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AI Security Standards & Regulations

Global frameworks and legal requirements for AI system security

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Compliance Landscape

These standards form the basis for lawful and ethical AI security testing practices

NIST AI RMF

Global

Risk Management

Risk management framework for trustworthy AI

  • Governance, mapping, measurement, and management
  • Voluntary but widely adopted

Pentesting Implications:

  • Align tests with framework mappings
  • Document risk measurement approaches

EU AI Act

Europe

Regulation Enforcement: Aug 2, 2026

First comprehensive AI law - fines up to €35M

  • Risk-based classification (4 tiers)
  • High-risk AI obligations: August 2, 2026

Pentesting Implications:

  • Conformity assessments required
  • Technical documentation mandatory

ISO/IEC 23053

Global

Standard

Standard for ML engineering

  • Development and deployment processes
  • System documentation requirements

Pentesting Implications:

  • Verify development process compliance
  • Check documentation completeness

OWASP LLM Top 10

Global

Guidelines

Top LLM security risks

  • Prompt injection (#1 risk)
  • Training data poisoning

Pentesting Implications:

  • Prioritize testing for Top 10 vulnerabilities
  • Use provided mitigation guidance

NIST AI RMF Documentation Requirements

What documentation does NIST AI RMF 1.1 require for AI risk management compliance?

Govern Documentation

  • AI risk management policy
  • Roles, responsibilities & accountability matrix
  • AI ethics and transparency commitments
  • Third-party AI vendor risk policy

Map Documentation

  • AI system inventory & use case registry
  • Business context & impact assessments
  • Data lineage & model provenance records
  • Stakeholder mapping & engagement logs

Measure Documentation

  • AI risk assessment reports
  • Model testing & validation results
  • Bias evaluation & fairness metrics
  • Security penetration test findings

Manage Documentation

  • Risk treatment plans & risk register
  • Incident response & recovery procedures
  • Continuous monitoring dashboards
  • Audit trail & compliance evidence

Organizations seeking NIST AI RMF compliance should maintain these documents as living artifacts, updated throughout the AI system lifecycle. Proper documentation is also a mitigating factor under the EU AI Act (up to €35M fines).

Compliance Framework Mapping

Requirement NIST EU AI Act ISO 23053
Risk Assessment Core Required Recommended
Data Governance Core Required Required
Security Testing Core High-Risk Recommended
Documentation Core Required Required

EU AI Act: Enforcement Timeline 2026

The EU AI Act (Regulation 2024/1689) is the world's first comprehensive AI legal framework.

February 2025

Prohibited AI practices banned

August 2025

GPAI obligations live

August 2, 2026

High-risk AI obligations enforceable

August 2027

Annex I HRAI systems deadline

Key Compliance Requirements

Fines

  • Up to €35M or 7% of global turnover
  • Documented compliance = mitigating factor

High-Risk AI Systems

  • Conformity assessments required
  • Technical documentation mandatory
  • Risk management systems

Source: EU AI Act Compliance Guide 2026

AI Security Training

Explore certifications and training programs to build your AI security skills.

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Legal Considerations

  • Jurisdictional requirements
  • Data protection laws
  • Intellectual property rights
  • Liability frameworks

Related Articles

Related Resources

AH
AI Hacking Team

The AI Hacking team researches and documents AI/LLM security vulnerabilities, red teaming techniques, and defensive strategies. Our guides are based on real-world pentesting experience and continuous monitoring of the AI security landscape.

Frequently Asked Questions

What is the NIST AI Risk Management Framework (RMF)?
The NIST AI RMF is a voluntary framework that provides guidance for managing AI-related risks. It is organized around four core functions: Govern, Map, Measure, and Manage. Version 1.1 adds specific guidance for generative AI systems, including requirements for transparency testing and bias evaluation in LLM applications.
What does the EU AI Act require for AI security?
The EU AI Act mandates risk classification for AI systems (unacceptable, high, limited, minimal risk). High-risk AI systems must implement risk management, data governance, transparency, human oversight, and cybersecurity measures throughout their lifecycle. Non-compliance can result in fines of up to 7% of global annual turnover.
How do AI security standards apply to penetration testing?
AI security standards like NIST AI RMF and ISO/IEC 42001 provide frameworks for structuring AI penetration tests. They define risk assessment methodologies, testing scope requirements, documentation standards, and validation criteria. Pentesters align their testing with these standards to ensure comprehensive coverage and regulatory compliance.
What is ISO/IEC 42001 and why does it matter?
ISO/IEC 42001 is the first international standard for AI management systems. It provides requirements for establishing, implementing, maintaining, and continually improving an AI management system. Organizations seeking certification demonstrate they have robust AI governance, risk management, and security controls in place.
What documentation does NIST AI RMF 1.1 require?
NIST AI RMF 1.1 documentation requirements span four core functions: Govern (AI risk policy, roles, vendor policy), Map (system inventory, use case registry, data lineage), Measure (risk assessments, test results, bias metrics), and Manage (risk treatment plans, incident response, audit trails). These documents must be maintained as living artifacts throughout the AI system lifecycle.

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