AI Privacy & Data Governance.

Prevent confidential data exposure

Private data appears in an answer, a log, or a tool call.

Prevent model memorization of sensitive data

The model can reproduce personal or secret training text.

Prevent agent data leakage

An agent carries private data into a tool, a page, or a later turn.

Detect accidental disclosure

A leak happens and no control notices it.

Obtain meaningful consent

Data is used for AI without consent a person could understand.

Manage consent over time

Consent is collected once and never revisited as the use changes.

Handle secondary data use

Data collected for one purpose trains or feeds another.

Respect data-use restrictions

Contractual or user limits on data are ignored by the pipeline.

Enable private inference

A query reveals more about a person than the answer requires.

Enable federated AI

Training across parties exposes one party data to the others.

Enable secure computation

Joint analysis requires pooling raw personal data.

Minimize sensitive data exposure

More sensitive data is collected and kept than the task needs.

Prevent inference of sensitive attributes

The system infers health, belief, or other sensitive traits the user did not provide.

Protect against membership inference

An outsider can tell whether a person data was in the training set.

Protect against attribute inference

Outputs or model access reveal attributes that were never asked for.

Limit unintended profiling

Interaction data builds a profile the person did not agree to.

Improve data quality

Training and decision data is wrong, stale, or incomplete and still used.

Enforce data access controls

Too many people and services can read the data behind the system.

Limit data retention

Personal data is kept after the purpose and the allowed time have ended.

Manage training data responsibly

Training data is gathered and reused without a record of rights or limits.