Most AI initiatives do not fail on the technology. They fail because no one could say which risks actually mattered, or when enough had been reduced to move forward.
De-Risking AI Adoption provides a practical operating model for governing AI across predictive, generative, and agentic systems.
Pamela Gupta, cybersecurity and AI governance leader, created the AI TIPS(TM) framework in 2019, four years before NIST AI RMF. Here she shows organizations how to move beyond principles and policies to measurable controls, lifecycle gates, evidence, accountability, and defensible decisions.
Inside, you'll learn how to:
- Identify and prioritize AI-specific risks across eight pillars of trust
- Translate governance requirements into eighty actionable controls
- Apply risk-based lifecycle gates from concept through retirement
- Quantify governance posture through the AI TIPS Trust Index(TM)
- Align implementation with NIST AI RMF, ISO/IEC 42001, the EU AI Act, CSA AICM, and OWASP
- Govern generative and agentic AI, including autonomy, identity, tools, and human oversight
- Produce the evidence boards, regulators, and auditors require
Written for boards, executives, AI governance leaders, CISOs, risk professionals, and organizations deploying AI at scale, this book turns trustworthy AI from an aspiration into an operating capability.
AI governance is not the brakes. It is the steering wheel.
With forewords by Jim Reavis, Dr Paul Dongha, and Khwaja Shaik.
