Security and Confidentiality
How enterprise AI data projects can be planned with controlled access, confidentiality expectations and project-specific data handling rules.
Secure project planning starts before upload
Access, transfer, retention and reviewer expectations should match the sensitivity of each dataset and engagement.
Discuss RequirementsData handling controls for enterprise annotation work
AI data annotation projects may involve proprietary, operational, customer, medical, financial or policy-sensitive content. Security requirements should be defined during project scoping so access, transfer, retention and review workflows match the sensitivity of the data.
Access control
Limit access to assigned project teams and necessary reviewers.
Least-privilege access
Provide only the data and tooling access needed for the assigned task.
Confidentiality obligations
Use project-specific confidentiality expectations for teams handling client data.
Secure transfer
Agree on transfer methods appropriate to the customer's security requirements.
Data storage and retention
Define how long project data and outputs should be retained or removed after delivery.
Project separation
Keep project instructions, datasets and delivery workflows separated by customer or engagement.
Auditability
Where supported by the toolchain, use activity logs, review records and delivery documentation.
Incident response planning
Define escalation paths for suspected data handling issues.