SECURITY IN ARTIFICIAL INTELLIGENCE: A Practical 7-Pillar Framework for Financial Institutions

Artificial Intelligence has transformed, in just a few years, from a technological promise to critical infrastructure. In the financial sector, this transformation is even more accelerated: AI models today grant credit, detect fraud in real time, serve millions of customers and execute autonomous transactions in fractions of a second.With massive adoption came an inconvenient reality: traditional information security frameworks were not designed to protect systems that learn, evolve and act. Prompt injection, model poisoning, Shadow AI, compromised non-human identities - these are threats that simply didn't exist five years ago and for which most organizations still have no structured response.This book was born out of a practical need: how to build, implement, and measure an AI security program that is both rigorous enough to satisfy regulators and auditors, and practical enough to be operationalized by real teams with limited resources.The answer I propose is a 7-pillar framework with 45 measurable controls, built on the NIST AI Risk Management Framework and enriched with the OWASP Top 10 for LLM, MITER ATLAS and best market practices. It is not a theoretical framework - each control has a KPI, goal, measurement frequency, recommended tools, implementation steps and common pitfalls to avoid.The book is written for CISOs, risk managers, security teams, compliance, audit, and technology leaders. Does not assume prior knowledge of AI - Chapter 1 presents the fundamentals. But it's not limited to the conceptual - the controls detailing chapters are implementation guides that can be delivered directly to operational teams.May this material contribute to making the adoption of AI in the financial sector not only innovative, but safe, auditable and worthy of the trust of the millions of people whose financial lives depend on these systems.