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
GlobalRisk 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
EuropeRegulation Enforcement: 2026
First comprehensive AI law
- Risk-based classification system
- Strict requirements for high-risk AI
Pentesting Implications:
- High-risk systems require conformity assessments
- Transparency documentation requirements
ISO/IEC 23053
GlobalStandard
Standard for ML engineering
- Development and deployment processes
- System documentation requirements
Pentesting Implications:
- Verify development process compliance
- Check documentation completeness
OWASP LLM Top 10
GlobalGuidelines
Top LLM security risks
- Prompt injection (#1 risk)
- Training data poisoning
Pentesting Implications:
- Prioritize testing for Top 10 vulnerabilities
- Use provided mitigation guidance
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 |
Documentation Templates
Legal Considerations
- Jurisdictional requirements
- Data protection laws
- Intellectual property rights
- Liability frameworks