Deployed inside the AI committees of
01 · The platform
Three capabilities. One control plane.
Find every model your organization runs.
Continuous discovery across cloud, hybrid, and on-prem. DataSafeguard picks up models in CI/CD, model registries, notebooks, and inference endpoints — without depending on someone remembering to register them.
Read the discovery spec →Policy at inference time, not at audit time.
Block, redact, or watermark requests that violate policy. Real-time enforcement on inputs and outputs, with one config bundle per regulatory framework — EU AI Act, NIST AI RMF, UK DSIT, or your internal model risk policy.
How enforcement works →Evidence regulators actually accept.
Every model decision, drift event, and policy change lands in an immutable audit log. Exports map to EU AI Act articles, NIST AI RMF functions, and ISO 42001 controls — so your annual review takes minutes, not weeks.
Compare evidence formats →02 · How it works
Three steps. Forty-eight hours to first signal.
Connect
Point DataSafeguard at your model registry, MLOps platform, or inference endpoints. SDK or agent-based — your call. No data ever leaves your environment in self-hosted mode.
Classify
Every model gets a risk tier (EU AI Act categories or your internal framework), a policy bundle, and a model card — automatically, with human-in-the-loop for anything ambiguous.
Govern
Policies enforce inline. The audit trail accumulates. Drift, bias, and PII leakage land on the model owner's Slack within minutes — not on a quarterly committee agenda.
Today on the platform
Next step
Stop discovering your models in the wrong meeting.
If “we don't know which models are subject to the EU AI Act yet” is a sentence anyone says in your organization, you're past the point where a spreadsheet will catch up.