AI-governance tools cover different scopes: documentation, execution environments, observability, policy workflow, and evidence. Product comparisons are meaningful only against a named control objective and deployment; this page does not claim that other approaches fail as a class.
cybiont's publicly checkable result is the signed AI-response record: a valid bundle, a tampered negative control, and an offline verifier with explicit limits. Browser DLP, policy workflow, and the broader ledger remain prototype work.
Where each module sits today
- Browser DLP — prototype / not a production system. The shipped manifest carries that warning.
- Compliance ledger — prototype / public verifier available. The public package establishes only its stated request-and-response commitments.
- Governance workflow — prototype. Risk-adaptive policy and review are design targets, not a production claim.
- Trusted execution — architecture concept / partner-dependent. Any isolation and evidence properties must be established for the actual deployment.
Operating principles
- Evidence and control remain with the client, not the hyperscaler.
- Human-in-the-loop governance is first-class — the system prevents single-channel dominance.
- Evidence available for a pilot, its claims, and its acceptance criteria must be identified in writing before engagement.