Cortega AI Governance Platform
Summary
The AI request your employee just sent to Claude contained last quarter's unreleased financials — and you found out three weeks later in a Slack message, not a log. Cortega sits in the request path before that call leaves your network, inspecting and enforcing policy before sensitive data reaches any external provider.
Cortega deploys as a gateway layer on infrastructure you control, intercepting every LLM call — from browsers, agents, servers, and cloud runners — and running identity verification, data inspection, and policy enforcement before the request egresses. The control plane manages policy, identity, routing, and budget caps across every gateway; the analytics plane turns that governed traffic into an org-wide picture of who is calling what model, at what cost, with what data categories. Where it earns its place is regulated environments: audit trails record identity, data category, policy decision, approval, model, provider, and outcome — the evidence reviewers actually ask for, not a raw log dump. The ceiling appears when your governance requirements demand deep application-layer logic that lives outside the gateway path, or when your teams are still in early AI experimentation and the architecture review overhead exceeds the compliance exposure you are trying to close.
Bottom line: Deploy this when you have regulated data moving toward external AI providers and need enforcement before egress — not after; skip it if your org's primary problem is building AI features rather than governing the ones already running.
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Pros
Sign in to edit- Pre-egress policy enforcement — detection, redaction, blocking, and rerouting happen before the LLM call leaves your network, so a misconfigured agent sending PII to an external model gets caught at the gateway rather than in a post-incident review.
- Self-hosted deployment on infrastructure you control, which means data residency and contractual requirements that bar third-party intermediaries do not force a governance gap.
- Hard budget caps enforced per team, model, and environment at the request level, so AI spend attribution does not require waiting for a provider invoice to discover which agent ran over budget.
- Structured audit records that map identity, data category, policy decision, approval, model, provider, and outcome to compliance controls — so evidence packages for regulated customer audits are assembled from live operational data rather than reconstructed from logs.
- Shadow AI discovery via gateway telemetry without per-app reconfiguration, which means IT gets a governed inventory of every AI tool in fleet use including custom agents employees built and never disclosed.
Cons
Sign in to edit- The architecture review and technical briefing gate the entire onboarding process — teams that need to evaluate governance tooling against a sprint deadline cannot self-serve a proof of concept, and the sales cycle adds weeks before a single request flows through the gateway.
- Governance coverage is limited to traffic that flows through the gateway. Agents or integrations that maintain direct provider connections — either by design or because discovery is incomplete — sit outside enforcement until they are redirected, which means the shadow AI problem Cortega solves is also the precondition that creates blind spots during rollout.
- Teams whose primary compliance requirement is application-level policy logic tied to business rules — not data inspection or spend control — will find that the gateway model enforces well at the transport layer but does not replace custom policy middleware inside the application. Those teams typically maintain Cortega for perimeter controls and a separate policy layer inside the app, running two enforcement surfaces.
- No open-source path and no self-serve tier means teams that hit a pricing or contractual ceiling have no community edition to fall back on. Organizations that outgrow the commercial terms or need to internalize the gateway capability entirely will rebuild on open-source LLM proxy tooling rather than fork Cortega.
About
- Platforms
- Self-hosted on customer infrastructure; supports AWS, Kubernetes, Docker, and multiple clouds
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-08-16T01:39:58.472Z
Best For
Who it's for
- Enterprises handling sensitive or regulated data
- Organizations needing pre-execution policy enforcement and approvals
- Teams requiring unified visibility and cost control over AI usage
- Compliance-focused deployments on customer-controlled infrastructure
What it does well
- Enforcing policy and protecting sensitive data before AI requests reach providers
- Discovering and governing shadow AI and custom agents across the organization
- Attributing and capping AI spend by team, model, or environment
- Generating audit-ready evidence for regulated industries
- Governing MCP tools and routing requests across providers or local models
Integrations
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Sign Up to ContributeFrequently Asked Questions
- Is Cortega AI Governance Platform free?
- Cortega AI Governance Platform is a paid tool. No permanent free tier is offered.
- Is Cortega AI Governance Platform open source?
- No — Cortega AI Governance Platform is a closed-source tool. Source code is not publicly available.
- Does Cortega AI Governance Platform have an API?
- Yes. Cortega AI Governance Platform exposes a developer API. See the official documentation at https://cortega.ai for details.
- Can I self-host Cortega AI Governance Platform?
- Yes. Cortega AI Governance Platform supports self-hosting on your own infrastructure.
- What platforms does Cortega AI Governance Platform support?
- Cortega AI Governance Platform is available on: Self-hosted on customer infrastructure; supports AWS, Kubernetes, Docker, and multiple clouds.
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The problem with late discovery
The AI request your employee just sent to Claude contained last quarter’s unreleased financials — and you found out three weeks later in a Slack message, not a log.
Cortega deploys as a gateway layer on infrastructure you control, intercepting every LLM call from browsers, agents, servers, and cloud runners. It runs identity verification, data inspection, and policy enforcement before the request leaves the network. The control plane manages policy, identity, routing, and budget caps across every gateway. The analytics plane turns that traffic into an org-wide view of who calls what model, at what cost, and with what data categories.
Key capabilities
Pre-egress policy enforcement catches detection, redaction, blocking, and rerouting before any call reaches a provider. Self-hosted deployment on customer infrastructure supports data residency needs. Hard budget caps apply per team, model, and environment at the request level. Audit trails record identity, data category, and policy decision for regulated work.
Integrations and platforms
It works with OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, Ollama, LangChain, LangGraph, CrewAI, Okta, Entra ID, and others. Platforms include AWS, Kubernetes, Docker, PostgreSQL, and Redis. An API is available and a self-hosted option exists.
Who it is for / who should skip it
Enterprises handling sensitive or regulated data, teams that need pre-execution policy enforcement, and compliance-focused deployments on customer infrastructure benefit most. Teams that require a quick self-serve proof of concept should skip it because an architecture review and technical briefing gate onboarding and extend the sales cycle by weeks.