Governance

Real-Time AI Governance

Author AI policy as code, simulate it in monitor mode, then enforce inline — from a coaching nudge to a hard block — with sub-10ms local decisions.

In short

Real-time AI governance means applying a policy decision at the moment of interaction — before a prompt or file is sent to a model — rather than detecting a violation afterwards in logs. KavachLab evaluates every interaction locally on the device in under 10 milliseconds, with no cloud round-trip, so enforcement works off-VPN and fully offline. Policy is graduated rather than binary: the same rule can coach, require justification, request approval, redact sensitive spans, or hard-block, depending on the data and the context.

What it gives you

Policy as code
Version-controlled, reviewable, and diffable policy — with the same change process as the rest of your infrastructure.
Simulate before you enforce
Run any policy in monitor mode against real traffic and see exactly who would have been blocked, before anyone is.
Graduated responses
Allow, warn, justify, require approval, redact, or block — matched to the actual sensitivity of the interaction.
Sub-10ms, local
Decisions are made on-device and never depend on a network round-trip, so enforcement never becomes latency.

Why blanket blocking fails

The instinctive response to Shadow AI is to block the domains. In practice this reliably produces three outcomes, none of them the intended one:

Usage moves to personal devices and personal accounts, where security has no visibility at all.
The business loses a genuine productivity advantage to competitors who found a safer way to say yes.
Security is positioned as an obstacle, which erodes the cooperation needed for every other control.

Graduated enforcement in practice

Most interactions are entirely benign and should pass untouched. The value of a policy engine is in how precisely it can respond to the small fraction that are not:

Coach — an inline nudge explaining why the content is sensitive, letting the user self-correct.
Justify — the user proceeds but records a business reason, which is captured in the audit trail.
Approve — the interaction is held pending a manager or security approval.
Redact — the sensitive span is removed and the rest of the prompt goes through, so work continues.
Block — the interaction is stopped outright, with a clear explanation of which rule applied.

Every decision is explainable

Enforcement that cannot be explained cannot be defended to an auditor, a regulator, or the employee who was blocked. Every KavachLab decision records the rule that fired, the data classification that triggered it, the contributing risk factors, and the resulting action — written to an immutable, tamper-evident audit trail.

FAQ

Real-Time AI Governance: questions we get asked

Straight answers to what security, privacy, and compliance teams ask us first.

Does inline enforcement slow down the user?

No. Decisions are made locally on the device in under 10 milliseconds, which is well below the threshold of human perception and far faster than the model response itself. Because there is no cloud round-trip, enforcement adds no network latency and continues to work offline.

Can we test a policy before enforcing it?

Yes. Any policy can run in monitor mode against live traffic, producing a full report of what would have happened — which users, which interactions, which actions — without affecting anyone. This is the recommended way to roll out every new rule.

What happens when a laptop is offline?

Enforcement continues. Because policy is evaluated locally, an off-VPN or fully offline device still applies the current policy, and the resulting audit events synchronise when connectivity returns.

Keep reading

Ready to see every AI interaction?

Talk to our team for a guided demo and a scoped, monitor-only discovery pilot.