On Trust and AI
A blueprint for confidence in the intelligent enterprise
AI can't guarantee its own reliability. Trust in its decisions depends on you.
AI is moving into core business processes faster than most organizations can govern it. Leaders are being asked to trust systems that behave intelligently but fail unpredictably—models making opaque decisions, tools granted silent privileges, and automated workflows drifting out of alignment long before anyone notices.
On Trust and AI explains why these failures happen, how attackers exploit the gaps, and what it takes to build AI that remains observable and controllable at enterprise scale. This is not a book about hype or distant futures—it's a field guide drawn from real operational environments where AI is already defending thousands of organizations from modern cyber threats.
For executives responsible for steering AI adoption, the message is direct: trust must be engineered. The book offers a practical path to deploy AI confidently, maintain oversight as systems evolve, and integrate automation in ways that strengthen human judgment instead of eroding it.
Read It Now
The book is being hosted by eSentire and is now available to read online.
Read Online at eSentire.comPrint Release: Coming Soon
Leverage It with AI
Ask the AI AdvisorI understand that many executives are navigating packed schedules while still needing to make informed decisions about AI governance. Rather than requiring you to read cover-to-cover, I've created an AI assistant built on the book's knowledge base. Use it to quickly apply the frameworks to your specific challenges, find relevant sections, or get answers grounded in the book's principles.
A note on trust: As the book itself emphasizes, AI outputs should be verified. I recommend treating the advisor's responses as a starting point—always check the cited passages and sections to confirm the context and nuance of the original material.
What You'll Learn
Shift Trust to Systems
Learn how to move trust off individual AI models and into observable, verifiable systems
Governance Frameworks
Practical approaches to AI governance that work in real enterprise environments
Human-AI Collaboration
Design workflows where AI enhances rather than replaces human judgment
Avoid Common Pitfalls
Recognize and prevent failure modes in AI adoption before they become costly