Written for CISOs, AI governance leads, and security architects building AI security programmes in regulated enterprise environments.
Chapter 01
The AI Attack Surface: What Most Organisations Don't Know They Have
The average enterprise has 4× more AI assets deployed than its security team believes. This chapter maps the full AI attack surface — LLM APIs, model weights, training pipelines, vector databases, embedding services, and shadow AI deployments — and explains why traditional scanner approaches miss most of them.
Chapter 02
16-Pillar AI Security Architecture
AI-Interceptor's 16-pillar security architecture covers every layer from model supply chain integrity to runtime inference monitoring. This chapter explains each pillar, the threat it addresses, and the control-plane checks that provide assurance — with a mapping to OWASP LLM Top 10 and MITRE ATLAS.
Chapter 03
The 8-Phase Scanning Pipeline Explained
A step-by-step breakdown of AI-Interceptor's scanning methodology: discovery → inventory → static analysis → dynamic probing → red team scenarios → compliance mapping → risk scoring → remediation handoff. Includes the safety gates between phases that prevent scanning from becoming an attack vector in itself.
Chapter 04
9-Framework Compliance Mapping: OWASP LLM to EU AI Act
How AI-Interceptor maps every finding to nine compliance frameworks simultaneously: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001, EU AI Act, SEBI CSCRF, RBI Cybersecurity Framework, DPDPA, and SOC 2. Includes a cross-framework control matrix with 152 mapped controls.
Chapter 05
Red Team Methodology with Safety Gates
Enterprise AI red teaming is not the same as traditional penetration testing. This chapter details the adversarial prompt library, model extraction resistance tests, data exfiltration scenarios, and the three-tier safety model that ensures red team activity is staged, authorised, and contained — with full audit trails.
Chapter 06
Building an AI Security Programme: 90-Day Roadmap
A practical 90-day playbook for standing up an AI security function from scratch: Day 1–30 (discovery and inventory), Day 31–60 (risk assessment and control gap analysis), Day 61–90 (programme operations, SLA definition, and board reporting). Includes a staffing model and toolchain architecture.