Sponsored by AI-RMF® LLC
Security of AI™ Framework Description:
The Security of AI™ Framework is organized into five connected layers that move from philosophy to operational outcome. Together, these layers show how organizations can govern, secure, assess, and continuously improve AI systems throughout their lifecycle.
- The Apex — Philosophy
The top layer establishes the overarching Security of AI™ philosophy: building and operating AI systems that are trustworthy, resilient, and responsible. It provides the strategic foundation for the entire framework. - The Three Pillars — Convergence
The second layer brings together the three core pillars:
- AI Governance defines what must be protected and why.
- AI Security defines how the system is defended.
- AI Assurance provides the evidence needed to demonstrate that the system is operating as intended.
- These pillars are designed to work together rather than as separate disciplines.
- The Core — Operational Engine
At the center is the AI Risk Management Framework (AI-RMF), which serves as the operational engine of the model. Through the functions of Map, Measure, and Manage, organizations identify AI risks, assess and prioritize those risks, apply controls, and monitor risk across the AI lifecycle. - Supporting Infrastructure — What Supports AI-RMF
The fourth layer provides the organizational, technical, and verification capabilities needed to make AI risk management effective in practice. These include:
- AI impact assessments
- AI inventory and classification
- Policy and compliance management
- Adversarial machine learning threat analysis
- Data provenance and integrity
- AI Bills of Materials
- Red-team testing
- Explainability tools
- Continuous monitoring
- This layer provides the practical mechanisms that support governance, security, and assurance activities.
- The Outcome — The Flow
The final layer shows how the framework produces its intended result. Governance provides the mandate, Security provides the defense, and Assurance provides the evidence and confidence. Together, these elements lead to the desired outcome: Trust and Resilience — safe, ethical, reliable, and defensible AI systems.
In this structure, Security of AI™ is not simply a cybersecurity model or compliance checklist. It is a five-layer operational philosophy that connects executive direction, governance, technical protection, assurance evidence, and risk management into a unified approach for trustworthy AI.
Why is Security of AI™ Important?
Security of AI™ Philosophy
This is the overarching umbrella that integrates ethical, technical, and operational safeguards to ensure AI systems are trustworthy and resilient.
The Three Pillars (Convergence)
- AI Governance: The "Directive" layer. It sets the policies, ethical boundaries, and legal compliance requirements. It defines what must be protected and why.
- AI Security: The "Protective" layer. It focuses on the technical defenses (e.g., adversarial hardening, data poisoning protection, and secure model weights). It defines how to defend the system.
- AI Assurance: The "Veridical" layer. It provides the evidence, auditing, and testing (V&V) to prove that the governance and security measures are working. It defines the proof of safety.
The Operational Engine: AI-RMF
You use the Map, Measure, Manage, and Govern functions to bridge the pillars:
- Translate Governance policies into risk profiles.
- Identify technical Security controls based on those risks.
- Supply the metrics used for AI Assurance auditing.
Supporting Infrastructure
You specialize in the sub-disciplines that feed the AI-RMF:
- Organizational: AI Impact Assessments (AIIA) and Inventory/Classification.
- Technical: Adversarial ML (AML) Taxonomies, Data Provenance, and AI-BOM (Bill of Materials).
- Verification: Red-Teaming, Explainability (XAI) Tools, and Continuous Monitoring.
AI-RMF® LLC Consulting
Whether you're using, building, deploying, or acquiring artificial intelligence systems, AI-RMF® using our Security of AI™ Philosophy helps you operationalize AI-Governance.