The problems this exists to solve.
How it works for your team, day to day.
Protect the code that is the company
For a software company, source is the balance sheet. Secret detection and proprietary-code classifiers run inline across IDEs, CLIs, and browser AI, so engineers keep their assistants while the crown jewels stay home.
Customer data stays in your tenant
Support, success, and sales teams keep using AI on tickets and call notes — with customer identifiers redacted inline before anything leaves. Your DPA commitments hold because the data they cover never reaches an unapproved model.
Turn the AI questionnaire into a win
When the prospect's security review asks how AI usage is governed, you answer with a live control plane: enforced policies, continuous evidence, SOC 2 Type II and ISO 42001 documentation on request. Vendors that can prove AI governance close enterprise deals that competitors stall on.
The questions we hear most.
Will this slow our engineers down?
Inspection runs at sub-38ms p99 and blocks show their reason with a sanctioned alternative. The common outcome is engineers keep every tool they had, minus the incidents.
How does deployment work for a remote-first fleet?
MDM push (Jamf, Intune, Workspace ONE, Kandji) plus SSO via Okta or Entra — no office network dependency, no VPN requirement, typically under 30 minutes end to end.
We're multi-tenant — how is our data handled?
Policy evaluation is inline, prompt content isn't used for model training, and residency options are available. Your own customers' data is redacted before it can reach third-party models.
Can we show this to our own enterprise prospects?
Yes — evidence exports and control documentation are designed to be shared under NDA in security reviews, alongside SOC 2 Type II and ISO 27001/42001 materials.