WitnessAI
Sits between employees and AI tools so a company can see and control what is being sent to them.
Highlights
- Network-path interception of AI traffic with no endpoint agent required
- Observability dashboard mapping which employees and apps use which AI models
- Intent-based policy engine to allow, redact, or block AI activity by user and use case
- Data and topic protection to stop sensitive data and off-limits subjects reaching LLMs
- Coverage for both public SaaS models and private/self-hosted LLMs
- Single-tenant deployment with customer-managed encryption keys for data isolation
- Millisecond-latency enforcement so guardrails don't slow user-facing AI
External link — opens witness.ai in a new tab. WitnessAI is a third-party product; we are not affiliated with it.
About WitnessAI
What it is
WitnessAI sits in the network path between users and language models, giving an organisation visibility of which AI tools employees and applications are using, what is being sent to them, and the ability to enforce policy on that traffic by intent rather than by blunt blocking.
Why it's different
It addresses shadow AI, which is a genuine and near-universal problem: employees paste customer data, source code and contracts into whichever assistant they like, and most organisations have no idea it is happening. The choice between blocking everything, which drives usage onto personal devices where there is no visibility at all, and allowing everything, which is what most have defaulted to, is a bad one. A policy layer is the sensible third option. What to weigh carefully: this is by design a system that inspects what employees type, which is a significant surveillance capability and needs to be introduced honestly rather than discovered. Intent-based classification also has false positives, and a control that blocks legitimate work gets routed around.
How people use it
It is deployed in regulated industries and in companies with real intellectual property exposure, usually after an incident or an audit finding. The pattern that works is starting in observe-only mode for a period — the usage data alone is generally surprising and is what justifies the policy — then enforcing on the specific categories that matter rather than trying to govern everything at once.
Written by the n3os team. We are not affiliated with WitnessAI.
This listing was written from public information, without WitnessAI’s involvement. If you own it and something here is wrong — or you would rather not be listed at all — email us and we will correct or remove it.
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