Trust

A practical security posture for an AI learning product.

Gapless treats learning data, prompts, and generated paths as sensitive product context that should be protected by default.

Here is how Gapless approaches security across data handling, access, and infrastructure during the beta.

Data minimization

We collect the information needed to generate and improve learning paths, and avoid asking for unrelated personal details.

Team workspaces are built around role-based access, workspace membership controls, and audit-friendly administration.

Gapless runs on managed cloud hosting with encrypted transport, secret management, and monitored release workflows.

We review AI and infrastructure vendors for data handling, retention, and contractual protections before adopting them.