Sponsored by AI-RMF® LLC
Welcome to Security of AI™
Security of AI™ is a Holistic Philosophy for building and operating Trustworthy, Safe and Resilient AI Systems.
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.
SOAI - Governance, Security and Assurance
1. Policy and Compliance
2. Ethical Boundaries
3. Accountability
4. Inventory and Classification
5. AI Impact Assessments
6. Human Oversight
1. Adversarial ML Defense
2. Data Provenance and Integrity
3. Access Control and Model Protection
4. AI-BOM and Dependency Visibility
5. Runtime Monitoring
6. Incident Response and Resilience
1. Validation and Verification
2. Red Team Testing
3. Audits with Evidence
4. Explainability and Interpretability
5. Evaluation Metrics and Benchmarks
6. Continuous Monitoring
1. Prompt Injection and Jailbreaks
2. Adversarial Inputs
3. Data and Model Manipulation
4. Agent and Tool Misuse
5. Boundary Failure Discovery
6. Evidence for Mitigation
Security Of AI™ Books
Books Now Available on Amazon
1. "Security of AI: The Convergence of AI Governance, Security and Assurance
2. "AI-RMF: Operationalizing AI Risk Management Through Security of AI"
3. "AI Assurance: The Proof Pillar of Security of AI"
The goal of the "Security of AI" YouTube Channel is to raise awareness, provide educational opportunities and report of relevant SOAI News Commentary. We aim to help people, at all levels, understand the impact and importance of AI-Governance, AI-Security and AI-Assurance. We reveal life challenges and how AI systems fail in the real world—and what happens next. We produce AI security thrillers showing worst-case scenarios, breaking news analysis on live incidents and vulnerabilities, and deep-dive frameworks covering OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF. From Agentic AI threat modeling to Red Team Test techniques, we show defenders how to spot risks before they become disasters.