ABC's of AI Security and Governance

AI is entering a new operational phase. We are moving from systems that answer questions to systems that plan, reason, use tools, access data, and execute actions across time. When AI becomes agentic, the highest-risk moment is no longer what the model said—it is what the system did: what it accessed, what it sent, what it changed, and under whose authority.

Most security and governance programs were built for deterministic software and bounded human users. Agentic systems are neither. They can retrieve from internal knowledge bases, invoke APIs, chain actions across systems, persist memory, and influence consequential workflows such as access grants, financial approvals, production changes, hiring decisions, and customer communications. That combination creates a new control problem—one that traditional risk models do not fully address.

This book defines and operationalizes that discipline as Agentic AI Control & Governance (AACG): bounding autonomous behavior at runtime through enforceable policy, decision rights, observability, containment, and audit-ready evidence.

Structured A–Z, each chapter addresses a critical domain—from adversarial attacks, bias, confidentiality, and data poisoning to human oversight, model risk management, secure AI SDLC, threat modeling, vendor risk, zero trust, and more—using a consistent, executive-ready framework.

Each chapter includes leader decisions that cannot be outsourced, practical control playbooks, key risk indicators, executive checklists, and audit-ready evidence templates so programs can withstand customer scrutiny, internal audit, and regulatory review.

At the core of the book is a simple but non-negotiable principle: model output is not authority—authority is granted by policy at execution time.