Governance & Compliance

AI governance frameworks, legal and ethical considerations, evaluation and benchmarking methodologies, and compliance tools for responsible AI red teaming and deployment.

AI red teaming operates within an increasingly structured regulatory and ethical landscape. What began as an informal practice has evolved into a discipline with legal requirements, industry standards, and professional obligations. Understanding governance and compliance is not optional for red teamers -- it defines what you can test, how you can test it, what you must report, and how your findings feed into organizational decision-making.

The governance landscape for AI is evolving rapidly. The EU AI Act establishes risk-based regulatory requirements that mandate security testing for high-risk AI systems. The NIST AI Risk Management Framework provides voluntary but influential guidance for AI system evaluation. OWASP's Top 10 for LLM Applications has become the de facto checklist for AI application security. ISO 42001 establishes management system requirements for AI. These frameworks increasingly require or recommend the kind of adversarial testing that AI red teaming provides, making red teamers key participants in the compliance process.

The Governance Landscape

AI governance spans multiple overlapping domains, each with distinct requirements and implications for red team practice.

Regulatory frameworks establish legal requirements for AI system security and safety. The EU AI Act creates mandatory requirements for high-risk AI systems, including security testing, documentation, and human oversight. NIST's AI RMF and the more recent NIST 600-1 provide structured approaches to AI risk management. MITRE ATLAS catalogs adversarial tactics and techniques specific to AI systems, serving as a shared vocabulary for describing attacks and defenses. SOC 2 compliance is being extended to cover AI-specific controls. These frameworks increasingly converge on the need for systematic adversarial evaluation as a component of responsible AI deployment.

Legal and ethical considerations define the boundaries of responsible red teaming. Authorization and contracts must clearly specify scope, data handling, and liability. Ethical disclosure practices determine how vulnerabilities are communicated and to whom. International law creates jurisdiction-specific requirements that affect cross-border engagements. Insurance and compliance requirements protect both the red team and the client. The legal landscape is particularly dynamic in the post-Executive Order environment in the United States, where federal guidance on AI safety testing continues to evolve.

Evaluation and benchmarking provides the scientific rigor that distinguishes professional red teaming from ad hoc testing. Metrics and methodology establish how to measure the severity and prevalence of vulnerabilities. Statistical rigor ensures that findings are reproducible and conclusions are well-supported. Harness building creates the infrastructure for systematic, repeatable testing. Without rigorous evaluation, red team findings are anecdotal -- with it, they become evidence that drives organizational decisions.

Compliance tooling operationalizes governance requirements. Risk assessment tools help organizations identify which AI systems require security evaluation and at what depth. Audit methodologies provide structured processes for evaluating compliance. Continuous compliance monitoring ensures that systems remain secure as they evolve, because a system that passes a point-in-time assessment can drift into non-compliance as models are updated, data sources change, or new features are added.

What You'll Learn in This Section

• Legal & Ethics -- Authorization and contracts for AI testing, ethics and disclosure practices, international law considerations, insurance and compliance requirements, state-level AI laws, China AI regulation, and sector-specific regulatory requirements
• Frameworks & Standards -- OWASP LLM Top 10, EU AI Act, NIST AI RMF, NIST 600-1, MITRE ATLAS, ISO 42001, SOC 2 for AI, framework mapping across standards, and the post-Executive Order regulatory landscape
• Evaluation & Benchmarking -- Metrics and methodology design, statistical rigor in AI security evaluation, and building evaluation harnesses for systematic testing
• Compliance Tools -- Risk assessment frameworks, audit methodology, and continuous compliance monitoring for AI systems

Prerequisites

This section is relevant to all AI security practitioners, but different roles will approach it differently:

• Red team operators should understand the legal and ethical boundaries before conducting any assessment
• Program managers should focus on frameworks, compliance tools, and evaluation methodology
• Legal and risk professionals will find the regulatory framework analysis and compliance tooling most relevant
• All readers benefit from having completed the Foundations section for technical context