A complete security framework for AI systems
Structured controls, risk management processes, and compliance mapping across NIST AI RMF, OWASP LLM Top 10, and ISO 42001 — giving your AI security programme a defensible, auditable foundation.
What is AI Security Framework — Structured Security for AI Systems?
An AI security framework is a structured set of controls, processes, and governance practices for managing security risk across an organisation's AI systems. It integrates guidance from NIST AI RMF (risk management for AI), OWASP LLM Top 10 (LLM application security), ISO 42001 (AI management systems), and MITRE ATLAS (AI adversarial techniques) into a unified operational framework.
Why ad-hoc AI security creates gaps
- Individual teams implement AI security controls independently — creating inconsistent protection levels and compliance gaps across the AI portfolio
- No single existing framework covers all dimensions of AI security: technical controls (OWASP LLM), risk management (NIST AI RMF), governance (ISO 42001), and adversary tactics (MITRE ATLAS)
- Security teams without an AI framework cannot demonstrate systematic security to auditors, enterprise customers, or regulators
- AI security controls without a framework are not maintained — they degrade as models, data, and deployment patterns change
A four-step operational model
Framework Selection
Select the AI security frameworks relevant to your context — OWASP LLM Top 10 for application security, NIST AI RMF for risk management, ISO 42001 for governance, MITRE ATLAS for adversary coverage.
- OWASP LLM Top 10 controls
- NIST AI RMF risk functions
- MITRE ATLAS adversary coverage
Control Implementation
Implement technical security controls mapped to the selected framework requirements — input/output filtering, access controls, monitoring, audit logging.
- Technical control implementation
- Framework requirement mapping
- Control gap identification
Risk Management Process
Establish ongoing AI risk identification, analysis, treatment, and monitoring processes aligned with NIST AI RMF and ISO 42001 requirements.
- Risk identification process
- Risk treatment programme
- Continuous risk monitoring
Compliance Evidence
Maintain audit-ready evidence of framework implementation — control testing results, risk assessments, incident records, and governance documentation.
- Control testing evidence
- Risk register maintenance
- Audit-ready compliance documentation
Outcomes for security teams
Frameworks provide defensible, repeatable security
A documented AI security framework demonstrates systematic security practice to auditors, regulators, and enterprise customers — ad-hoc controls do not.
Multiple frameworks must be harmonised
NIST AI RMF, OWASP LLM Top 10, ISO 42001, and EU AI Act overlap significantly — a unified implementation approach avoids duplicate effort.
AI security requires ongoing maintenance
AI systems, models, and attack techniques evolve continuously — a framework provides the structure for systematic updates to controls and risk assessments.
Direct answers
What is an AI security framework?+
A structured set of controls, processes, and governance practices for managing security risk across AI systems — integrating NIST AI RMF, OWASP LLM Top 10, ISO 42001, and MITRE ATLAS into a unified operational programme.
How does NIST AI RMF differ from OWASP LLM Top 10?+
NIST AI RMF covers risk management processes across the full AI lifecycle; OWASP LLM Top 10 covers specific technical security vulnerabilities in LLM applications. A complete AI security framework uses both.
Is ISO 42001 the same as ISO 27001 for AI?+
ISO 42001 is an AI management system standard analogous to ISO 27001 in structure, but focused on AI governance rather than information security. Many organisations implement both, with significant control overlap.
How does Threatstealth implement the AI security framework?+
Threatstealth maps platform controls (LLM scanner, AI threat detection, AI governance tooling) to NIST AI RMF, OWASP LLM Top 10, and ISO 42001 requirements — providing continuous framework compliance monitoring.