AI Security Frameworks Overview

Landscape of AI security frameworks including OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and EU AI Act. How they relate, which to use when, and gap analysis.

No single framework covers the full spectrum of AI security risks. Each major framework addresses a different slice of the problem: vulnerability taxonomies, adversarial tactics, risk management processes, or regulatory compliance. Effective AI red teaming requires understanding how these frameworks complement each other and selecting the right combination for each engagement.

The Framework Landscape

Framework Relationships

                    ┌─────────────────────┐
                    │   Regulatory Layer   │
                    │   EU AI Act          │
                    │   US Executive Orders│
                    └────────┬────────────┘
                             │ requires
                    ┌────────▼────────────┐
                    │  Management Layer    │
                    │  NIST AI RMF        │
                    │  ISO 42001          │
                    └────────┬────────────┘
                             │ operationalized by
              ┌──────────────┼──────────────┐
              │              │              │
     ┌────────▼───┐  ┌──────▼──────┐ ┌─────▼──────┐
     │ Vulnerability│  │  Adversarial │ │  Testing   │
     │ Taxonomies  │  │  Modeling    │ │  Standards │
     │ OWASP Top 10│  │  MITRE ATLAS│ │  NIST 600-1│
     └─────────────┘  └─────────────┘ └────────────┘

Choosing the Right Framework

By Engagement Type

By Audience

Different stakeholders speak different framework languages:

Framework Coverage Analysis

Each framework has blind spots. Understanding these gaps is essential for comprehensive assessments.

What Each Framework Covers Well

Common Gaps Across Frameworks

No current framework adequately addresses:

• Agentic AI risks: Tool use exploitation, multi-step reasoning manipulation, autonomous action safety
• Multi-model architectures: Risks in systems that chain multiple AI models together
• Emergent capabilities: Security implications of capabilities that emerge at scale
• Real-time adversarial adaptation: Attackers who modify techniques during an engagement based on model responses

Framework Versioning and Updates

Frameworks evolve. Track these update cycles:

Combining Frameworks in Practice

For a comprehensive AI red teaming engagement, combine frameworks at different stages:

1. 1

   Planning: MITRE ATLAS

   Use ATLAS tactics and techniques to model the threat landscape and plan attack scenarios. Map the target system's attack surface to ATLAS categories.

2. 2

   Execution: OWASP LLM Top 10

   Use the OWASP Top 10 as a testing checklist to ensure coverage of common vulnerability categories. Each OWASP item suggests specific test cases.

3. 3

   Risk Assessment: NIST AI RMF

   Frame findings within the NIST AI RMF risk categories to communicate organizational risk posture. Map vulnerabilities to the Govern, Map, Measure, Manage functions.

4. 4

   Compliance Mapping: EU AI Act

   For organizations serving EU markets, map findings to EU AI Act requirements to demonstrate compliance gaps or conformity.

5. 5

   Reporting: Cross-framework references

   Include cross-references in your report so different stakeholders can find findings in their preferred framework. See cross-framework mapping.

Related Topics

• OWASP LLM Top 10 Deep Dive -- detailed analysis of each OWASP category
• MITRE ATLAS Walkthrough -- practical use of ATLAS for engagement planning
• NIST AI RMF & ISO 42001 -- risk management framework deep dive
• Cross-Framework Mapping Reference -- unified taxonomy and quick reference tables

References

• "OWASP Top 10 for LLM Applications" - OWASP Foundation (2025) - Industry-standard vulnerability taxonomy for large language model applications
• "MITRE ATLAS (Adversarial Threat Landscape for AI Systems)" - MITRE Corporation (2024) - Knowledge base of adversarial tactics and techniques targeting AI systems
• "NIST AI Risk Management Framework (AI RMF 1.0)" - National Institute of Standards and Technology (2023) - Voluntary framework for managing risks throughout the AI lifecycle
• "ISO/IEC 42001:2023 Artificial Intelligence Management System" - International Organization for Standardization (2023) - Certifiable standard for establishing and maintaining AI management systems
• "EU Artificial Intelligence Act" - European Parliament (2024) - Comprehensive regulatory framework for AI systems including adversarial testing mandates