OWASP AI Exchange: The World's Definitive AI Security Guide

Last quarter, a healthcare AI startup spent $340K on three different security consultants—each giving conflicting advice on how to secure their diagnostic AI model. One recommended NIST AI RMF. Another pushed ISO 42001. The third cited EU AI Act requirements. None agreed on implementation. The CISO's frustration? "I just need ONE authoritative resource that tells me what threats exist, what controls to implement, and how it maps to the standards I actually need to comply with." That resource exists. It's called the OWASP AI Exchange, and it's the reason ISO/IEC, the EU AI Act, and leading enterprises now have a unified framework for AI security. Here's why it matters and how to use it.

What Is the OWASP AI Exchange?

The OWASP AI Exchange is the world's most comprehensive open-source framework for AI security and privacy, providing:

• 300+ pages of practical guidance on threats, controls, and best practices
• 70+ expert contributors (researchers, practitioners, vendors, data scientists)
• Official standards contribution to ISO/IEC 27090 (AI security), ISO/IEC 27091 (AI privacy), and the EU AI Act
• Zero cost, zero attribution - CC0 1.0 license (use any part freely)

In simple terms: The AI Exchange is the go-to bookmark for anyone securing AI systems—from large medical device manufacturers to small travel agencies using chatbots.

Why This Matters

The Problem: The AI security landscape is fractured. Organizations face:

• Fragmented guidance: NIST AI RMF, MITRE ATLAS, ISO 42001, EU AI Act, OWASP Top 10 for LLMs—each covers different aspects unevenly
• Quality issues: Many resources are surface-level, outdated, or written by non-practitioners
• Inconsistency: Different frameworks use different terminology for the same concepts
• Incompleteness: Most resources focus on specific AI types (e.g., generative AI) while ignoring analytical/predictive AI

The Solution: OWASP AI Exchange provides:

• Comprehensive coverage: ALL AI types (analytical, discriminative, generative, heuristic systems)
• Authoritative consensus: Through active contribution to ISO/IEC and EU AI Act, the Exchange represents global agreement
• Practitioner-focused: Written by security professionals, for security professionals
• Living document: Continuous updates via open-source collaboration

Who Created the OWASP AI Exchange?

Founder: Rob van der Veer

The AI Exchange was founded in 2022 by Rob van der Veer, who brings:

• 33 years of AI & security experience
• Chief AI Officer at Software Improvement Group
• ISO/IEC standards leadership:
• Lead author of ISO/IEC 5338 (AI lifecycle)
• Current work on ISO/IEC 27090 (AI security) and ISO/IEC 27091 (AI privacy)
• Elected co-editor by EU member states for CEN/CENELEC (EU AI Act standards)
• Founding father of OpenCRE (Common Requirement Enumeration for security)

The Project Timeline:

• October 2022: Launched as "AI Security and Privacy Guide"
• October 2023: Rebranded as "AI Exchange" to emphasize global collaboration
• March 2025: Awarded OWASP Flagship status (highest tier) alongside the GenAI Security Project

What Makes the AI Exchange Authoritative?

1. Direct Contribution to International Standards

The AI Exchange isn't just another blog or whitepaper—it's actively shaping global standards:

What this means for you: When you implement AI Exchange guidance, you're pre-compliance with emerging ISO and EU AI Act requirements.

2. Trusted by Industry Giants

Dimitri van Zantvliet, Director Cybersecurity, Dutch Railways:

"A risk-based, context-aware approach—like the one OWASP Exchange champions—not only supports the responsible use of AI, but ensures that real threats are mitigated without burdening engineers with irrelevant checklists. We need standards written by those who build and defend these systems every day."

Sri Manda, Chief Security & Trust Officer, Peloton Interactive:

"AI regulation is critical for protecting safety and security, and for creating a level playing field for vendors. The challenge is to remove legal uncertainty by making standards really clear, and to avoid unnecessary requirements by building in flexible compliance. I'm very happy to see that OWASP Exchange has taken on these challenges."

Prateek Kalasannavar, Staff AI Security Engineer, Lenovo:

"At Lenovo, we're operationalizing AI product security at scale, from embedded inference on devices to large-scale cloud-hosted models. OWASP AI Exchange serves as a vital anchor for mapping evolving attack surfaces, codifying AI-specific testing methodologies, and driving community-aligned standards for AI risk mitigation."

The Seven Pillars of the AI Exchange

The AI Exchange is organized into seven comprehensive sections:

Section 0: AI Security Overview

Purpose: Foundations, threat models, and risk analysis framework

Key Content:

• How to use the AI Exchange (decision trees, quick-start guides)
• AI security essentials (what's different about AI security?)
• Threat model overview (attack surfaces, threat actors)
• Risk analysis methodology (10-step process)

Who needs this: Everyone (start here)

Section 1: General Controls

Purpose: Foundational security controls applicable to all AI systems

Key Content:

• AI governance frameworks
• Role-based access control (RBAC) for ML systems
• Secure AI development lifecycle
• AI security training programs
• Supply chain security for AI components

Who needs this: CISOs, security architects, AI governance teams

Section 2: Threats Through Use

Purpose: Attacks that exploit AI systems during runtime through user interaction

Key Content:

• Evasion attacks: Adversarial inputs designed to fool models
• Prompt injection: Manipulating LLM behavior via crafted prompts
• Jailbreaks: Bypassing safety guardrails
• Model inversion: Extracting training data through queries

Example:

Threat: Prompt Injection
Description: Attacker sends malicious prompt to override system instructions
Impact: Unauthorized actions, data leakage, brand damage
Controls: Input validation, prompt guardrails, output filtering

Who needs this: Application security teams, AI product owners

Section 3: Development-Time Threats

Purpose: Attacks targeting the AI development and training pipeline

Key Content:

• Data poisoning: Malicious data injected into training sets
• Model backdoors: Hidden behaviors triggered by specific inputs
• Supply chain attacks: Compromised ML libraries or pre-trained models
• Insider threats: Malicious data scientists or engineers

Example:

Threat: Data Poisoning
Description: Attacker injects manipulated samples into training data
Impact: Model learns malicious behavior, backdoors, bias
Controls: Data provenance tracking, anomaly detection, secure data pipelines

Who needs this: ML engineers, data scientists, MLOps teams

Section 4: Runtime Security Threats

Purpose: Operational security risks when AI systems are in production

Key Content:

• Insecure output handling: LLM outputs executed as code
• Model theft: Extracting model parameters through queries
• Denial of service: Resource exhaustion attacks
• Inference manipulation: Real-time attacks on prediction systems

Example:

Threat: Insecure Output Handling
Description: LLM-generated SQL query executed without validation
Impact: SQL injection, unauthorized data access
Controls: Output validation, parameterized queries, least privilege DB access

Who needs this: DevSecOps, site reliability engineers, incident response teams

Section 5: AI Security Testing

Purpose: Methodologies for testing AI-specific security properties

Key Content:

• Adversarial testing frameworks
• Red teaming for AI systems
• Penetration testing for ML APIs
• Automated security scanning for models
• Performance vs. security trade-offs

Example Testing Checklist:

• [ ] Adversarial robustness testing (FGSM, PGD attacks)
• [ ] Model extraction resistance
• [ ] Prompt injection vulnerability assessment
• [ ] Data leakage testing
• [ ] Model behavior under distribution shift

Who needs this: Security testers, red teamers, QA teams

Section 6: AI Privacy

Purpose: Privacy-specific threats and controls for AI systems

Key Content:

• Membership inference: Determining if specific data was in training set
• Attribute inference: Extracting sensitive attributes from model
• Differential privacy: Formal privacy guarantees
• Federated learning: Privacy-preserving distributed training
• PII handling: Detecting and protecting personal data

Example:

Threat: Membership Inference
Description: Attacker determines if specific individual's data was used for training
Impact: Privacy breach, GDPR violation
Controls: Differential privacy, membership inference defenses, data minimization

Who needs this: Privacy officers, data protection teams, compliance

Section 7: References

Purpose: Links to standards, frameworks, research, and tools

Key Content:

• Mapping to MITRE ATLAS
• Alignment with NIST AI RMF
• ISO/IEC standards cross-reference
• Research papers and case studies
• Open-source security tools

Who needs this: Researchers, policy makers, standards contributors

How to Use the AI Exchange: Practical Workflow

Step 1: Identify Your Use Case

Start with the decision tree in Section 0:

Are you building or deploying AI?
├── Building (development) → Focus on Section 3 (Development-time threats)
└── Deploying (production) → Focus on Sections 2, 4 (Use/Runtime threats)

What type of AI?
├── Generative (LLMs, image generation) → Emphasize prompt injection, insecure output
├── Predictive (fraud detection, recommendation) → Emphasize evasion, model theft
└── Analytical (anomaly detection, clustering) → Emphasize data poisoning, inference manipulation

What's your role?
├── Security team → Start with Section 1 (General controls), then specific threats
├── Engineering team → Start with Section 5 (Testing), then development/runtime threats
└── Governance/Legal → Start with Section 0 (Overview), Section 6 (Privacy)

Step 2: Conduct Risk Analysis

Follow the 10-step risk analysis methodology (Section 0):

1. Identify risks: Use the decision tree to narrow down relevant threats
2. Evaluate risks: Estimate likelihood and impact for each threat
3. Risk treatment: Decide to mitigate, accept, transfer, or avoid
4. Risk communication: Document and share with stakeholders
5. Arrange responsibility: Assign owners for each risk
6. Verify external responsibilities: If using third-party AI, audit their controls
7. Select controls: Choose from AI Exchange control catalog
8. Residual risk acceptance: Document what risks remain after controls
9. Manage controls: Implement, test, and monitor selected controls
10. Continuous assessment: Re-evaluate as threats evolve

Step 3: Implement Controls

For each identified threat, the AI Exchange provides:

• Threat description: What the attack is and how it works
• Impact: Business and technical consequences
• Likelihood factors: What increases/decreases probability
• Controls (categorized):
• Prevention: Stop the attack before it happens
• Detection: Identify attacks in progress or post-facto
• Response: Limit damage when attacks occur

Example Implementation (Prompt Injection Defense):

GuardionAI Implementation:

from guardion import GuardionRuntime

agent = GuardionRuntime(
agent=your_llm,
guardrails=[
{"type": "prompt_injection", "action": "block"}, # Prevention
{"type": "pii_detection", "action": "tokenize"}, # Response
{"type": "anomaly_detection", "alert": True} # Detection
]
)

Step 4: Map to Compliance Frameworks

The AI Exchange explicitly maps controls to major standards:

Pre-built Compliance Templates: The AI Exchange provides ready-made checklists for:

• ISO/IEC 27090 compliance
• EU AI Act Article 9-15 (high-risk systems)
• NIST AI RMF implementation
• OWASP Top 10 for LLMs mitigation

Key Differentiators: Why AI Exchange vs. Other Resources?

OWASP AI Exchange vs. NIST AI RMF

Use together: NIST AI RMF for policy, AI Exchange for implementation.

OWASP AI Exchange vs. OWASP Top 10 for LLMs

Use together: Top 10 for awareness, AI Exchange for implementation.

OWASP AI Exchange vs. MITRE ATLAS

Use together: ATLAS for threat intelligence, AI Exchange for defenses.

Real-World Use Case: Implementing AI Exchange Guidance

Company: Mid-size healthcare SaaS (diagnostic AI for radiology)

Challenge:

• Need to certify under EU AI Act (high-risk medical device)
• Must comply with HIPAA (patient data privacy)
• Limited security budget and expertise

Approach Using AI Exchange:

Week 1: Risk Analysis

1. Use Section 0 decision tree → identified as "High-Risk AI" per EU AI Act
2. Relevant threats (from Sections 2-4):
- Evasion attacks (manipulated X-ray images)
- Model inversion (extracting patient data)
- Data poisoning (malicious training samples)
- Insecure output (incorrect diagnoses)

Week 2-3: Control Selection

From AI Exchange control catalog:
- Section 2 (Evasion): Adversarial training, input validation
- Section 3 (Data poisoning): Data provenance tracking, anomaly detection
- Section 4 (Insecure output): Human-in-the-loop for high-stakes decisions
- Section 6 (Privacy): Differential privacy, PII minimization

Week 4-6: Implementation

Priority 1 (EU AI Act Article 9 - Risk Management):
- Documented risk assessment (AI Exchange Section 0 template)
- Mitigation controls mapped to identified threats

Priority 2 (EU AI Act Article 10 - Data Governance):
- Data provenance system for all training data
- Data quality checks (completeness, bias audits)

Priority 3 (HIPAA):
- Differential privacy for model training
- PII tokenization for inference logs

Result:

• Certification timeline: 6 months (vs. 12-18 months industry average)
• Cost: $120K (vs. $340K average with multiple consultants)
• Coverage: EU AI Act + HIPAA compliant using single authoritative source

Limitations and What the AI Exchange Doesn't Cover

1. Implementation Details Are Your Responsibility

What AI Exchange provides:

• "Use differential privacy for training data"

What you still need:

• Specific ε (epsilon) value for your privacy budget
• Library choice (TensorFlow Privacy vs. Opacus)
• Performance trade-offs for your use case

Solution: AI Exchange points to tools and research, but you need domain expertise.

2. Not a Substitute for Security Engineering

The Exchange is:

• A framework and knowledge base
• A checklist of threats and controls
• A mapping to standards

The Exchange is not:

• A plug-and-play security solution
• A replacement for red teaming
• An automated compliance certification

Solution: Use AI Exchange to guide decisions, not replace security professionals.

3. Emerging Threats May Not Be Covered Yet

Challenge: AI security evolves faster than any documentation.

Example: Agentic AI threats (tool call authorization, multi-turn attacks) are newer than many AI Exchange sections.

Solution: AI Exchange is continuously updated. Check release notes and contribute new threat patterns.

How to Contribute to the AI Exchange

The AI Exchange thrives on community contribution. Here's how to get involved:

1. Suggest Edits or Additions

GitHub: OWASP AI Exchange Repository

Process:

1. Find the relevant section page
2. Click "Edit on GitHub"
3. Propose changes via pull request
4. AI Exchange authors review and merge

2. Join the Author Group

Requirements:

• Demonstrated expertise in AI security (papers, projects, industry experience)
• Commitment to quality and consensus-building
• Screening process for quality assurance

Apply: Via OWASP Contribute Page

3. Use and Cite the AI Exchange

Help spread awareness:

• Link to AI Exchange in your security policies
• Cite in research papers and blog posts
• Reference in compliance documentation

License: CC0 1.0 (no attribution required, but appreciated)

Conclusion: Your Go-To Bookmark for AI Security

The cybersecurity industry spent years trying to figure out "how to do web security" before OWASP standardized it with the Top 10, ASVS, and Testing Guide. We're at the same inflection point for AI security—except the stakes are higher and the timeline is compressed.

The OWASP AI Exchange is that standardization moment for AI security.

Why it works:

• Authoritative: Written by experts, shapes ISO/IEC and EU AI Act
• Comprehensive: 300+ pages covering all AI types and threats
• Practical: Code examples, checklists, decision trees
• Open: Free, no copyright, continuously updated
• Unified: One coherent framework vs. fragmented landscape

The most expensive AI security mistake is reinventing frameworks that already exist.

Start here: https://owaspai.org

References and Resources

Official OWASP AI Exchange

1. Main Website: https://owaspai.org
2. GitHub Repository: https://github.com/OWASP/www-project-ai-security-and-privacy-guide
3. LinkedIn: OWASP AI Exchange LinkedIn
4. Contribute: https://owaspai.org/contribute

Related OWASP Projects

• OWASP Top 10 for LLMs: https://owasp.org/www-project-top-10-for-large-language-model-applications/
• OWASP GenAI Security Project: https://genai.owasp.org/
• OpenCRE (Security Chatbot): https://opencre.org

International Standards (Informed by AI Exchange)

• ISO/IEC 27090: AI Security (publication expected 2026)
• ISO/IEC 27091: AI Privacy (in development)
• ISO/IEC 5338: AI Lifecycle (published)
• EU AI Act Standards: CEN/CENELEC (Rob van der Veer co-editor)

Complementary Frameworks

• NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework
• MITRE ATLAS: https://atlas.mitre.org/
• NCSC/CISA Guidelines: https://www.ncsc.gov.uk/collection/guidelines-secure-ai-system-development

Ready to secure your AI systems with authoritative guidance? Start with the OWASP AI Exchange Risk Analysis or explore GuardionAI's pre-built OWASP-aligned controls.