Workplace AI Data Boundary Record

Workplace AI Data Boundary Record

Core rule

Data can enter an AI workflow only when the organization has authority for the purpose, the exact system path is approved, the data is necessary, access is controlled, retention and downstream use are understood, and the output can be handled under the same or stronger boundary.

"Do not paste secrets" is incomplete. Work data can include personal information, customer records, intellectual property, contracts, credentials, security details, employee data, regulated information, licensed content, confidential business plans, and ordinary records whose combination becomes sensitive.

Data-flow record

Map the source, owner, classification, purpose, minimum fields, user, device, application, tenant, connector, vendor, subprocessors, model path, logs, training or improvement use, retention, deletion, access, output destination, export, incident path, and contract.

NIST's Privacy Framework treats privacy risk across the data lifecycle and ecosystem. The FTC's AI confidentiality guidance explains why business customers should examine claims about retention, secondary use, training, and confidentiality rather than relying on an AI label.

Minimum necessary boundary

Start with synthetic, public, de-identified, or specifically authorized material when that can answer the test question. De-identification requires qualified review because free text and combinations of fields can re-identify people.

Do not move data into a consumer account because an enterprise product is unavailable. Account type, contract, tenant, administrative control, and connector state are part of the system.

Security boundary

CISA's secure AI development guidance emphasizes ownership, secure design, deployment, and operation. For a workplace buyer, the relevant checks include identity, least privilege, logging, source access, connector scope, incident response, and decommissioning.

Decision

The approved-system matrix should state what is allowed, prohibited, or requires review for each task and data class. It needs an owner and refresh trigger because vendors, contracts, features, models, connectors, laws, and organizational data change.