Connecting your AI red teaming activities to established industry standards and regulatory frameworks is not just good practice; it’s essential for demonstrating due diligence, achieving compliance, and integrating security into the broader governance structure. This mapping provides a reference for aligning specific red team tests with key principles from major AI-related standards.
Use this table to translate technical findings into business-relevant compliance language, justify testing efforts to stakeholders, and ensure your assessments cover requirements mandated by regulations like the EU AI Act or frameworks like the NIST AI RMF.
| Standard / Framework | Relevant Section / Principle | AI Red Teaming Activity | Connection & Purpose |
|---|---|---|---|
| NIST AI Risk Management Framework (AI RMF 1.0) | |||
| NIST AI RMF | Robustness & Resilience (Core: MAP 1-3) | Adversarial Example Generation (Evasion) | Tests the model’s ability to maintain performance when inputs are intentionally perturbed, directly assessing its resilience against common attacks. |
| NIST AI RMF | Safety (Core: GOVERN 4-4) | Jailbreaking & Prompt Injection on Safety-Critical Systems | Validates that safety filters and alignment mechanisms cannot be bypassed, preventing the AI from generating harmful or unsafe outputs. |
| NIST AI RMF | Security & Privacy (Core: MEASURE 2-5) | Model Inversion & Membership Inference Attacks | Assesses the risk of the model leaking sensitive training data, which directly relates to privacy-enhancing controls and data governance. |
| NIST AI RMF | Explainability & Interpretability (Core: MAP 2-4) | Feature Attribution Analysis & Counterfactual Explanations | While not a direct attack, this red team activity tests whether the model’s decision-making process is scrutable, a key aspect of trustworthiness. |
| EU AI Act (High-Risk Systems) | |||
| EU AI Act | Article 15: Accuracy, robustness and cybersecurity | Data Poisoning Simulation (Backdoor Attack) | Directly tests the system’s resilience to corrupted training data and ensures it behaves as intended, even under adversarial conditions. |
| EU AI Act | Article 15: Accuracy, robustness and cybersecurity | Fuzzing of Input Interfaces and APIs | Evaluates the system’s resilience against unexpected or malicious inputs, a core requirement for cybersecurity in high-risk applications. |
| EU AI Act | Article 13: Transparency and provision of information to users | Detection of Watermarks & Provenance Markers | Verifies that mechanisms for identifying AI-generated content are effective and cannot be easily stripped, ensuring transparency for end-users. |
| MITRE ATLAS (Adversarial Threat Landscape for AI Systems) | |||
| MITRE ATLAS | ML Model Access (TA0005) | Model Stealing (Extraction) Attacks | Simulates an attacker’s attempt to steal a proprietary model by querying its public API, mapping directly to tactics for illicitly gaining model access. |
| MITRE ATLAS | Evasion (TA0003) | Physical-World Adversarial Attacks (e.g., printed patches) | Emulates techniques for deceiving models (like computer vision systems) in the physical world, a specific and critical evasion tactic. |
| OWASP Top 10 for Large Language Models | |||
| OWASP LLM Top 10 | LLM01: Prompt Injection | Indirect Prompt Injection Testing | Assesses the model’s vulnerability to manipulation through third-party data sources (e.g., web pages, documents), a primary threat vector for LLMs. |
| OWASP LLM Top 10 | LLM04: Model Denial of Service | Resource Exhaustion via Complex Queries | Tests whether specifically crafted, computationally expensive prompts can overload the model, leading to a denial of service for other users. |
| OWASP LLM Top 10 | LLM06: Sensitive Information Disclosure | Targeted Querying to Extract PII | Probes the LLM to determine if it inadvertently reveals sensitive data from its training set or user prompts, directly testing for this vulnerability. |
| ISO/IEC AI Standards | |||
| ISO/IEC 23894 | Risk Management – Robustness | Model Robustness Benchmarking (e.g., against common corruptions) | Systematically evaluates model performance against a range of data shifts and perturbations, aligning with the standard’s focus on robustness. |
| ISO/IEC 42001 | Annex A: AI system impact assessment | Red Teaming for Unintended Consequences & Dual-Use | A strategic red teaming activity that explores how the AI system could be misused or cause unforeseen harm, directly informing the impact assessment required by the standard. |
A Living Reference: The landscape of AI regulation and standardization is evolving rapidly. The mappings presented here are based on the versions of these documents available at the time of writing. As a practitioner, you must treat this as a starting point. Always consult the latest official publications and be prepared to adapt your testing methodologies as new requirements and best practices emerge.