DevSecOps

Prerequisite: Familiarity with the concepts introduced in Part 4 — Review Pipeline Multi Agent. Review it first if the terminology in this part is unfamiliar. Part 5 — AI Code Security: Prompt Injection & Credentials When developers generate application code using AI tools (Cursor, GitHub Copilot, Claude), LLMs frequently insert plain-text synthetic API keys or sample secrets (e.g., api_key = "sk_live_9988221100abc"). If a developer accepts the AI generation without auditing every line, live production credentials end up committed to public or private git repositories. ...

Prerequisite: Familiarity with the concepts introduced in Part 6 — Ai Observability Governance. Review it first if the terminology in this part is unfamiliar. Answer-first: AI Security Engineering replaces traditional perimeter security with a Zero-Trust Defense-in-Depth architecture. By deploying pre-retrieval AST prompt scanners, cryptographically enforced Row-Level Security (RLS), and post-generation output sanitizers, enterprise systems neutralize indirect prompt injections and data poisoning attacks with 99.4% efficacy. Architecting this pipeline enforces sub-50ms P99 latency guarantees, OpenTelemetry GenAI semantic conventions, and 2026 Model Context Protocol ttlMs cache invalidation. ...