Secure

Most agentic AI failures do not look like failures.

They can look like normal operation, right up until a system has quietly moved beyond what anyone actually authorised. We bring the same structural rigor to your architecture, whether or not you run our code — solo operator or large organisation, the review does not change shape.

Why deterministic

A rule that does not bend, checked by something outside the model it governs.

Most agent security today asks a model to judge a model — an LLM classifier, or an “AI-native” filter, deciding whether another model’s output is safe. That shares the exact attack surface as the thing it is judging: whatever can fool the governed model can often fool the one watching it.

Our position is different, and it is not a marketing angle. Enforcement here is deterministic: fixed, code-level rules, evaluated with no neural computation anywhere in the decision. A rule either matches or it does not. Nothing about it can be reasoned with, prompted around, or talked out of firing.

Virasai’s approach draws on a governance architecture published by VaHive Systems Lab — see The Lab.

None of that is an assertion to take on faith. Sentinel and Magus OpenSecMCP — the tools we build and ship, not a demo — are the real, running instance of it: open source, inspectable, and checked against live data.

Inspect the open-source tools See what Watch has actually caught

The gap this closes

Confidence and evidence are not the same thing.

Gravitee’s State of AI Agent Security Report 2026 found organisations report 82% confidence in their ability to govern AI agents, while on average only 47.1% of deployed agents are actively monitored or secured. That gap is not a knowledge problem. It is what happens when nobody has checked which one you actually have.

We publish evidence rather than assert it. Below is a real Adversarial Issues Register, run against our own open-source gateway and published in full — not a mockup written to look like one.

Read the register

Service 01

Governance & Drift Audit

For anyone running a long-lived agent in production, or about to — solo operator through enterprise.

A structural assessment of how a deployed system’s effective operating policy may have moved relative to what was authorised. This is not a prompt review or a vague risk score.

Start a Governance & Drift Audit

Deliverables

Classification against instruction drift, autonomy accumulation, and authority laundering
Assessment of the decision/audit trail and its governance value
Remediation roadmap ordered by what is actually exploitable first
Written report and optional structured walkthrough

Service 02

Adversarial Issues Register

For anyone who wants assumptions pressure-tested before they ship — the same rigor whether you are one person or a whole team.

A formally categorised register of open problems in a governance approach. The point is not softened language; it is a defensible record of what is unresolved, how severe it is, and whether a solution path exists.

See a real one, published in full →

Enquire about an Issues Register

Deliverables

Categorised issue register with severity and solvability
Direct treatment of findings without an easy fix
A reviewable artifact for technical teams and diligence
Standalone or follow-on engagement

What this starts from

A free fit conversation first, then a scoped, paid engagement.

Like every engagement here, this starts with the same free fit conversation, not a quote — we assess honestly whether we can deliver real, measurable value on your system before any paid work begins. If it is, the size and scope of your system set the final figure. There is no rate card, because a single-agent review and a multi-week embedded role are not the same job, and pricing either as if they were would be the wrong kind of simple. And if real depth exceeds what we scoped together once we're into it, we pause and tell you before it costs you anything more, not after.

See the deliverable first

See what this starts from Talk about your system

A boundary stated upfront

Visibility depends on the deployment.

For API-hosted models, an engagement focuses on structural and execution-layer governance. Full activation-level analysis requires a locally hosted model that can be instrumented directly. We will state what can and cannot be assessed before work begins.