Shadow AI From Unsanctioned Use to Enterprise Value. Discovery, Risk Management, and Enablement for the AI-Powered Enterprise (en Inglés)
Reseña del libro "Shadow AI From Unsanctioned Use to Enterprise Value. Discovery, Risk Management, and Enablement for the AI-Powered Enterprise (en Inglés)"
Your employees are already using AI. The question is whether you know which tools, which data, and which risks. Shadow AI has outpaced every enterprise AI governance policy written in the last three years. Browser extensions, embedded copilots, and autonomous AI agents are moving regulated data outside sanctioned systems right now. Prohibition does not work. Ignorance is not defensible. Discovery, risk management, and enablement are.
This book is hands-on with Scenarios, Examples, and Prompt code. Manager and Engineer friendly...
Inside this book, readers will learn how to:
• Discover every AI tool employees are using across browsers, SaaS apps, OAuth grants, and local model runtimes using your existing telemetry.
• Build a four-inventory model covering Models, Applications, Tools and Connectors, and Data so your AI inventory reflects reality, not just procurement records.
• Score and prioritize AI risk with a structured AI risk assessment framework aligned to the NIST AI RMF Measure function and ISO 42001 Clause 6.
• Navigate the full regulatory stack spanning the EU AI Act, HIPAA, GLBA, FedRAMP, and NIST AI RMF with practical crosswalks that map each requirement to a real control.
• Map every AI policy to a technical control using vendor-neutral DLP, CASB, conditional access, egress filtering, and AI proxy patterns that enforce what the policy promises.
• Run a shadow-to-sanctioned pipeline with defined stages, SLAs, and exit criteria that converts unsanctioned tools into approved enterprise AI in 72 hours or less.
• Stand up a cross-functional RACI that assigns accountability across IT, security, legal, compliance, and HR so Shadow AI governance stops falling through organizational gaps.
• Govern autonomous AI agents with identity controls, tool-call audit trails, and kill-switch architecture built for the agentic AI era.
The cost of inaction is measurable. EU AI Act fines reach 3 percent of global annual turnover. HIPAA penalties for unsanctioned data handling climb into the millions. Data leakage through AI prompts has triggered breach notifications and regulatory inquiries. Every prompt containing protected health information or controlled unclassified information is a potential incident. Organizations without a defensible AI inventory are not ready for the next audit.
This book moves in order: discover first, measure risk, build controls, enable people. Each chapter pairs a Manager's Decision Guide with an Engineer's Playbook. The NIST AI RMF, ISO 42001, and EU AI Act appear as practical tools, not abstractions. AI governance framework concepts are rendered as decision trees, pseudocode, and RACI tables for field use. Every AI risk assessment rubric is designed to produce a decision in fifteen minutes.
Organizations that finish this program change posture. The AI maturity model in the final chapter takes teams from reactive, unable to name their tools, to anticipatory, governing autonomous systems before the risk community names the threat. Responsible AI is an operational capability built from inventory, scoring, and pipelines. The enterprises that win the next decade will have the fastest decisions and the clearest pipeline from shadow experiment to sanctioned operations.
Written for CIOs, CISOs, Chief AI Officers, chief data officers, IT managers, security engineers, AI governance leads, compliance officers, and federal and healthcare leaders. Whether your generative AI governance program is underway or you are still mapping what employees are using, this book meets you where you are. As autonomous AI agents become enterprise standard, the discipline built here is the foundation every organization will need.
