The AI governance problem
Once more than one person, application, or agent is calling an LLM or an MCP tool, the same problems come up. This is the full list Cortega is built against, not a marketing summary of it.
Model routing and outages
A model returns errors or times out, and every caller hits the same failing model.
Addressed by AI Border Gateway: health-based failover and automatic same-request retry.
Shadow AI
AI usage on employee devices that never goes through anything you control: a desktop app, a browser assistant, a local MCP tool.
Addressed by Endpoint Guard: a native device agent and allow/deny AI-domain policy.
Unauthenticated or unattributed access
A request reaches a model or tool with no way to trace who or what sent it.
Addressed by AI Border Gateway: identities and virtual keys tie every request to a person, app, or team.
Identity spoofing in web and enterprise apps
A public-facing app needs to call a model, but putting a static API key in front of end users risks credential theft.
Addressed by AI Border Gateway: OIDC/JWT-based identity instead of a static key.
Credential and API key sprawl
Provider API keys hardcoded and copied across services are hard to rotate and easy to leak.
Addressed by AI Border Gateway: provider credentials are stored once, encrypted; callers use a Cortega-issued key, never the real one.
Credential leakage to upstream vendors
A client's own credential shouldn't be forwarded to a third-party model provider as-is.
Addressed by AI Border Gateway: a client-supplied credential authenticates to Cortega, then is stripped and replaced before the call leaves.
Unrestricted MCP tool access
An agent that can reach a tool server can call any tool on it, with no per-team limits.
Addressed by AI Border Gateway: MCP Authorization grants specific teams specific tools on specific servers, checked on every call.
Data exfiltration and PII leakage
Sensitive data leaving through a prompt or a response.
Addressed by AI Border Gateway: guardrails with built-in PII detectors (SSNs, credit cards, phone numbers, emails) and custom patterns. Endpoint Guard runs the same kind of detection locally on the device, for AI usage that never reaches a gateway at all.
Unsafe or injected content
Malicious or unwanted content reaching a model, or reaching a caller in a response.
Addressed by AI Border Gateway: a webhook guardrail can call an external content-safety or prompt-injection classifier.
Unbounded spend
Cost climbs with no limit as usage grows.
Addressed by AI Border Gateway: budgets and spend limits, enforced before a call is allowed through.
Model lock-in
Switching models means rewriting what every caller sends.
Addressed by AI Border Gateway: virtual models group several real models behind one name.
Hallucinated or unverified answers
A response reads as confident and is wrong. A fabricated legal citation is the flagship example.
Addressed by AI Verifier: its citation-checking agent, checked against a real case-law database.
Model version changes and deprecation
A model you depend on gets deprecated or superseded, and you have to find a replacement before it stops working.
Addressed by Model Intelligence: flags a deprecated or superseded model and names a specific successor, using your own traffic pattern and usage volume.
Not sure which product covers what you need? See Choosing the right product.