AI Security: From Risk to Runtime Control: Governing Generative AI Through Accountability and Control

Artificial intelligence is already making decisions inside your organization.

Generative AI systems, large language models (LLMs), AI copilots, autonomous agents, and enterprise AI workflows are influencing security, compliance, operations, and business risk every single day.

But when AI fails – through hallucinations, prompt injection, data leakage, model abuse, compliance violations, or security incidents – who is accountable?

Most organizations cannot answer that question.

And regulators soon will demand one.

AI Security: From Risk to Runtime Control is a practical, executive-level guide for securing AI systems, managing enterprise AI risk, and building operational AI governance that works in the real world – not just on paper.

Written for CISOs, security leaders, risk managers, compliance professionals, security architects, and technology executives, this book explains how to move beyond theoretical AI governance into measurable AI security operations and runtime control.

In This Book, You Will Learn:
How AI systems fail – and how attackers exploit those weaknesses
The most dangerous AI attack vectors, including prompt injection, model manipulation, and agentic AI abuse
How to secure generative AI and large language model (LLM) environments
Where AI cybersecurity controls must exist across the AI lifecycle
How to establish real AI accountability and ownership structures
How to implement Zero Trust AI and runtime enforcement strategies
How to operationalize AI governance across security, compliance, and business teams
How to build an enterprise AI security program aligned with organizational risk
How to prepare for AI incident response, audits, and regulatory investigations
How frameworks such as NIST AI RMF, ISO 42001, and the EU AI Act impact your organization
A practical 90-day roadmap to launch an AI governance framework and AI control plane
Key Topics Covered:

AI security, artificial intelligence security, generative AI security, LLM security, AI governance, AI cybersecurity, AI risk management, AI accountability, AI compliance, Zero Trust AI, enterprise AI risk, AI runtime enforcement, AI maturity models, AI control planes, AI incident response, AI governance frameworks, agentic AI security, prompt injection attacks, AI failure modes, AI security operations, ZT-GAI-CSMM methodologies.

Whether your organization is adopting generative AI, deploying internal LLM systems, or preparing for evolving AI regulations, this book provides actionable strategies to reduce operational risk, strengthen AI governance, and improve enterprise AI security posture.

No prior knowledge of AI governance required.

Perfect For:
CISOs and cybersecurity leaders
Security architects and AI security teams
Enterprise risk managers
Compliance and governance professionals
Technology executives
Organizations adopting generative AI and LLM-based systems
Security professionals preparing for AI regulation and compliance audits

Your AI systems are already making decisions.

The real question is:

When something goes wrong, can you prove your organization was governing AI responsibly?

If the answer is no, this book is where to start.