Use cases
AI governance use cases.
Eight ways teams put Cortega in the path of real AI use — regulated data, shadow AI, spend, agents, MCP, and audit evidence.
Eight ways teams put Cortega in the path of real AI use — regulated data, shadow AI, spend, agents, MCP, and audit evidence.
From the Cortega console.
Patient, financial, or legal data ends up in prompts. Cortega detects it and blocks, redacts, or reroutes before the request leaves the approved path.
Sensitive data →Employees adopt AI tools faster than IT can approve them. Cortega redirects unmanaged AI traffic into governed paths using controls you already run.
Shadow AI →AI activity is spread across teams with no single view. Cortega builds a live picture from the traffic path — who called, which model, what data moved, which policy fired.
Agent security →An abstract risk debate stalls the AI program. Route one real request through the gateway and show the reviewer the policy decision and a replayable log.
Agent governance →The AI bill grows with no owner. Cortega attributes spend to the caller, app, team, model, provider, and environment, and enforces hard budget caps.
Budget management →Regulated customers ask for proof, not slideware. Cortega records identity, data categories, policy decision, approval, model, provider, and outcome per request.
Audit evidence →Leaders need plain-English answers without becoming log analysts. Cortega Insights turns governed traffic into an explorable AI usage map on OTEL-standard data.
AI Border Gateway →MCP servers are another AI surface. Cortega brings them into the same identity, policy, audit, and observability model, with 2025↔2026 protocol bridging.
Agent security →We published a performance note for one gateway instance: the setup, the results, the failure case, and what we still need to measure.
Read the performance noteFoundation is free — one gateway, standard guardrails, no time limit.
We’ll map your current AI traffic path, the controls you need, and the fastest way to try Cortega.