AI architecture for sensitive data

When your company needs this

Your company wants to use AI with restricted documents, conversations or records. Alongside model selection, it must define who can access each record, what leaves the environment and how a person checks the response.

Implementation experience

• Backend access control by workspace and action. Authorisation requires membership of the selected workspace and the corresponding permission.
• Media stored outside the public web directory and separated by workspace. Files move through disk or streams to limit memory use.
• Action records include the actor, context and result. Technical inference metrics are kept separate from input and output text.
• Structured model output validation and citation checks against the attributed speaker. A missing or misattributed excerpt is removed before display.
• Encrypted block transfers with integrity checks tied to the file and execution. Remote processing receives identified inputs.

What your team can engage me for

• A map of data flows, permissions and the services allowed to receive each type of content.
• Integration of private inference or an approved provider, storage and an audit trail.
• Validation, human review and recovery criteria with defined responsibilities.

The limit of verification

Checking that a citation exists does not prove that its interpretation is correct. The application must distinguish the source, the model's suggestion and the responsible person's decision.

The proposal defines technical controls and evidence. Legal requirements and decisions about data use must be defined with the company's responsible staff.