The "Security of AI" Book Now Available on Amazon®
Published By: AI-RMF® LLC
What is Security of AI™?
1. The Apex: Security of AI Philosophy
This is the overarching umbrella that integrates ethical, technical, and operational safeguards to ensure AI systems are trustworthy and resilient.
2. 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.
3. The Core: AI Risk Management Framework (AI-RMF):
The AI-RMF sits at the center of these pillars. It is the functional framework that maps, measures, and manages risks across the entire AI lifecycle. It functions as the bridge:
- From Governance: It takes policy objectives and turns them into risk profiles.
- To Security: It identifies which technical controls are needed based on those risks.
- To Assurance: It provides the metrics and benchmarks used for auditing.
4. Supporting Infrastructure (What Supports AI-RMF):
To function effectively, the AI-RMF relies on a foundation of specialized sub-disciplines:
- Organizational Support:
- AI Impact Assessments (AIIA): Evaluating societal and individual harm.
- Inventory & Classification: Identifying every model, dataset, and third-party API.
- Technical Support (The "Security" feed):
- Adversarial ML (AML) Taxonomies: Categorizing attacks like prompt injection and evasion.
- Data Provenance: Tracking the lineage and integrity of training data.
- Model Bill of Materials (AI-BOM): Visibility into the "ingredients" of the AI stack.
- Verification Support (The "Assurance" feed):
- Red-Teaming: Continuous stress-testing of model boundaries.
- Explainability (XAI) Tools: Ensuring the model’s "reasoning" can be audited.
- Continuous Monitoring: Real-time drift detection and anomaly reporting.
5. Summary of the Flow:
- Governance provides the Mandate.
- AI-RMF provides the Process (Map, Measure, Manage, Govern).
- Security provides the Defense.
- Assurance provides the Certainty. education, and real-world impact
Learn More
Purchase The "Security of AI" Book on Amazon®, Watch our "Security of AI" YouTube Channel, or Visit our "Security of AI" website for lots of additional information and real world Python Test Scripts. Our goal is to provide what you need to get started with your AI-Governance, AI-Security, AI-Assurance and AI-Testing journey. Our primary mission is promote Trustworthy AI.
SOAI - Governance, Security and Assurance Convergence
- Policy and Compliance
- Ethical Boundaries
- Accountability
- Inventory and Classification
- AI Impact Assessments
- Human Oversight
- Adversarial ML Defense
- Data Provenance & Integrity
- Access Control & Model Protection
- AI-BOM & Dependency Visibility
- Runtime Monitoring
- Incident Response and Resilience
- Validation and Verification
- Red Team Test
- Audit Evidence
- Explainability & Interpretability
- Evaluation Metrics & Benchmarks
- Continuous Monitoring
- Prompt Injection & Jailbreaks
- Adversarial Inputs
- Data and Model Manipulation
- Agent and Tool Misuse
- Boundary Failure Discovery
- Evidence for Mitigation
Interactive AI Risk Tool
- Links 50 most critical AI risks
- Covers Five Risk Domains
- Generates a Likelihood and Impact Score
- Generates a Risk Score
- Provides Mitigation Guidance
- Provides NIST, OWASP and MITRE ATLAS Framework Mapping