SentinelMind – Autonomous Threat Guardian for Agentic AI Systems
GitHubZero-trust security layer and real-time monitoring gateway built for multi-agent AI ecosystems.
Overview
SentinelMind acts as a reverse proxy and inspection engine specifically designed for AI agents. It intercepts LLM-generated API calls before they reach execution environments, blocking adversarial instructions.
The Problem
As AI agents transition to taking autonomous, real-world actions, they become vulnerable to adversarial command hijacking, privilege escalation, and data exfiltration from compromised internal components.
System Architecture
Inline Go-based proxy that inspects JSON RPC payloads. The proxy forwards telemetry asynchronously to a Next.js dashboard via WebSockets.
Engineering Trade-offs
Used deterministic keyword and regex heuristics rather than a secondary LLM for inspection to guarantee sub-12ms latency, sacrificing some contextual understanding for raw speed.
Lessons Learned & Future
Production Learnings
Validating non-deterministic JSON outputs from LLMs requires extremely fault-tolerant parsing logic.
Future Improvements
Adding a lightweight ML model (like a quantized BERT) for semantic anomaly detection that runs on CPU.
Database Layer
Redis for high-speed rate limiting and sliding-window anomaly detection. PostgreSQL for permanent audit logs.
API Design
Standard reverse proxy architecture. The management dashboard uses a GraphQL API for flexible querying of complex threat metrics.
Security Decisions
1. Fail-closed architecture: if the inspection engine crashes, all agent actions are blocked. 2. Keyword heuristic engines run entirely in-memory to prevent injection attacks on the inspection layer itself.
Scale & Metrics
Maintains <12ms p95 latency for payload inspection, capable of handling 5,000 requests per second per node.