Crosswalk mapping NIST AI RMF Generative AI Profile functions to LLM firewall capabilities, showing how compliance officers can operationalize AI governance with technical controls.
Practical implementation guide for security teams to configure prompt redaction, response inspection, egress validation, and exfiltration scoring to prevent data leaks in LLM workflows.
Full adversarial AI threat taxonomy covering eight attack categories beyond prompt injection, with defense-in-depth controls mapped to five inspection layers.
Why traditional DLP cannot cover LLM traffic, the six fundamental differences between DLP and LLM firewalls, and why enterprises need both for full data protection.
Why traditional SIEM tools cannot inspect AI conversation content, the six specific blind spots in LLM traffic monitoring, and how an inline firewall fills the gap.
Analysis of the five major threats targeting AI coding agents in 2026, from Agentjacking to GhostApproval, and the firewall controls needed to secure developer AI workflows.
Step-by-step architecture guide for securing enterprise LLM deployments with an inline firewall, covering traffic mapping, inspection points, policy design, and phased rollout.
Mapping each OWASP Top 10 for LLM Applications risk to concrete LLM firewall controls, with coverage levels and real 2026 incident examples.
Five real exfiltration patterns from 2026 incidents showing how attackers move data through LLMs, and the firewall controls needed to stop each one.
Technical guide to detecting and defending against direct and indirect prompt injection attacks, with real 2026 incident analysis and firewall detection requirements.
