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 They can reproduce insecure patterns, suggest outdated APIs, or introduce unvetted components.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 28 2026

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

Category: Attack Matrix,owaspdisc7 @ 9:42 am

Below is a clear, practitioner-oriented breakdown of the OWASP Top 10 Web Application Security Risks (latest major revision: OWASP Top 10 – 2021, which is still the current standard as of 2025).

I’ll cover what each risk is, why it matters, common attack examples, and practical mitigations—useful for both defensive (Blue Team) and offensive (Red Team / pentesting) perspectives.


1. A01: Broken Access Control

What it is:
Failures that allow users to act outside their intended permissions.

Why it matters:
Access control flaws are the most common and most damaging web app vulnerabilities.

Common examples:

  • IDOR (Insecure Direct Object Reference): /api/users/1234
  • Horizontal privilege escalation
  • Vertical privilege escalation (user → admin)
  • Missing authorization checks on API endpoints

Attack scenario:

GET /api/invoices/9876

Attacker changes 9876 to another user’s invoice ID.

Mitigations:

  • Enforce server-side authorization on every request
  • Use deny-by-default policies
  • Implement role-based access control (RBAC)
  • Log and alert on access control failures

2. A02: Cryptographic Failures

(formerly Sensitive Data Exposure)

What it is:
Improper protection of sensitive data in transit or at rest.

Why it matters:
Leads directly to data breaches, credential theft, and compliance violations.

Common examples:

  • Plaintext passwords
  • Weak hashing (MD5, SHA1)
  • No HTTPS or weak TLS
  • Hardcoded secrets

Attack scenario:

  • Attacker intercepts traffic over HTTP
  • Dumps password hashes and cracks them offline

Mitigations:

  • Use TLS 1.2+ everywhere
  • Hash passwords with bcrypt / Argon2
  • Encrypt sensitive data at rest
  • Proper key management (HSM, KMS)

3. A03: Injection

What it is:
Untrusted data is interpreted as code by an interpreter.

Why it matters:
Injection often leads to full database compromise or RCE.

Common types:

  • SQL Injection
  • NoSQL Injection
  • Command Injection
  • LDAP Injection

Attack scenario (SQLi):

' OR 1=1--

Mitigations:

  • Use parameterized queries
  • Avoid dynamic query construction
  • Input validation (allow-lists)
  • ORM frameworks (used correctly)

4. A04: Insecure Design

What it is:
Architectural or design flaws that cannot be fixed with simple code changes.

Why it matters:
Secure coding cannot fix insecure architecture.

Common examples:

  • No rate limiting
  • No threat modeling
  • Trusting client-side validation
  • Missing business logic controls

Attack scenario:

  • Unlimited password attempts → credential stuffing

Mitigations:

  • Perform threat modeling
  • Use secure design patterns
  • Abuse-case testing
  • Define security requirements early

5. A05: Security Misconfiguration

What it is:
Improperly configured frameworks, servers, or platforms.

Why it matters:
Misconfigurations are easy to exploit and extremely common.

Common examples:

  • Default credentials
  • Stack traces exposed
  • Open admin panels
  • Directory listing enabled

Attack scenario:

  • Attacker finds /admin or /phpinfo.php

Mitigations:

  • Harden systems (CIS benchmarks)
  • Disable unused features
  • Automated configuration audits
  • Secure deployment pipelines

6. A06: Vulnerable and Outdated Components

What it is:
Using libraries or components with known vulnerabilities.

Why it matters:
Many breaches occur via third-party dependencies.

Common examples:

  • Log4Shell (Log4j)
  • Old jQuery with XSS
  • Outdated CMS plugins

Attack scenario:

  • Exploit known CVE with public PoC

Mitigations:

  • Maintain an SBOM
  • Regular dependency updates
  • Use tools like:
    • OWASP Dependency-Check
    • Snyk
    • Dependabot

7. A07: Identification and Authentication Failures

What it is:
Weak authentication or session management.

Why it matters:
Allows account takeover and impersonation.

Common examples:

  • Weak passwords
  • No MFA
  • Session fixation
  • JWT misconfiguration

Attack scenario:

  • Brute-force login without rate limiting

Mitigations:

  • Enforce strong password policies
  • Implement MFA
  • Secure session cookies (HttpOnly, Secure)
  • Proper JWT validation

8. A08: Software and Data Integrity Failures

What it is:
Failure to protect integrity of code and data.

Why it matters:
Leads to supply chain attacks.

Common examples:

  • Unsigned updates
  • Insecure CI/CD pipelines
  • Deserialization flaws

Attack scenario:

  • Malicious dependency injected during build

Mitigations:

  • Code signing
  • Secure CI/CD pipelines
  • Validate serialized data
  • Use trusted repositories only

9. A09: Security Logging and Monitoring Failures

What it is:
Insufficient logging and alerting.

Why it matters:
Attacks go undetected or are discovered too late.

Common examples:

  • No login failure logs
  • No alerting on privilege escalation
  • Logs not protected

Attack scenario:

  • Attacker maintains persistence for months unnoticed

Mitigations:

  • Centralized logging (SIEM)
  • Log authentication and authorization events
  • Real-time alerting
  • Incident response plans

10. A10: Server-Side Request Forgery (SSRF)

What it is:
Server makes unauthorized requests on behalf of attacker.

Why it matters:
Can lead to cloud metadata compromise and internal network access.

Common examples:

  • Fetching URLs without validation
  • Accessing 169.254.169.254 (cloud metadata)

Attack scenario:

POST /fetch?url=http://localhost/admin

Mitigations:

  • URL allow-listing
  • Block internal IP ranges
  • Disable unnecessary outbound requests
  • Network segmentation

OWASP Top 10 Summary Table

RankCategory
A01Broken Access Control
A02Cryptographic Failures
A03Injection
A04Insecure Design
A05Security Misconfiguration
A06Vulnerable & Outdated Components
A07Identification & Authentication Failures
A08Software & Data Integrity Failures
A09Logging & Monitoring Failures
A10SSRF

How This Is Used in Practice

  • Developers: Secure coding & design baseline
  • Pentesters: Test case foundation
  • Blue Teams: Control prioritization
  • Compliance: Mapping to ISO 27001, PCI-DSS, SOC 2

Below is a practical alignment of OWASP Top 10 (2021) with MITRE ATT&CK (Enterprise).
This mapping is widely used in threat modeling, purple-team exercises, and SOC detection engineering to bridge application-layer risk with adversary behavior.

⚠️ Important:
OWASP describes what is vulnerable; MITRE ATT&CK describes how adversaries operate.
The mapping is therefore many-to-many, not 1:1.


OWASP Top 10 ↔ MITRE ATT&CK Mapping


A01 – Broken Access Control

Core Risk: Unauthorized actions and privilege escalation

MITRE ATT&CK Techniques

  • T1068 – Exploitation for Privilege Escalation
  • T1078 – Valid Accounts
  • T1098 – Account Manipulation
  • T1548 – Abuse Elevation Control Mechanism

Real-World Flow

  1. Attacker exploits IDOR
  2. Accesses admin-only endpoints
  3. Performs privilege escalation

Detection Focus

  • Unusual object access patterns
  • Privilege changes without admin action
  • Cross-account data access

A02 – Cryptographic Failures

Core Risk: Exposure of credentials or sensitive data

MITRE ATT&CK Techniques

  • T1555 – Credentials from Password Stores
  • T1003 – OS Credential Dumping
  • T1040 – Network Sniffing
  • T1110 – Brute Force

Real-World Flow

  1. Intercept plaintext credentials
  2. Crack weak hashes
  3. Reuse credentials for lateral access

Detection Focus

  • TLS downgrade attempts
  • Excessive authentication failures
  • Credential reuse anomalies

A03 – Injection

Core Risk: Interpreter abuse leading to DB or OS compromise

MITRE ATT&CK Techniques

  • T1190 – Exploit Public-Facing Application
  • T1059 – Command and Scripting Interpreter
  • T1505 – Server Software Component Abuse

Real-World Flow

  1. SQLi in login form
  2. Dump credentials
  3. RCE via stacked queries

Detection Focus

  • SQL syntax errors in logs
  • Unexpected shell execution
  • WAF rule triggers

A04 – Insecure Design

Core Risk: Business logic and architectural weaknesses

MITRE ATT&CK Techniques

  • T1499 – Endpoint Denial of Service
  • T1110 – Brute Force
  • T1213 – Data from Information Repositories

Real-World Flow

  1. Abuse missing rate limits
  2. Enumerate accounts
  3. Mass data harvesting

Detection Focus

  • High-frequency request patterns
  • Logic abuse (valid requests, malicious intent)
  • API misuse metrics

A05 – Security Misconfiguration

Core Risk: Default or insecure settings

MITRE ATT&CK Techniques

  • T1580 – Cloud Infrastructure Discovery
  • T1082 – System Information Discovery
  • T1190 – Exploit Public-Facing Application

Real-World Flow

  1. Discover open admin interfaces
  2. Access debug endpoints
  3. Extract secrets/configs

Detection Focus

  • Access to admin/debug endpoints
  • Configuration file exposure attempts
  • Unexpected service enumeration

A06 – Vulnerable & Outdated Components

Core Risk: Known CVEs exploited

MITRE ATT&CK Techniques

  • T1190 – Exploit Public-Facing Application
  • T1210 – Exploitation of Remote Services
  • T1505.003 – Web Shell

Real-World Flow

  1. Exploit known CVE (e.g., Log4Shell)
  2. Deploy web shell
  3. Persistence achieved

Detection Focus

  • Known exploit signatures
  • Abnormal child processes
  • Web shell indicators

A07 – Identification & Authentication Failures

Core Risk: Account takeover

MITRE ATT&CK Techniques

  • T1110 – Brute Force
  • T1078 – Valid Accounts
  • T1539 – Steal Web Session Cookie

Real-World Flow

  1. Credential stuffing
  2. Session hijacking
  3. Account takeover

Detection Focus

  • Geo-impossible logins
  • MFA bypass attempts
  • Session reuse patterns

A08 – Software & Data Integrity Failures

Core Risk: Supply chain compromise

MITRE ATT&CK Techniques

  • T1195 – Supply Chain Compromise
  • T1059 – Command Execution
  • T1608 – Stage Capabilities

Real-World Flow

  1. Malicious dependency injected
  2. Code executes during build
  3. Backdoor deployed

Detection Focus

  • Unsigned builds
  • Unexpected CI pipeline changes
  • Integrity check failures

A09 – Logging & Monitoring Failures

Core Risk: Undetected compromise

MITRE ATT&CK Techniques

  • T1562 – Impair Defenses
  • T1070 – Indicator Removal on Host
  • T1027 – Obfuscated/Encrypted Payloads

Real-World Flow

  1. Disable logging
  2. Clear logs
  3. Persist undetected

Detection Focus

  • Gaps in telemetry
  • Sudden log volume drops
  • Disabled security agents

A10 – Server-Side Request Forgery (SSRF)

Core Risk: Internal service abuse

MITRE ATT&CK Techniques

  • T1190 – Exploit Public-Facing Application
  • T1046 – Network Service Discovery
  • T1552 – Unsecured Credentials

Real-World Flow

  1. SSRF to cloud metadata service
  2. Extract IAM credentials
  3. Pivot into cloud environment

Detection Focus

  • Requests to metadata IPs
  • Internal-only endpoint access
  • Abnormal outbound traffic

Visual Summary (Condensed)

OWASP CategoryMITRE ATT&CK Tactics
Access ControlPrivilege Escalation, Credential Access
Crypto FailuresCredential Access, Collection
InjectionInitial Access, Execution
Insecure DesignCollection, Impact
MisconfigurationDiscovery, Initial Access
Vulnerable ComponentsInitial Access, Persistence
Auth FailuresCredential Access
Integrity FailuresSupply Chain, Execution
Logging FailuresDefense Evasion
SSRFDiscovery, Lateral Movement

How to Use This Mapping Practically

🔵 Blue Team

  • Map OWASP risks → detection rules
  • Prioritize logging for ATT&CK techniques
  • Improve SIEM correlation

🔴 Red Team

  • Convert OWASP findings into ATT&CK chains
  • Report findings in ATT&CK language
  • Increase exec-level clarity

🟣 Purple Team

  • Design attack simulations
  • Validate SOC coverage
  • Measure MTTD/MTTR

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 Top 10 Web Application Security Risks


Dec 26 2025

Why AI-Driven Cybersecurity Frameworks Are Now a Business Imperative

Category: AI,AI Governance,ISO 27k,ISO 42001,NIST CSF,owaspdisc7 @ 8:52 am

A reliable industry context about AI and cybersecurity frameworks from recent market and trend reports. I’ll then give a clear opinion at the end.


1. AI Is Now Core to Cyber Defense
Artificial Intelligence is transforming how organizations defend against digital threats. Traditional signature-based security tools struggle to keep up with modern attacks, so companies are using AI—especially machine learning and behavioral analytics—to detect anomalies, predict risks, and automate responses in real time. This integration is now central to mature cybersecurity programs.

2. Market Expansion Reflects Strategic Adoption
The AI cybersecurity market is growing rapidly, with estimates projecting expansion from tens of billions today into the hundreds of billions within the next decade. This reflects more than hype—organizations across sectors are investing heavily in AI-enabled threat platforms to improve detection, reduce manual workload, and respond faster to attacks.

3. AI Architectures Span Detection to Response
Modern frameworks incorporate diverse AI technologies such as natural language processing, neural networks, predictive analytics, and robotic process automation. These tools support everything from network monitoring and endpoint protection to identity-based threat management and automated incident response.

4. Cloud and Hybrid Environments Drive Adoption
Cloud migrations and hybrid IT architectures have expanded attack surfaces, prompting more use of AI solutions that can scale across distributed environments. Cloud-native AI tools enable continuous monitoring and adaptive defenses that are harder to achieve with legacy on-premises systems.

5. Regulatory and Compliance Imperatives Are Growing
As digital transformation proceeds, regulatory expectations are rising too. Many frameworks now embed explainable AI and compliance-friendly models that help organizations demonstrate legal and ethical governance in areas like data privacy and secure AI operations.

6. Integration Challenges Remain
Despite the advantages, adopting AI frameworks isn’t plug-and-play. Organizations face hurdles including high implementation cost, lack of skilled AI security talent, and difficulties integrating new tools with legacy architectures. These challenges can slow deployment and reduce immediate ROI. (Inferred from general market trends)

7. Sophisticated Threats Demand Sophisticated Defenses
AI is both a defensive tool and a capability leveraged by attackers. Adversarial AI can generate more convincing phishing, exploit model weaknesses, and automate aspects of attacks. A robust cybersecurity framework must account for this dual role and include AI-specific risk controls.

8. Organizational Adoption Varies Widely
Enterprise adoption is strong, especially in regulated sectors like finance, healthcare, and government, while many small and medium businesses remain cautious due to cost and trust issues. This uneven adoption means frameworks must be flexible enough to suit different maturity levels. (From broader industry reports)

9. Frameworks Are Evolving With the Threat Landscape
Rather than static checklists, AI cybersecurity frameworks now emphasize continuous adaptation—integrating real-time risk assessment, behavioral intelligence, and autonomous response capabilities. This shift reflects the fact that cyber risk is dynamic and cannot be mitigated solely by periodic assessments or manual controls.


Opinion

AI-centric cybersecurity frameworks represent a necessary evolution in defense strategy, not a temporary trend. The old model of perimeter defense and signature matching simply doesn’t scale in an era of massive data volumes, sophisticated AI-augmented threats, and 24/7 cloud operations. However, the promise of AI must be tempered with governance rigor. Organizations that treat AI as a magic bullet will face blind spots and risks—especially around privacy, explainability, and integration complexity.

Ultimately, the most effective AI cybersecurity frameworks will balance automated, real-time intelligence with human oversight and clear governance policies. This blend maximizes defensive value while mitigating potential misuse or operational failures.

AI Cybersecurity Framework — Summary

AI Cybersecurity framework provides a holistic approach to securing AI systems by integrating governance, risk management, and technical defense across the full AI lifecycle. It aligns with widely-accepted standards such as NIST RMF, ISO/IEC 42001, OWASP AI Security Top 10, and privacy regulations (e.g., GDPR, CCPA).


1️⃣ Govern

Set strategic direction and oversight for AI risk.

  • Goals: Define policies, accountability, and acceptable risk levels
  • Key Controls: AI governance board, ethical guidelines, compliance checks
  • Outcomes: Approved AI policies, clear governance structures, documented risk appetite


2️⃣ Identify

Understand what needs protection and the related risks.

  • Goals: Map AI assets, data flows, threat landscape
  • Key Controls: Asset inventory, access governance, threat modeling
  • Outcomes: Risk register, inventory map, AI threat profiles


3️⃣ Protect

Implement safeguards for AI data, models, and infrastructure.

  • Goals: Prevent unauthorized access and protect model integrity
  • Key Controls: Encryption, access control, secure development lifecycle
  • Outcomes: Hardened architecture, encrypted data, well-trained teams


4️⃣ Detect

Find signs of attack or malfunction in real time.

  • Goals: Monitor models, identify anomalies early
  • Key Controls: Logging, threat detection, model behavior monitoring
  • Outcomes: Alerts, anomaly reports, high-quality threat intelligence


5️⃣ Respond

Act quickly to contain and resolve security incidents.

  • Goals: Minimize damage and prevent escalation
  • Key Controls: Incident response plans, investigations, forensics
  • Outcomes: Detailed incident reports, corrective actions, improved readiness


6️⃣ Recover

Restore normal operations and reduce the chances of repeat incidents.

  • Goals: Service continuity and post-incident improvement
  • Key Controls: Backup and recovery, resilience testing
  • Outcomes: Restored systems and lessons learned that enhance resilience


Cross-Cutting Principles

These safeguards apply throughout all phases:

  • Ethics & Fairness: Reduce bias, ensure transparency
  • Explainability & Interpretability: Understand model decisions
  • Human-in-the-Loop: Oversight and accountability remain essential
  • Privacy & Security: Protect data by design


AI-Specific Threats Addressed

  • Adversarial attacks (poisoning, evasion)
  • Model theft and intellectual property loss
  • Data leakage and inference attacks
  • Bias manipulation and harmful outcomes


Overall Message

This framework ensures trustworthy, secure, and resilient AI operations by applying structured controls from design through incident recovery—combining cybersecurity rigor with ethical and responsible AI practices.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

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Tags: AI-Driven Cybersecurity Frameworks


Nov 09 2025

🧭 5 Steps to Use OWASP AI Maturity Assessment (AIMA) Today

Category: AI,AI Governance,ISO 42001,owaspdisc7 @ 9:21 pm

1️⃣ Define Your AI Scope
Start by identifying where AI is used across your organization—products, analytics, customer interactions, or internal automation. Knowing your AI footprint helps focus the maturity assessment on real, operational risks.

2️⃣ Map to AIMA Domains
Review the eight domains of AIMA—Responsible AI, Governance, Data Management, Privacy, Design, Implementation, Verification, and Operations. Map your existing practices or policies to these areas to see what’s already in place.

3️⃣ Assess Current Maturity
Use AIMA’s Create & Promote / Measure & Improve scales to rate your organization from Level 1 (ad-hoc) to Level 5 (optimized). Keep it honest—this isn’t an audit, it’s a self-check to benchmark progress.

4️⃣ Prioritize Gaps
Identify where maturity is lowest but risk is highest—often in governance, explainability, or post-deployment monitoring. Focus improvement plans there first to get the biggest security and compliance return.

5️⃣ Build a Continuous Improvement Loop
Integrate AIMA metrics into your existing GRC dashboards or risk scorecards. Reassess quarterly to track progress, demonstrate AI governance maturity, and stay aligned with emerging standards like ISO 42001 and the EU AI Act.


💡 Tip: You can combine AIMA with ISO 42001 or NIST AI RMF for a stronger governance framework—perfect for organizations starting their AI compliance journey.

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

 Limited-Time Offer: ISO/IEC 42001 Compliance Assessment – Clauses 4-10

Evaluate your organization’s compliance with mandatory AIMS clauses through our 5-Level Maturity Model

Limited-Time Offer — Available Only Till the End of This Month!
Get your Compliance & Risk Assessment today and uncover hidden gaps, maturity insights, and improvement opportunities that strengthen your organization’s AI Governance and Security Posture.

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Check out our earlier posts on AI-related topics: AI topic

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Tags: AIMA, Use OWASP AI Maturity Assessment


Aug 20 2025

The highlights from the OWASP AI Maturity Assessment framework

Category: AI,owaspdisc7 @ 3:51 pm

1. Purpose and Scope

The OWASP AI Maturity Assessment provides organizations with a structured way to evaluate how mature their practices are in managing the security, governance, and ethical use of AI systems. Its scope goes beyond technical safeguards, emphasizing a holistic approach that covers people, processes, and technology.

2. Core Maturity Domains

The framework divides maturity into several domains: governance, risk management, security, compliance, and operations. Each domain contains clear criteria that organizations can use to assess themselves and identify both strengths and weaknesses in their AI security posture.

3. Governance and Oversight

A strong governance foundation is highlighted as essential. This includes defining roles, responsibilities, and accountability structures for AI use, ensuring executive alignment, and embedding oversight into organizational culture. Without governance, technical controls alone are insufficient.

4. Risk Management Integration

Risk management is emphasized as an ongoing process that must be integrated into AI lifecycles. This means continuously identifying, assessing, and mitigating risks associated with data, algorithms, and models, while also accounting for evolving threats and regulatory changes.

5. Security and Technical Controls

Security forms a major part of the maturity model. It stresses the importance of secure coding, model hardening, adversarial resilience, and robust data protection. Secure development pipelines and automated monitoring of AI behavior are seen as crucial for preventing exploitation.

6. Compliance and Ethical Considerations

The assessment underscores regulatory alignment and ethical responsibilities. Organizations are expected to demonstrate compliance with applicable laws and standards while ensuring fairness, transparency, and accountability in AI outcomes. This dual lens of compliance and ethics sets the framework apart.

7. Operational Excellence

Operational maturity is measured by how well organizations integrate AI governance into day-to-day practices. This includes ongoing monitoring of deployed AI systems, clear incident response procedures for AI failures or misuse, and mechanisms for continuous improvement.

8. Maturity Levels

The framework uses levels of maturity (from ad hoc practices to fully optimized processes) to help organizations benchmark themselves. Moving up the levels involves progress from reactive, fragmented practices to proactive, standardized, and continuously improving capabilities.

9. Practical Assessment Method

The assessment is designed to be practical and repeatable. Organizations can self-assess or engage third parties to evaluate maturity against OWASP criteria. The output is a roadmap highlighting gaps, recommended improvements, and prioritized actions based on risk appetite.

10. Value for Organizations

Ultimately, the OWASP AI Maturity Assessment enables organizations to transform AI adoption from a risky endeavor into a controlled, strategic advantage. By balancing governance, security, compliance, and ethics, it gives organizations confidence in deploying AI responsibly at scale.


My Opinion

The OWASP AI Maturity Assessment stands out as a much-needed framework in today’s AI-driven world. Its strength lies in combining technical security with governance and ethics, ensuring organizations don’t just “secure AI” but also use it responsibly. The maturity levels provide clear benchmarks, making it actionable rather than purely theoretical. In my view, this framework can be a powerful tool for CISOs, compliance leaders, and AI product managers who need to align innovation with trust and accountability.

visual roadmap of the OWASP AI Maturity levels (1–5), showing the progression from ad hoc practices to fully optimized, proactive, and automated AI governance and security.

Download OWASP AI Maturity Assessment Ver 1.0 August 11, 2025

PDF of the OWASP AI Maturity Roadmap with business-value highlights for each level.

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

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Tags: OWASP AI Maturity, OWASP Security Testing


Dec 18 2023

OWASP API Security Checklist for 2023

Category: API security,owaspdisc7 @ 2:08 pm

OWASP API Security Checklist for 2023 – via Practical DevSecOps

API Security in Action

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Aug 03 2023

OWASP Top 10 for LLM (Large Language Model) applications is out!

Category: owaspdisc7 @ 12:45 pm

The OWASP Top 10 for LLM (Large Language Model) Applications version 1.0 is out, it focuses on the potential security risks when using LLMs.

OWASP released the OWASP Top 10 for LLM (Large Language Model) Applications project, which provides a list of the top 10 most critical vulnerabilities impacting LLM applications.

The project aims to educate developers, designers, architects, managers, and organizations about the security issues when deploying Large Language Models (LLMs).

The organization is committed to raising awareness of the vulnerabilities and providing recommendations for hardening LLM applications.

“The OWASP Top 10 for LLM Applications Working Group is dedicated to developing a Top 10 list of vulnerabilities specifically applicable to applications leveraging Large Language Models (LLMs).” reads the announcement of the Working Group. “This initiative aligns with the broader goals of the OWASP Foundation to foster a more secure cyberspace and is in line with the overarching intention behind all OWASP Top 10 lists.”

The organization states that the primary audience for its Top 10 is developers and security experts who design and implement LLM applications. However the project could be interest to other stakeholders in the LLM ecosystem, including scholars, legal professionals, compliance officers, and end users.

“The goal of this Working Group is to provide a foundation for developers to create applications that include LLMs, ensuring these can be used securely and safely by a wide range of entities, from individuals and companies to governments and other organizations.” continues the announcement.

The Top Ten is the result of the work of nearly 500 security specialists, AI researchers, developers, industry leaders, and academics. Over 130 of these experts actively contributed to this guide.

Clearly the project is a work in progress, LLM technology continues to evolve, and the research on security risk will need to keep pace.

Below is the Owasp Top 10 for LLM version 1.0

LLM01: Prompt Injection

This manipulates a large language model (LLM) through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources.

LLM02: Insecure Output Handling

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences like XSS, CSRF, SSRF, privilege escalation, or remote code execution.

LLM03: Training Data Poisoning

This occurs when LLM training data is tampered, introducing vulnerabilities or biases that compromise security, effectiveness, or ethical behavior. Sources include Common Crawl, WebText, OpenWebText, & books.

LLM04: Model Denial of Service

Attackers cause resource-heavy operations on LLMs, leading to service degradation or high costs. The vulnerability is magnified due to the resource-intensive nature of LLMs and unpredictability of user inputs.

LLM05: Supply Chain Vulnerabilities

LLM application lifecycle can be compromised by vulnerable components or services, leading to security attacks. Using third-party datasets, pre-trained models, and plugins can add vulnerabilities.

LLM06: Sensitive Information Disclosure

LLM’s may inadvertently reveal confidential data in its responses, leading to unauthorized data access, privacy violations, and security breaches. It’s crucial to implement data sanitization and strict user policies to mitigate this.

LLM07: Insecure Plugin Design

LLM plugins can have insecure inputs and insufficient access control. This lack of application control makes them easier to exploit and can result in consequences like remote code execution.

LLM08: Excessive Agency

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from excessive functionality, permissions, or autonomy granted to the LLM-based systems.

LLM09: Overreliance

Systems or people overly depending on LLMs without oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

LLM10: Model Theft

This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information.

The organization invites experts to join it and provide support to the project.

You can currently download version 1.0 in two formats.  The full PDF and the abridged slide format.

Web Application Security: Exploitation and Countermeasures for Modern Web Applications

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