Browser and desktop AI
Uploads and pastes into consumer AI tools intercepted at the point of use, so the customer list never reaches the chat window rather than being discovered in a log review.
Every other control tells you what already happenedThis one holds the action before it does
Monitors browser, desktop, coding-agent, MCP and cloud actions, and before impact holds the risky one to evaluate actor, resource, destination, arguments, privilege, business intent and policy — then redacts, requires approval, reroutes, downgrades or denies, and proves no data left and production did not change.
See the programme
The enforcement layer across every domain
How It Works
Observe, hold, decide, prove.
01
Observe
Actions are watched where they happen — the browser upload, the desktop agent's file read, the coding agent's shell command, the MCP tool call, the cloud mutation — starting in monitor mode so the baseline is real traffic.
02
Hold
An action matching a risky pattern is held before it takes effect, which is the difference between preventing an exfiltration and writing it up afterwards.
03
Decide
The held action is evaluated on actor, resource, destination, arguments, privilege, business intent and policy, then redacted, approved, rerouted to a sandbox or approved model, downgraded to weaker credentials, or denied.
04
Prove
Each decision produces evidence of what did not happen — non-release of data, non-execution of the command — alongside coverage, latency and bypass metrics for the control itself.
What You Can Do
Everything it controls.
Coding agents and CLI
Commands from IDE and terminal agents evaluated before execution, because the destructive operation and the helpful refactor arrive through the same channel.
MCP and tool calls
Tool invocations checked against the arguments they were actually given, so a read tool pointed at a credential path is stopped even though the tool itself is approved.
Cloud mutations
Changes to production infrastructure held for evaluation, so an agent with a valid role still cannot delete the database because its credentials permitted it.
Redact rather than block
Sensitive fields stripped from an otherwise legitimate request, so the workflow completes without the data leaving — the option between allow and deny that keeps the control from being switched off.
Require approval in the moment
Risky-but-plausible actions routed to a human with the full context of what was requested and why, rather than failing silently and pushing the user to find a way around.
Reroute to a sandbox or approved model
A request headed for an unapproved model or an unsafe environment is redirected to a sanctioned one, so the work continues inside policy instead of stopping at it.
Downgrade credentials in flight
The action proceeds with weaker privileges than the caller holds, which contains a compromised agent without requiring every workflow to be re-architected first.
Monitor mode first
Every policy runs in observation before it prevents anything, so the blast radius of the control itself is known before it is turned on.
Proof, not just logs
Evidence on coverage, latency, bypass, non-release and non-execution, which is what turns an enforcement claim into something an auditor or a board can accept.
Business Outcomes
What it changes.
Prevented
The harmful action is stopped before data leaves or production changes, rather than reconstructed from logs once the impact is already real.
Usable
Redact, approve, reroute and downgrade sit between allow and deny, so the control survives contact with people trying to do their jobs.
Provable
Every decision produces evidence of what did not happen, which is the only form of assurance a prevention claim can actually offer.
Stop the action, not the investigation.
Start in monitor mode on agreed browser, agent, MCP and cloud paths, then enable prevention on the ones that matter with evidence on both sides of the switch.