AI Security Standards & Regulations
Global frameworks and legal requirements for AI system security
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 |
AI Security Training
Explore certifications and training programs to build your AI security skills.
View Certifications →Legal Considerations
- Jurisdictional requirements
- Data protection laws
- Intellectual property rights
- Liability frameworks