An authorization gateway for AI agent tool calls. Session-scoped whitelists, intent-matched policies, behavioral anomaly detection, and a full forensic audit trail — all in under 1ms per request. Drop it in front of any MCP server. No agents to install.
Live adversarial scenario: QuantumBank financial services. Full case study — every request, every audit entry →
Traditional IAM was built for humans. It authenticates identities and controls which resources they can access. AI agents are different: they authenticate once and then make thousands of autonomous tool calls — each one a real action with real consequences. RBAC doesn't see individual tool calls. Nothing has been watching.
A compromised agent can export your entire customer database before a human detects anything. Traditional monitoring wasn't built for autonomous actors operating at API speed.
An agent authorized to "read customer data" can call write, export, and delete tools unless something enforces the boundary between its declared intent and its actual operations.
When something goes wrong, there's no structured log tying agent identity to tool call to arguments to decision. You're reconstructing events from scattered access logs.
SOC 2, PCI-DSS, HIPAA, and GDPR all require access controls and audit trails. AI agents bypass every control designed for human users. IBM 2024: avg breach cost $4.88M.
A transparent proxy that sits between your agents and your MCP server. Every tool call passes through 9 inspection stages. Zero changes to your existing agents or MCP server. Average overhead: under 1ms.
Distributed tracing, Prometheus metrics, and structured audit capture initialize on every request — before any enforcement decision is made.
OAuth 2.1 JWT validation, MCP protocol parsing (tool name + full arguments), and session enforcement — whitelist, time limit, call budget. No session header? 403.
Deny-by-default policy engine. Intent-keyword matching. Behavioral anomaly detection flags when actual operations contradict declared intent. Forward or block.
We deployed Sentinel in front of a financial services MCP server and ran an adversarial stress test. Data exfiltration attempt. Account manipulation. Privilege escalation. Audit destruction. Every tool call is logged. Every decision is explained. Every number is real and reproducible.
Agent risk-analyzer-7 — authorized for transaction analysis only — attempted 4 unauthorized operations. Sentinel blocked all 4 before a single byte reached the upstream server. Full audit trail, raw Prometheus metrics, and reproduction commands included.
Read the full case study — including what the demo doesn't cover →A single Rust binary. No agents to install. No sidecars. No service mesh changes. Point it at your MCP server — your tool calls are now enforced, audited, and observable.
The gap between AI agent deployment velocity and AI agent security infrastructure is the largest unaddressed risk surface in enterprise software. Every company shipping AI agents is exposed. Almost none of them know it.
Anthropic, OpenAI, Google, and Microsoft all shipped agent frameworks in 2024-2025. MCP is becoming the standard interface for AI agent tool use. Enterprise deployments are live today.
Existing IAM enforces endpoint-level access. AI agents need tool-call-level authorization: which agent, which session, which intent, which tool, which parameters. Nothing does this today.
NIST AI RMF, the EU AI Act, and state-level AI governance are converging on accountability for autonomous AI decision-making. Audit trails aren't optional for much longer.
Security infrastructure you can't read is infrastructure you can't trust. Apache 2.0. Every enforcement decision, every audit format, every policy rule is in the source code.
Open source. Apache 2.0. No vendor lock-in. No agents to install. Drop it in front of your MCP server and you have full visibility and control today.
Enterprise support and custom policy development available. Contact us.