RBAC

Prerequisite: Familiarity with the concepts introduced in Part 4 — Streaming Cdc Federated Rag. Review it first if the terminology in this part is unfamiliar. Part 5 — Enterprise Security, RBAC & Data Poisoning Defense in RAG Answer-first: RAG applications are vulnerable to indirect prompt injection and vector store poisoning, where malicious payloads embedded in uploaded documents compromise LLM safety. Enforcing defense-in-depth requires embedding cryptographically verified JWT RBAC filters directly into vector database queries while scanning incoming context chunks for adversarial text patterns. Architecting this pipeline enforces sub-50ms P99 latency guarantees, OpenTelemetry GenAI semantic conventions, and 2026 Model. ...

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. ...