How can businesses ensure their AI governance aligns with data privacy regulations?
Map data flows for each AI use case. Choose a basis that aligns with regulations. Minimize and pseudonymize training data. Explain when AI is in the loop. Honor data rights, secure training sets, and run DPIAs when risk is high or data is sensitive.
Why it matters
AI multiplies privacy risk when inputs, purposes, and downstream sharing are not controlled.
Deep dive
Purpose and basis: document purpose and lawful basis for each use case and avoid secondary use creep.
Minimization: remove identifiers and prefer synthetic data when possible. Keep secrets out of prompts and segregate them.
Transparency: use just in time notices, provide simple explanations of outcomes, and offer a human appeal path.
Rights handling: maintain a DSAR playbook that can locate training and output records when applicable.
DPIAs: required for sensitive data, automated decisions, or high impact uses.
Vendors: DPAs, subprocessor checks, and limits on logging and retention.
Checklist
Map data and purposes for each AI use case.
Select a lawful basis and document necessity and safeguards.
Minimize and pseudonymize training data and block secrets in prompts.
Provide notices and appeals and log significant decisions.
Run DPIAs and manage vendor DPAs and retention limits.
Definitions
Pseudonymization: Replacing direct identifiers with tokens.
Automated decision with legal effect: An outcome that materially affects rights or obligations.