From Privacy and data protection into AI governance

What you already carry

Credit before a single lesson. A work product counts as shown; experience you would describe counts as a claim. Neither is proof yet, and the graded path turns them into proof.

• Privacy law applied to AI (shown by a work product)

  Privacy assessments and lawful-basis records are your work product.

• AI risk and impact assessment (shown by a work product)

  A DPIA is an impact assessment; the AI version adds accuracy, bias and oversight.

• Data lineage and provenance (shown by a work product)

  Data maps and records of processing are lineage by another name.

• AI vendor due diligence and third-party risk (described, not yet shown)

  Processor and vendor review is routine privacy work.

• Data classification, access and retention (described, not yet shown)

  Classification and retention rules are familiar territory.

Roles this background reaches

• AI Governance Analyst

  The draft risk tier of a use case and the evidence package that goes to the reviewer.

• AI Compliance Manager

  Which obligation maps to which control, who owns it, and whether an exception may be granted.

• Responsible AI Lead

  The review criteria for sensitive uses, and how tension between speed and harm is resolved.

• AI Privacy Engineer

  The data flows, minimization rules, retention and technical controls an AI system ships with.

  43% from this background alone
• Chief Privacy Officer, AI privacy edition

  Whether a proposed use of personal data in an AI system is lawful, minimized and defensible.