Data protection
Quality work happens on your data, in your cloud. Nothing leaves the environment.
- Test data stays inside the customer environment
- No customer data is used to train shared models
- Access to results follows the existing identity model
Regulatory Compliance Guide
A practical starting point for using AI-first QA as part of your compliance work. Not legal advice — a map of the controls the framework makes auditable by default.
This guide is informational. It does not constitute legal or regulatory advice.
Information security
The framework inherits your security controls instead of importing its own.
- Runs under your pipeline credentials and secrets store
- Repository access scoped to the minimum required
- Audit events logged for every pipeline action
Audit trail & evidence
Every gate decision leaves a record a reviewer can follow.
- Every run is tied to a commit and a timestamp
- Gate failures retain the evidence that caused them
- Reports are exported as durable, human-readable records
Accessibility
Generated suites include checks for the accessibility of the systems you ship.
- Keyboard navigation and focus order are covered
- Contrast and label checks run in the regular suite
- Failures surface at the gate, not after release
Release governance
Quality gates are release controls — the record proves the control ran.
- Coverage gate required before every release
- Overrides are recorded with a reason and an owner
- Trend history kept for review cycles
Evidence trail
Every run, result, and gate decision is recorded against a commit — a trail an auditor can follow end to end.
Traceability
Requirements link to cases, cases link to results, results link to releases. Nothing floats unattached.
Control by default
Quality gates are enforced by the pipeline, not by memory. The control either ran or the release did not happen.
Talk through your compliance context.
Tell us what your team must evidence — we'll map the framework to it.