Jan 29 2026

🔐 What the OWASP Top 10 Is and Why It Matters

Category: Information Security,owasp — disc7 @ 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 They can reproduce insecure patterns, suggest outdated APIs, or introduce unvetted components.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.

OWASP Top 10 Web Application Security Risks ↔ MITRE ATT&CK Mapping

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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

Category: cyber security,Cyber Strategy,Information Security,Security Compliance,Security Risk Assessment — disc7 @ 9:55 am

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.

ISO 27001 Information Security Management: A Comprehensive Framework for Modern Organizations
Zero Trust Architecture to ISO/IEC 27001:2022 Controls Crosswalk

Tags: Cybersecurity Frameworks

Jan 24 2026

Smart Contract Security: Why Audits Matter Before Deployment

Category: Information Security,Internal Audit,Smart Contract — disc7 @ 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 Security — disc7 @ 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 42001 — disc7 @ 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

Category: Cyber resilience,cyber security,Cyber Strategy,Information Security — disc7 @ 8:52 am

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

Category: CISO,cyber security,Information Security,ISO 27k,ISO 42001,vCISO — disc7 @ 9:58 am

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,vCISO — disc7 @ 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.

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Tags: Age of AI, CISO

Jan 13 2026

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

Category: Identity Theft,Information Security,Security Incident,Web Search Engine,Web Security — disc7 @ 12:42 pm

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,Ransomware — disc7 @ 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

Jan 12 2026

ISO 27001 vs ISO 27002: Why Governance Comes Before Controls

Category: Information Security,ISO 27k,vCISO — disc7 @ 8:49 am

Structured summary of the difference between ISO 27001 and ISO 27002

  1. ISO 27001 is frequently misunderstood, and this misunderstanding is a major reason many organizations struggle even after achieving certification. The standard is often treated as a technical security guide, when in reality it is not designed to explain how to secure systems.
  2. At its core, ISO 27001 defines the management system for information security. It focuses on governance, leadership responsibility, risk ownership, and accountability rather than technical implementation details.
  3. The standard answers the question of what must exist in an organization: clear policies, defined roles, risk-based decision-making, and management oversight for information security.
  4. ISO 27002, on the other hand, plays a very different role. It is not a certification standard and does not make an organization compliant on its own.
  5. Instead, ISO 27002 provides practical guidance and best practices for implementing security controls. It explains how controls can be designed, deployed, and operated effectively.
  6. However, ISO 27002 only delivers value when strong governance already exists. Without the structure defined by ISO 27001, control guidance becomes fragmented and inconsistently applied.
  7. A useful way to think about the relationship is simple: ISO 27001 defines governance and accountability, while ISO 27002 supports control implementation and operational execution.
  8. In practice, many organizations make the mistake of deploying tools and controls first, without establishing clear ownership and risk accountability. This often leads to audit findings despite significant security investments.
  9. Controls rarely fail on their own. When controls break down, the root cause is usually weak governance, unclear responsibilities, or poor risk decision-making rather than technical shortcomings.
  10. When used together, ISO 27001 and ISO 27002 go beyond helping organizations pass audits. They strengthen risk management, improve audit outcomes, and build long-term trust with regulators, customers, and stakeholders.

My opinion:
The real difference between ISO 27001 and ISO 27002 is the difference between certification and security maturity. Organizations that chase controls without governance may pass short-term checks but remain fragile. True resilience comes when leadership owns risk, governance drives decisions, and controls are implemented as a consequence—not a substitute—for accountability.

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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: iso 27001, ISO 27001 2022, iso 27001 certification, ISO 27001 Internal Audit, ISO 27001 Lead Implementer, iso 27002

Jan 12 2026

Security Without Risk Context Is Noise: How Cyber Risk Assessment Drives Better Decisions

Category: Information Security,Risk Assessment,Security Risk Assessment — disc7 @ 8:07 am

Below is a clear, structured explanation Cybersecurity Risk Assessment Process

What Is a Cybersecurity Risk Assessment?

A cybersecurity risk assessment is a structured process for understanding how cyber threats could impact the business, not just IT systems. Its purpose is to identify what assets matter most, what could go wrong, how likely those events are, and what the consequences would be if they occur. Rather than focusing on tools or controls first, a risk assessment provides decision-grade insight that leadership can use to prioritize investments, allocate resources, and accept or reduce risk knowingly. When aligned with frameworks like ISO 27001, NIST CSF, and COSO, it creates a common language between security, executives, and the board.

1. Identify Assets & Data

The first step is to identify and inventory critical assets, including hardware, software, cloud services, networks, data, and sensitive information. This step answers the fundamental question: what are we actually protecting? Without a clear understanding of assets and their business value, security efforts become unfocused. Many breaches stem from misconfigured or forgotten assets, making visibility and ownership essential to effective risk management.

2. Identify Threats

Once assets are known, the next step is identifying the threats that could realistically target them. These include external threats such as malware, ransomware, phishing, and supply chain attacks, as well as internal threats like insider misuse or human error. Threat identification focuses on who might attack, how, and why, based on real-world attack patterns rather than hypothetical scenarios.

3. Identify Vulnerabilities

Vulnerabilities are weaknesses that threats can exploit. These may exist in system configurations, software, access controls, processes, or human behavior. This step examines where defenses are insufficient or outdated, such as unpatched systems, excessive privileges, weak authentication, or lack of security awareness. Vulnerabilities are the bridge between threats and actual incidents.

4. Analyze Likelihood

Likelihood analysis evaluates how probable it is that a given threat will successfully exploit a vulnerability. This assessment considers threat actor capability, exposure, historical incidents, and the effectiveness of existing controls. The goal is not precision but reasonable estimation, enabling organizations to distinguish between theoretical risks and those that are most likely to occur.

5. Analyze Impact

Impact analysis focuses on the potential business consequences if a risk materializes. This includes financial loss, operational disruption, data theft, regulatory penalties, legal exposure, and reputational damage. By framing impact in business terms rather than technical language, this step ensures that cyber risk is understood as an enterprise risk, not just an IT issue.

6. Evaluate Risk Level

Risk level is determined by combining likelihood and impact, commonly expressed as Risk = Likelihood Ă— Impact. This step allows organizations to rank risks and identify which ones exceed acceptable thresholds. Not all risks require immediate remediation, but all should be understood, documented, and owned at the appropriate level.

7. Treat & Mitigate Risks

Risk treatment involves deciding how to handle each identified risk. Options include remediating the risk through controls, mitigating it by reducing likelihood or impact, transferring it through insurance or contracts, avoiding it by changing business practices, or accepting it when the risk is within tolerance. This step turns analysis into action and aligns security decisions with business priorities.

8. Monitor & Review

Cyber risk is not static. New threats, technologies, and business changes continuously reshape the risk landscape. Monitoring and review ensure that controls remain effective and that risk assessments stay current. This step embeds risk management into ongoing governance rather than treating it as a one-time exercise.

Bottom line:
A cybersecurity risk assessment is not about achieving perfect security—it’s about making informed, defensible decisions in an environment where risk is unavoidable. When done well, it transforms cybersecurity from a technical function into a strategic business capability.

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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 process

Jan 10 2026

When Security Is Optional—Until It Isn’t

Category: cyber security,data security,Information Security,ISO 27k — disc7 @ 2:21 pm

ISO/IEC 27001 is often described as “essential,” but in reality, it remains a voluntary standard rather than a mandatory requirement. Its value depends less on obligation and more on organizational intent.

When leadership genuinely understands how deeply the business relies on information, the importance of managing information risk becomes obvious. In such cases, adopting 27001 is simply a logical extension of good governance.

For informed management teams, information security is not a technical checkbox but a business enabler. They recognize that protecting data protects revenue, reputation, and operational continuity.

In these environments, frameworks like 27001 support disciplined decision-making, accountability, and long-term resilience. The standard provides structure, not bureaucracy.

However, when leadership does not grasp the organization’s information dependency, advocacy often falls on deaf ears. No amount of persuasion will compensate for a lack of awareness.

Pushing too hard in these situations can be counterproductive. Without perceived risk, security efforts are seen as cost, friction, or unnecessary compliance.

Sometimes, the most effective catalyst is experience rather than explanation. A near miss or a real incident often succeeds where presentations and risk registers fail.

Once the business feels tangible pain—financial loss, customer impact, or reputational damage—the conversation changes quickly. Security suddenly becomes urgent and relevant.

That is when security leaders are invited in as problem-solvers, not prophets—stepping forward to help stabilize, rebuild, and guide the organization toward stronger governance and risk management.

My opinion:

This perspective is pragmatic, realistic, and—while a bit cynical—largely accurate in how organizations actually behave.

In an ideal world, leadership would proactively invest in ISO 27001 because they understand information risk as a core business risk. In practice, many organizations only act when risk becomes experiential rather than theoretical. Until there is pain, security feels optional.

That said, waiting for an incident should never be the strategy—it’s simply the pattern we observe. Incidents are expensive teachers, and the damage often exceeds what proactive governance would have cost. From a maturity standpoint, reactive adoption signals weak risk leadership.

The real opportunity for security leaders and vCISOs is to translate information risk into business language before the crisis: revenue impact, downtime, legal exposure, and trust erosion. When that translation lands, 27001 stops being “optional” and becomes a management tool.

Ultimately, ISO 27001 is not about compliance—it’s about decision quality. Organizations that adopt it early tend to be deliberate, resilient, and better governed. Those that adopt it after an incident are often doing damage control.

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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: iso 27001, Real Risk

Jan 09 2026

AI Can Help Our Health — But at What Cost to Privacy?

Category: AI,AI Governance,Information Security — disc7 @ 8:34 am

Potential risks of sharing medical records with a consumer AI platform

  1. OpenAI recently introduced “ChatGPT Health,” a specialized extension of ChatGPT designed to handle health-related conversations and enable users to link their medical records and wellness apps for more personalized insights. The company says this builds on its existing security framework.
  2. According to OpenAI, the new health feature includes “additional, layered protections” tailored to sensitive medical information — such as purpose-built encryption and data isolation that aims to separate health data from other chatbot interactions.
  3. The company also claims that data shared in ChatGPT Health won’t be used to train its broader AI models, a move intended to keep medical information out of the core model’s training dataset.
  4. OpenAI says millions of users widely ask health and wellness questions on its platform already, which it uses to justify a dedicated space where those interactions can be more contextualized and, allegedly, safer.
  5. Privacy advocates, however, are raising serious concerns. They note that medical records uploaded to ChatGPT Health are no longer protected by HIPAA, the U.S. law that governs how healthcare providers safeguard patients’ private health information.
  6. Experts like Sara Geoghegan from the Electronic Privacy Information Center warn that releasing sensitive health data into OpenAI’s systems removes legal privacy protections and exposes users to risk. Without a law like HIPAA applying to ChatGPT, the company’s own policies are the only thing standing between users and potential misuse.
  7. Critics also caution that OpenAI’s evolving business model, particularly if it expands into personalization or advertising, could create incentives to use health data in ways users don’t expect or fully understand.
  8. Key questions remain unanswered, such as how exactly the company would respond to law enforcement requests for health data and how effectively health data is truly isolated from other systems if policies change.
  9. The feature’s reliance on connected wellness apps and external partners also introduces additional vectors where sensitive information could potentially be exposed or accessed if there’s a breach or policy change.
  10. In summary, while OpenAI pitches ChatGPT Health as an innovation with enhanced safeguards, privacy advocates argue that without robust legal protections and clear transparency, sharing medical records with a consumer AI platform remains risky.

My Opinion

AI has immense potential to augment how people manage and understand their health, especially for non-urgent questions or preparing for doctor visits. But giving any tech company access to medical records without the backing of strong legal protections like HIPAA feels premature and potentially unsafe. Technical safeguards such as encryption and data isolation matter — but they don’t replace enforceable privacy laws that restrict how health data can be used, shared, or disclosed. In healthcare, trust and accountability are paramount, and without those, even well-intentioned tools can expose individuals to privacy risks or misuse of deeply personal information. Until regulatory frameworks evolve to explicitly protect AI-mediated health data, users should proceed with caution and understand the privacy trade-offs they’re making.

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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 Health, ChatGPT Health, privacy concerns

Jan 07 2026

Agentic AI: Why Autonomous Systems Redefine Enterprise Risk

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

Evolution of Agentic AI

1. Machine Learning

Machine Learning represents the foundation of modern AI, focused on learning patterns from structured data to make predictions or classifications. Techniques such as regression, decision trees, support vector machines, and basic neural networks enable systems to automate well-defined tasks like forecasting, anomaly detection, and image or object recognition. These systems are effective but largely reactive—they operate within fixed boundaries and lack reasoning or adaptability beyond their training data.

2. Neural Networks

Neural Networks expand on traditional machine learning by enabling deeper pattern recognition through layered architectures. Convolutional and recurrent neural networks power image recognition, speech processing, and sequential data analysis. Capabilities such as deep reinforcement learning allow systems to improve through feedback, but decision-making is still task-specific and opaque, with limited ability to explain reasoning or generalize across domains.

3. Large Language Models (LLMs)

Large Language Models introduce reasoning, language understanding, and contextual awareness at scale. Built on transformer architectures and self-attention mechanisms, models like GPT enable in-context learning, chain-of-thought reasoning, and natural language interaction. LLMs can synthesize knowledge, generate code, retrieve information, and support complex workflows, marking a shift from pattern recognition to generalized cognitive assistance.

4. Generative AI

Generative AI extends LLMs beyond text into multimodal creation, including images, video, audio, and code. Capabilities such as diffusion models, retrieval-augmented generation, and multimodal understanding allow systems to generate realistic content and integrate external knowledge sources. These models support automation, creativity, and decision support but still rely on human direction and lack autonomy in planning or execution.

5. Agentic AI

Agentic AI represents the transition from AI as a tool to AI as an autonomous actor. These systems can decompose goals, plan actions, select and orchestrate tools, collaborate with other agents, and adapt based on feedback. Features such as memory, state persistence, self-reflection, human-in-the-loop oversight, and safety guardrails enable agents to operate over time and across complex environments. Agentic AI is less about completing individual tasks and more about coordinating context, tools, and decisions to achieve outcomes.

Key Takeaway

The evolution toward Agentic AI is not a single leap but a layered progression—from learning patterns, to reasoning, to generating content, and finally to autonomous action. As organizations adopt agentic systems, governance, risk management, and human oversight become just as critical as technical capability.

Security and governance lens (AI risk, EU AI Act, NIST AI RMF)

Zero Trust Agentic AI Security: Runtime Defense, Governance, and Risk Management for Autonomous Systems

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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: Agentic AI, Autonomous syatems, Enterprise Risk Management

Jan 06 2026

The Best Cybersecurity Investment Strategy: Balance Fast Wins with Long-Term Resilience

Category: Cyber Investment,Cyber resilience,Cyber Strategy,Information Security — disc7 @ 9:44 am

Cybersecurity Investment Strategy: Investing With Intent

One of the most common mistakes organizations make in cybersecurity is investing in tools and controls without a clear, outcome-driven strategy. Not every security investment delivers value at the same speed, and not all controls produce the same long-term impact. Without prioritization, teams often overspend on complex solutions while leaving foundational gaps exposed.

A smarter approach is to align security initiatives based on investment level versus time to results. This is where a Cybersecurity Investment Strategy Matrix becomes valuable—it helps leaders visualize which initiatives deliver immediate risk reduction and which ones compound value over time. The goal is to focus resources on what truly moves the needle for the business.

Some initiatives provide fast results but require higher investment. Capabilities like EDR/XDR, SIEM and SOAR platforms, incident response readiness, and next-generation firewalls can rapidly improve detection and response. These are often critical for organizations facing active threats or regulatory pressure, but they demand both financial and operational commitment.

Other controls deliver fast results with relatively low investment. Measures such as multi-factor authentication, password managers, security baselines, and basic network segmentation quickly reduce attack surface and prevent common breaches. These are often high-impact, low-friction wins that should be implemented early.

Certain investments are designed for long-term payoff and require significant investment. Zero Trust architectures, DevSecOps, CSPM, identity governance, and DLP programs strengthen security at scale and maturity, but they take time, cultural change, and sustained funding to deliver full value.

Finally, there are long-term, low-investment initiatives that quietly build resilience over time. Security awareness training, vulnerability management, penetration testing, security champions programs, strong documentation, meaningful KPIs, and open-source security tools all improve security posture steadily without heavy upfront costs.

A well-designed cybersecurity investment strategy balances quick wins with long-term capability building. The real question for leadership isn’t what tools to buy, but which three initiatives, if prioritized today, would reduce the most risk and support the business right now.

My opinion: the best cybersecurity investment strategy is a balanced, risk-driven mix of fast wins and long-term foundations, not an “all-in” bet on any single quadrant.

Here’s why:

  1. Start with fast results / low investment (mandatory baseline)
    This should be non-negotiable. Controls like MFA, security baselines, password managers, and basic network segmentation deliver immediate risk reduction at minimal cost. Skipping these while investing in advanced tools is one of the most common (and expensive) mistakes I see.
  2. Add 1–2 fast results / high investment controls (situational)
    Once the basics are in place, selectively invest in high-impact capabilities like EDR/XDR or incident response readiness—only if your threat profile, regulatory exposure, or business criticality justifies it. These tools are powerful, but without maturity, they become noisy and underutilized.
  3. Continuously build long-term / low investment foundations (quiet multiplier)
    Security awareness, vulnerability management, documentation, and KPIs don’t look flashy, but they compound over time. These initiatives increase ROI on every other control you deploy and reduce operational friction.
  4. Delay long-term / high investment initiatives until maturity exists
    Zero Trust, DevSecOps, DLP, and identity governance are excellent goals—but pursuing them too early often leads to stalled programs and wasted spend. These work best when governance, ownership, and basic hygiene are already solid.

Bottom line:
The best strategy is baseline first → targeted protection next → long-term maturity in parallel.
If I had to simplify it:

Fix what attackers exploit most today, while quietly building the capabilities that prevent tomorrow’s failures.

This approach aligns security spend with business risk, avoids tool sprawl, and delivers both immediate and sustained value.

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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 Investment Strategy

Jan 03 2026

8 Practical Cybersecurity Steps Every Small Business Can Take Today

Category: cyber security,Information Security — disc7 @ 11:47 am

Many small and medium businesses are attractive targets for cybercriminals because they hold valuable data and often lack extensive IT resources. Threats like ransomware, phishing and business email compromise can disrupt operations, damage reputation, and cause financial loss. Recognizing that no business is too small to be targeted is the first step toward protection.

1. Teach employees to recognize and report phishing attacks. Phishing is one of the primary ways attackers gain access. Regular awareness training helps staff spot suspicious emails, links, and requests, reducing the chance that a click triggers a breach.

2. Require strong passwords across your organization. Weak or reused passwords are easily guessed or brute-forced. Establish a strong password policy and consider tools like password managers so employees can securely store unique credentials.

3. Implement multifactor authentication (MFA). Adding MFA means users must provide more than just a password to access accounts. This extra layer of verification dramatically reduces the odds that attackers can impersonate employees, even if they obtain a password.

4. Keep systems and software up to date. Outdated software often contains known security flaws that attackers exploit. Having regular patching schedules and enabling automatic updates wherever possible keeps your systems protected against many common vulnerabilities.

5. Enable logging and monitoring. Logging system activity gives you visibility into what’s happening on your network. Monitoring logs helps detect suspicious behavior early, so you can respond before an incident becomes a major breach.

6. Back up your business data regularly. Ransomware and other failures can cripple operations if you can’t access critical files. Maintain backups following a reliable strategy—such as the 3-2-1 rule—to ensure you can restore data quickly and resume business functions.

7. Encrypt sensitive data and devices. Encryption transforms your data into unreadable code for anyone without access keys. Applying encryption to data at rest and in transit helps protect information even if a device is lost or a system is compromised.

8. Report cyber incidents and share threat information. If an incident occurs, reporting it to agencies like CISA helps the broader business community stay informed about emerging threats and may provide access to additional guidance or alerts.

Taken together, these steps create a practical cybersecurity foundation for your business. Start with basics like employee training and MFA, then build up to backups, encryption, and incident reporting to strengthen your resiliency against evolving threats.

Source: You Can Protect Your Business from Online Threats (CISA)

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Tags: Cybersecurity for SMBs

Dec 30 2025

From Regulation to Revenue: The Power of Strong Security Compliance

Category: Information Security — disc7 @ 8:15 am

Compliance today isn’t just about checking boxes — it’s directly tied to business survival and stakeholder trust.

Organizations now face intense scrutiny from clients, regulators, and supply chain partners. With reputations and revenue on the line, getting compliance right the first time is essential.

DISC InfoSec has been leading that mission since 2002, supporting businesses across industries in achieving and sustaining certification.

Our team includes seasoned specialists with over 20 years of practical experience in security and compliance.

We specialize in multi-framework strategies — including ISO 27001, ISO 42001, GDPR, SOC 2, PCI, and HIPAA — allowing companies to streamline efforts and reduce operational costs.

AI is rapidly reshaping how organizations operate—but without strong oversight, it introduces serious regulatory, ethical, and operational challenges.

ISO 42001 delivers a structured governance framework to ensure AI is developed and used responsibly. It focuses on key safeguards such as bias mitigation, transparency, accountability, and ongoing performance monitoring—especially vital for high-risk sectors like defense, healthcare, and finance.

100% Certification Success: Why Businesses Trust DISC InfoSec

This approach is why we have a 100% client certification success rate with zero exceptions. Every organization we support passes.

From global enterprises to early-stage innovators, we help build security programs that protect contracts, strengthen customer confidence, and ultimately fuel business growth.

When the stakes are high and compliance is mission-critical, you deserve a partner who delivers results — every time.

Partner with DISC InfoSec to secure your compliance roadmap and safeguard your business advantage.
📩 Contact: Info@DeuraInfoSec.com
🔐 www.DeuraInfoSec.com

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Dec 29 2025

12 Pillars of Cybersecurity

Category: cyber security,Information Security — disc7 @ 9:56 am

12 Pillars of Cybersecurity — Simplified Overview — Start by getting the basics right — it’s the foundation of every effective security program.

1️⃣ Disaster Recovery
Disaster Recovery ensures organizations can quickly restore systems and data after a disruptive event such as ransomware, hardware failure, or natural disasters. A well-designed plan includes data backups, documented recovery procedures, and resilience testing so the business can continue operating with minimal downtime.

2️⃣ Authentication
Authentication verifies that users are who they claim to be. Strong password policies, secure login controls, and multifactor authentication (MFA) help prevent unauthorized access to critical systems, reducing the risk of credential theft and account compromise.

3️⃣ Authorization
Authorization determines what authenticated users are allowed to do. Properly managed access roles and least-privilege principles ensure individuals only access the information needed for their job, minimizing internal misuse and breach exposure.

4️⃣ Encryption
Encryption protects sensitive data by making it unreadable to unauthorized entities. Whether data is stored or in transit, encryption standards like TLS help maintain confidentiality and integrity, even if attackers intercept it.

5️⃣ Vulnerability Management
This includes identifying weaknesses in applications, systems, or configurations before attackers exploit them. Regular scanning, patching, and proactive remediation are essential to stay ahead of constantly emerging threats.

6️⃣ Audit & Compliance
Audit and compliance confirm that cybersecurity controls meet legal, industry, and internal requirements. Through continuous monitoring, reporting, and assessments, organizations strengthen governance and reduce regulatory risk.

7️⃣ Network Security
Network security protects communication flowing between devices and systems. Firewalls, intrusion detection, segmentation, and DNS security reduce unauthorized access and lateral movement inside the network.

8️⃣ Terminal (Endpoint) Security
Endpoints—like laptops, servers, and mobile devices—must be protected from malware and misuse. Tools such as EDR (Endpoint Detection & Response), encryption, and device control help secure data where employees work every day.

9️⃣ Emergency Response
Incident Response and business continuity actions are triggered when a cyberattack occurs. Quick detection, containment, and communication limit damage and accelerate recovery while maintaining stakeholder trust.

🔟 Container Security
Containerized workloads, used heavily in cloud environments, require specialized protections. Securing container images, runtime behavior, and orchestration platforms prevents vulnerabilities from spreading rapidly across applications.

1️⃣1️⃣ API Security
APIs are now core to digital integrations, making them a prime target for attackers. Secure authentication, encryption, rate limiting, and runtime monitoring protect data shared between systems and prevent unauthorized access.

1️⃣2️⃣ Third-Party / Vendor Management
Vendors introduce additional risk since their systems may connect to yours. Risk assessments, clear security expectations, and continuous monitoring help ensure third-party access doesn’t become the weakest link.

⭐ Expert Opinion

These 12 pillars offer a strong foundational framework — but cybersecurity only works when measurements, monitoring, and automation continuously improve these controls. With attackers advancing faster every year, organizations must treat cybersecurity as an adaptable lifecycle, not a one-time checklist. Prioritized risk-based implementation and skilled oversight remain the keys to real cyber resilience.

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Dec 12 2025

When a $3K “cybersecurity gap assessment” reveals you don’t actually have cybersecurity to assess…

Category: Information Security,ISO 27k,vCISO — disc7 @ 8:51 am

When a $3K “cybersecurity gap assessment” reveals you don’t actually have cybersecurity to assess…

A prospect just reached out wanting to pay me $3,000 to assess their ISO 27001 readiness.

Here’s how that conversation went:

Me: “Can you share your security policies and procedures?” Them: “We don’t have any.”

Me: “How about your latest penetration test, vulnerability scans, or cloud security assessments?” Them: “Nothing.”

Me: “What about your asset inventory, vendor register, or risk assessments?” Them: “We haven’t done those.”

Me: “Have you conducted any vendor security due diligence or data privacy reviews?” Them: “No.”

Me: “Let’s try HR—employee contracts, job descriptions, onboarding/offboarding procedures?” Them: “It’s all ad hoc. Nothing formal.”

Here’s the problem: You can’t assess what doesn’t exist.

It’s like subscribing to a maintenance plan for an appliance you don’t own yet

The reality? Many organizations confuse “having IT systems” with “having cybersecurity.” They’re running business-critical operations with zero security foundation—no documentation, no testing, no governance.

What they actually need isn’t an assessment. It’s a security program built from the ground up.

ISO 27001 compliance isn’t a checkbox exercise. It requires: âś“ Documented policies and risk management processes âś“ Regular security testing and validation âś“ Asset and vendor management frameworks âś“ HR security controls and awareness training

If you’re in this situation, here’s my advice: Don’t waste money on assessments. Invest in building foundational security controls first. Then assess.

What’s your take? Have you encountered organizations confusing security assessment with security implementation?

#CyberSecurity #ISO27001 #InfoSec #RiskManagement #ISMS

DISC InfoSec blog post on ISO 27k

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Get in touch if you want a thorough evaluation of how your environment aligns with ISO 27001 or ISO 42001 requirements.

Tags: iso 27001, ISO 27001 gap assessment

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