AI governance in the request path

AI governance for the whole enterprise.

Cortega gives teams one place to observe AI use, apply policy, protect sensitive data, govern MCP tools, route models, and control cost. It runs on infrastructure you control and works across clouds, providers, and local models.

Request a conversation See how it works

Your infrastructure Policy before the call MCP governance Local models supported

Live AI traffic path Governed

AI use

Employee devices

Browsers

Servers

Cloud agents

Cortega

VerifyAuthenticate every caller

InspectDetect sensitive data, attacks

EnforceApply policy before egress

RecordEvidence mapped to controls

Destinations

Anthropic

OpenAI

Bedrock

MCP tools

09:41:22opencode requested claude-opus-4-5Allowed

09:42:03chatbot-prod sent PII to external modelRedacted

09:42:18unknown-tool attempted direct provider accessRedirected

Integrates with the stack you already run

OpenAI

Anthropic

Meta AI

Gemini

Bedrock

Azure OpenAI

Google Cloud

DeepSeek

Qwen

Ollama

LangChain

LangGraph

CrewAI

Pydantic

Okta

Entra ID

OpenTelemetry

Langfuse

AWS

Kubernetes

Docker

CapRover

PostgreSQL

Redis

Rust

OpenAI

Anthropic

Meta AI

Gemini

Bedrock

Azure OpenAI

Google Cloud

DeepSeek

Qwen

Ollama

LangChain

LangGraph

CrewAI

Pydantic

Okta

Entra ID

OpenTelemetry

Langfuse

AWS

Kubernetes

Docker

CapRover

PostgreSQL

Redis

Rust

What makes Cortega different

See AI activity. Enforce the intent behind your policy.

Governance on your infrastructure

Deploy on your infrastructure. Keep sensitive prompts, responses, tool calls, and telemetry aligned with your operating requirements.

Policy before the call

Block, redact, route, require approval, or record a decision before AI requests and tool calls proceed.

Data and tool protection

Detect sensitive data, govern MCP tools, and reduce risk from prompt injection, tool changes, and unexpected agent behavior.

Enterprise control and intelligence

Manage SSO, RBAC, audit evidence, routing, budgets, model choice, and AI usage intelligence from one platform.

Engineering note

We measured one gateway under load.

Cortega is built for the request path, so we started measuring the cost of being there. The first note covers a single gateway instance, fixed-rate load tests, the results, and the limits of what those results prove.

Read the performance statistics

Evaluation paths

Different searches. The same governance problem.

Some teams start by looking for an AI gateway. Some look for an AI firewall. Some begin with an open-source proxy and later discover the operating work around security, scale, and compliance. Cortega is built for the enterprise layer behind those searches.

AI gateway and firewall

Control AI traffic before it becomes action

For teams evaluating an AI gateway, AI firewall, MCP governance, sensitive data controls, routing, cost, and audit evidence.

Policy before the call Core and Edge coverage Enterprise controls

Open source alternative

When routing becomes governance

For teams comparing LiteLLM-style gateways with the cost of operating, scaling, securing, and proving compliance around them.

Less assembly Audit-ready posture Built for enterprise AI use

How we compare

One platform for the AI operating layer.

Capability	Cortega	SaaS AI securityStraiker, Witness, Trust3	Dev / OSS gatewaysLiteLLM, Portkey, TrueFoundry	API-mgmt incumbentsKong, Gravitee
Runs on infrastructure you control	✓	✗	✓	✓
Policy enforcement before the call proceeds	✓	~	~	~
Sensitive data detection without a separate scanner	✓	✗	✗	✗
Enterprise controls: SSO, RBAC, budgets, audit	✓	~	✗	~
Pre-execution human approval with cryptographic attribution	✓	✗	✗	✗
MCP tool governance	✓	~	~	✗
Govern many gateways from one control plane	✓	✗	✗	✓
Per-control compliance evidence (not raw logs)	✓	~	✗	~
Intelligence layer on standards-oriented telemetry	✓	~	✗	~

✓ built in · ~ partial or add-on · ✗ not offered. Based on each vendor's current public positioning.

A platform that scales

Many gateways. One control plane. One analytics plane.

Data plane

Gateways at the Edge and Core enforce policy where traffic flows: LLM calls, MCP tools, browser paths, employee AI assistants, and agents.

Control plane

One source of truth for posture, policy, identity, and budgets across every gateway, including gateways you already operate.

Analytics & intelligence plane

Governed traffic becomes an organization-wide picture of AI usage, concerns, quality, cost, and gaps between strategy and execution.

How it works

A distributed farm of AI security endpoints.

Cortega has a management backend for posture, policy, configuration, insight, and evidence. Gateways handle traffic where it already flows: endpoint, edge, network, server, or cloud. The intelligence layer sits above it all, consuming standards-oriented telemetry — including from gateways you already run.

Edge

Govern traffic close to users and devices. Browser, desktop, CLI, phone, and endpoint AI activity can be handled near the source.

Core

Govern production systems and internal services. Gateways scale horizontally and can be upgraded independently.

Use cases

Start with the operating problem.

Regulated data is often where the need becomes urgent, but the same controls cover visibility, shadow AI, budgets, quality, and MCP governance.

Data control

"We handle patient, financial, or legal data and need tighter control over AI use."

Detect, redact, block, or route sensitive data before provider calls happen, with controls that match your operating requirements.

GuardrailsCompliance

Shadow AI

"Employees are creating their own agents and IT doesn't know about them."

Discover, attribute, and govern every AI tool in use across your fleet — without per-app reconfiguration.

Shadow AI PolicyIdP Attribution

Visibility

"We have agents running everywhere and we don't know what they're all doing."

Create a governed inventory from gateway telemetry — from your gateways and ours — and ask questions in plain English.

InsightsObservability

In deployment today

Observe the request. Control the outcome.

Gateway events become OTEL-standard observability records. The management backend keeps policy, posture, evidence, and replay context in one place.

Built for teams that need AI to move from promising development demos to controlled production use.

Observability that supports enforcement.

Every governed request carries identity, model, department, user, policy result, timing, and replay context — in OTEL format. This is where audit and operations meet.

Requested model and model actually used.
Department, user, policy tag, and timestamp.
Events produced by gateways during normal AI use.

ObservabilityGoverned AI requests with identity, models, department, user, and time.

See what AI is running in your environment.

Point Cortega at your AI traffic and map every call — agents, employees, MCP tools — on your infrastructure. Tell us where AI runs today and we'll map the traffic path and controls.