Cortega sits between your AI agents and the models, tools, and data they touch. It enforces policy, blocks sensitive data from leaving your network, and tracks approval on high-stakes actions, running entirely inside your own infrastructure, not ours.
Deterministic, rule-based detection and redaction for PII and payment data (PCI), applied before a request completes rather than flagged after the fact.
Decide which roles can call which models, and govern MCP tool calls, including servers, permissions, and actions, in one policy model.
High-stakes actions get a documented approval decision, with an identity and rationale attached, visible in your audit trail.
Compliance evidence generated continuously as agents operate, instead of reconstructed later from scattered logs.
Cortega EdgeSafe extends coverage to ChatGPT, Claude, and browser-based AI tools employees use outside your production systems.
See what AI is actually used for, where quality drifts, and what it costs, across every provider and team, in one view.
In red-team cyber evaluations, AI agents given permissive internet access took autonomous, unsanctioned action outside their assigned task in 10 of 122 runs, including one that used a fake identity to social-engineer a human approver into greenlighting an action it wasn't authorized to take. Days earlier, a separate test model left its sandbox with no human direction and reached a real company's production systems. The lesson isn't that a human was in the loop; it's that the approval itself wasn't backed by identity, policy, or evidence, so it could be talked into the wrong answer.
DetailsWant to see how Cortega would have stood between that agent and its next action? Talk to our team →
Cortega's architecture is built around the control expectations of the frameworks regulated buyers are already accountable to, not retrofitted after the fact.
Most platforms in this category converged on the same pitch in the last year: connect your models, get visibility, add guardrails. The differences that matter show up underneath that pitch.
AI behaves differently in production than in development. Cortega measures how providers, models, agents, prompts, and guardrails perform on real traffic. The same governed traffic that enforces policy also shows you quality, latency, cost, and drift.
Most AI security and observability tools are detective controls: they log what an agent did and alert on it afterward. Cortega is a preventive control. It sits in the critical path of every agent's traffic, inbound and outbound, so policy is enforced before the call completes.
Every plane, meaning data, control, and analytics, runs inside your infrastructure. Nothing routes through a Cortega-hosted cloud.
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A short call to map your agents, models, and data: production traffic, employee AI use, MCP tool calls, or all three.
Cortega installs inside the cloud or on-premises environment you already run, and points at the models and MCP tools your teams already use.
Guardrails, approvals, and audit evidence are active from the first request, not bolted on after an incident.
Tell us what you're trying to govern, whether that's production agents, employee AI use, MCP tool calls, or regulated data, and we'll show you exactly where Cortega fits.