelancers are also heavily targeted due to sensitive data and limited downtime tolerance.

How Ransomware Enters Systems

Attackers commonly use fake emails, malicious attachments, phishing links, weak or reused passwords, and outdated software to gain access. These methods are effective because they blend in with normal business activity.

Warning Signs of a Ransomware Attack

Early indicators include files that won’t open, unusual file extensions, sudden ransom notes appearing on screens, and systems becoming noticeably slow or unstable.

The Cost of One Attack

A single ransomware incident can result in direct financial losses, extended business downtime, loss of critical data, and long-term reputational damage that impacts customer trust.

Why People Fall for It

Attackers design messages that look authentic and urgent. They use fear, pressure, and trusted branding to push users into acting quickly without verifying authenticity.

Biggest Mistakes Organizations Make

Common errors include clicking links without verification, failing to maintain regular backups, ignoring software updates, reusing the same password everywhere, and downloading pirated or cracked software.

How to Prevent Ransomware

Basic prevention includes using strong and unique passwords, enabling multi-factor authentication, keeping systems updated, and training employees to recognize phishing attempts.

What to Do If You’re Attacked

If ransomware strikes, immediately disconnect affected systems from the internet, notify IT or security teams, avoid paying the ransom, restore systems from clean backups, and act quickly to limit damage.

Myths About Ransomware

Many believe attackers won’t target them, antivirus alone is sufficient, or only large companies are at risk. In reality, ransomware affects organizations of all sizes, and layered defenses are essential.

How to Protect Your Business from Cyber Attacks

Employee Cybersecurity Education

Educating employees on phishing, password hygiene, and reporting suspicious activity is one of the most cost-effective security controls. Well-trained staff significantly reduce the likelihood of successful attacks.

Use an Internet Security Suite

A comprehensive security suite—including antivirus, firewall, and intrusion detection—helps protect systems from known threats. Keeping these tools updated is critical for effectiveness.

Prepare for Zero-Day Attacks

Organizations should assume unknown threats will occur. Security solutions should focus on containment and behavior-based detection rather than relying solely on known signatures.

Stay Updated with Patches

Regularly applying software and system updates closes known vulnerabilities. Unpatched systems remain one of the easiest entry points for attackers.

Back Up Your Data

Frequent, secure backups ensure business continuity. Backups should be stored separately from primary systems to prevent them from being encrypted during an attack.

Be Cautious with Public Wi-Fi

Public and unsecured Wi-Fi networks expose systems to interception and attacks. Employees should avoid unknown networks or use secure VPNs when remote.

Use Secure Web Browsers

Modern secure browsers reduce exposure to malicious websites and exploits. Choosing hardened, updated browsers adds another layer of defense.

Secure Personal Devices Used for Work

Personal devices accessing business data must meet organizational security standards. Unsecured endpoints can undermine even strong network defenses.

Establish Access Controls

Each employee should have a unique account with access limited to what they need. Enforcing least privilege reduces the impact of compromised credentials.

Ensure Systems Are Malware-Free

Regular system scans help detect hidden malware that may evade initial defenses. Early detection prevents long-term data theft and damage.


How to Protect Small and Mid-Sized Businesses (SMBs) from Cyber Attacks

For SMBs, cybersecurity must be practical, risk-based, and repeatable. Start with strong identity controls such as multi-factor authentication and unique passwords. Maintain regular, tested backups and keep systems patched. Limit access based on roles, monitor for unusual activity, and educate employees continuously. Most importantly, SMBs should adopt a simple incident response plan and consider periodic risk assessments aligned with frameworks like ISO 27001 or NIST CSF. Cybersecurity for SMBs isn’t about expensive tools—it’s about visibility, discipline, and readiness.


How Attacks Get In

  • 📧 Phishing Emails
  • 🔑 Weak / Reused Passwords
  • 🧩 Unpatched Systems
  • 👤 Excessive User Access
  • 💾 No Reliable Backups

ISO 27001 controls

  • 🔐 MFA & Identity Control
    (A.5.17)
  • 🎓 Security Awareness
    (A.6.3)
  • 🛡️ Malware Protection
    (A.8.7)
  • 🔄 Patch Management
    (A.8.8)
  • 🧭 Least Privilege Access
    (A.5.15 / A.5.18)
  • 💽 Backups & Recovery
    (A.8.13)
  • 🚨 Incident Response
    (A.5.24–26)

What the Business Feels

  • ⏱️ Operational Downtime
  • 💰 Financial Loss
  • 📉 Reputation Damage
  • ⚖️ Compliance Exposure
  • 👔 Executive Accountability

Ransomware is not a technology failure — it’s a governance failure.

Subtext (smaller):
vCISO oversight aligns ISO 27001 controls to real business risk.

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At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: ransomware attacks, Ransomware Protection Playbook


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