Regulatory Quick Reference

beginner9 min readUpdated 2026-03-15

Quick reference for AI-relevant regulations and frameworks including NIST AI RMF, ISO/IEC 42001, EU AI Act, and sector-specific requirements.

regulation compliance nist iso eu-ai-act governance cheat-sheet

Regulatory Quick Reference

A condensed reference of the major AI governance frameworks and regulations relevant to AI red teaming. Use this to quickly understand which frameworks apply, what they require, and how red teaming supports compliance.

Framework Overview

Framework	Type	Jurisdiction	Mandatory?	Primary Focus
NIST AI RMF	Framework	US (global influence)	Voluntary (but increasingly referenced)	Risk management for trustworthy AI
EU AI Act	Regulation	EU (extraterritorial effect)	Mandatory (phased implementation)	Risk-based AI regulation
ISO/IEC 42001	Standard	International	Voluntary (certifiable)	AI management system requirements
OWASP LLM Top 10	Industry guidance	Global	Voluntary	LLM-specific security risks
MITRE ATLAS	Knowledge base	Global	Voluntary	Adversary TTPs for AI/ML
EO 14110	Executive Order	US	Mandatory for federal agencies	Safe, secure, and trustworthy AI
NIST AI 100-2	Guidelines	US (global influence)	Voluntary	Adversarial ML taxonomy

NIST AI Risk Management Framework (AI 100-1)

Core Functions

Function	Purpose	Red Teaming Connection
GOVERN	Establish policies, roles, accountability, and culture for AI risk management	Red team findings inform policy decisions about acceptable risk thresholds and governance requirements
MAP	Identify and understand AI risks in context -- who is affected, what can go wrong	Red team threat modeling contributes to comprehensive risk identification and attack surface mapping
MEASURE	Assess, analyze, and quantify AI risks through testing and evaluation	Red teaming is a primary Measure function activity -- it empirically evaluates AI system vulnerabilities
MANAGE	Prioritize, treat, and monitor identified risks	Red team findings drive remediation priorities, and retesting validates that risk treatments work

Key Characteristics of Trustworthy AI

The NIST AI RMF defines seven trustworthiness characteristics. Red teaming directly assesses several:

Characteristic	Definition	Red Team Relevance
Valid and reliable	System performs as intended under expected and unexpected conditions	Testing unexpected/adversarial inputs
Safe	System does not endanger human life, health, property, or environment	Testing for harmful output generation
Secure and resilient	System resists unauthorized access and maintains function under attack	Core red teaming focus
Accountable and transparent	System operation can be understood and explained	Testing for system prompt leaking, decision explainability
Explainable and interpretable	System outputs can be understood by stakeholders	Assessing whether model decisions can be audited
Privacy-enhanced	System protects individuals' privacy	Testing for PII leakage, training data extraction
Fair (bias managed)	System does not create unjust impacts across groups	Testing for bias in model outputs across demographics

EU AI Act

Risk Classification

Risk Level	Examples	Requirements	Red Teaming Implication
Unacceptable	Social scoring, real-time biometric identification (with exceptions), manipulative AI	Prohibited	Not applicable -- system cannot be deployed
High-risk	AI in employment decisions, credit scoring, law enforcement, critical infrastructure, education	Mandatory risk management, testing, documentation, human oversight, transparency	Red teaming is essential for demonstrating compliance with robustness and cybersecurity requirements
Limited risk	Chatbots, deepfake generators	Transparency obligations (disclose AI interaction)	Red teaming recommended but not mandatory
Minimal risk	Spam filters, AI in games	No specific requirements	Red teaming optional

General-Purpose AI (GPAI) Requirements

GPAI Category	Requirements	Red Teaming Relevance
All GPAI models	Technical documentation, training data transparency, copyright compliance	Documentation of model capabilities and limitations
GPAI with systemic risk (above compute threshold)	Model evaluation, adversarial testing, incident reporting, cybersecurity measures	Mandatory adversarial testing -- red teaming is explicitly required

Key Timelines

Date	Milestone
August 2024	AI Act entered into force
February 2025	Prohibitions on unacceptable-risk AI apply
August 2025	GPAI provisions apply
August 2026	High-risk AI system requirements apply
August 2027	Some high-risk categories (Annex I) fully apply

Compliance Documentation

High-risk AI systems require:

Risk management system documentation
Data governance measures
Technical documentation of the system
Record-keeping and logging
Transparency and user information
Human oversight measures
Accuracy, robustness, and cybersecurity documentation
Conformity assessment results

ISO/IEC 42001 (AI Management System)

Structure

ISO/IEC 42001 follows the Annex SL management system structure (similar to ISO 27001):

Clause	Topic	Key Requirements
4	Context	Understand the organization, stakeholders, and scope of the AIMS
5	Leadership	Management commitment, AI policy, roles and responsibilities
6	Planning	Risk assessment, AI-specific risk identification, objectives
7	Support	Resources, competence, awareness, communication, documentation
8	Operation	Operational planning, AI system lifecycle management, third-party considerations
9	Performance evaluation	Monitoring, measurement, internal audit, management review
10	Improvement	Nonconformity, corrective action, continual improvement

Annex A Controls (Selected)

Control Area	Examples of Controls	Red Teaming Connection
AI policies	AI impact assessment policy, responsible AI policy	Red team scope should align with organizational AI policy
AI system lifecycle	Development process, verification and validation	Red teaming as validation activity
Data management	Data quality, data provenance, bias management	Testing for training data poisoning, data extraction
AI system monitoring	Performance monitoring, anomaly detection	Validating monitoring effectiveness through testing
Third-party management	Supplier assessment, contract requirements	Assessing supply chain risks in AI components

ISO 42001 vs. ISO 27001

Aspect	ISO 27001	ISO 42001
Scope	Information security	AI management
Risk focus	Confidentiality, integrity, availability	AI-specific risks (bias, safety, transparency, security)
Controls	Annex A information security controls	Annex A AI-specific controls + Annex B guidance
Relationship	Foundational for IT security	Builds on 27001; references it for security controls
Certification	Widely established	Newer; growing adoption

Executive Order 14110 (US)

Key Requirements

Area	Requirement	Agency
Safety testing	Developers of powerful AI must share safety test results with the government	Commerce (NIST)
Red teaming	NIST to develop guidelines for AI red teaming	NIST
Standards	Develop standards for AI safety and security	NIST
Watermarking	Develop standards for AI content authentication and watermarking	Commerce
Privacy	Evaluate AI's impact on privacy	Various
Equity	Address AI's potential to exacerbate discrimination	Various

NIST AI 100-2 (Adversarial ML)

Companion document providing taxonomy and terminology for adversarial ML:

Attack Category	Definition
Evasion	Manipulating inputs at inference time to cause misclassification
Poisoning	Manipulating training data to compromise model behavior
Privacy	Extracting information about training data or model internals
Abuse	Using AI systems for unintended, harmful purposes

Sector-Specific Regulations

Sector	Regulation/Framework	AI-Specific Implications
Healthcare	HIPAA, FDA SaMD guidance	PHI protection in AI pipelines, medical device classification for diagnostic AI, adverse event reporting
Finance	SR 11-7 (Model Risk Management), Fair Lending, SEC/FINRA	Model validation requirements, explainability for credit decisions, algorithmic trading oversight
Critical Infrastructure	CISA guidelines, sector-specific standards	Resilience requirements for AI in energy/water/transport, incident reporting obligations
Government	EO 14110, OMB guidance, FedRAMP	Safety testing requirements, authorized AI use cases, procurement standards
Education	FERPA, state-level AI in education laws	Student data protection in AI tutoring, bias in grading AI
Employment	NYC Local Law 144, EEOC guidance	Bias auditing for AI hiring tools, notice and disclosure requirements

Red Teaming and Compliance Mapping

How red teaming activities map to compliance requirements:

Red Teaming Activity	NIST AI RMF	EU AI Act	ISO 42001
Threat modeling	MAP function	Risk management system (Art. 9)	Clause 6 (Planning)
Vulnerability testing	MEASURE function	Robustness testing (Art. 15)	Clause 8 (Operation)
Bias testing	MEASURE function	Non-discrimination (Art. 10)	Annex A (Data management)
Penetration testing	MEASURE function	Cybersecurity (Art. 15)	Annex A + ISO 27001
Report writing	MANAGE function	Technical documentation (Art. 11)	Clause 7 (Documentation)
Remediation verification	MANAGE function	Conformity assessment (Art. 43)	Clause 9 (Performance evaluation)
Continuous monitoring	MANAGE function	Post-market monitoring (Art. 72)	Clause 9 (Monitoring)

Quick Reference: When Is Red Teaming Required?

Scenario	Required?	Framework/Regulation
High-risk AI system in EU	Effectively yes	EU AI Act Art. 9, 15
GPAI with systemic risk	Explicitly yes	EU AI Act Art. 55
US federal AI deployment	Strongly recommended	EO 14110, NIST AI RMF
ISO 42001 certification	Part of validation	ISO 42001 Clause 8
Financial services AI (US)	Required by guidance	SR 11-7, OCC guidance
Healthcare AI (FDA-regulated)	Required for SaMD	FDA pre-market requirements
Any AI handling PII	Strongly recommended	GDPR Art. 25, 35 (DPIA)

Regulatory Quick Reference

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