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Specialised Certification

AI Privacy & Data Governance Professional (APGP)

AI systems depend on data in ways that traditional data management was not designed to handle. Training data carries biases that become embedded in model behaviour. Synthetic data introduces its own quality risks. Foundation models are trained on data whose provenance the deploying organisation cannot verify. A specialised certification for professionals who need to govern data quality, lineage, and privacy obligations across the full AI data lifecycle.

Exam Duration
110 minutes
Validity
3 years
Format
Online Sessions

Target Roles

  • Privacy Officers
  • DPOs
  • Data Governance Leads
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AI Privacy & Data Governance Professional (APGP) certification badge

Why Pursue This Certification?

When data governance has not caught up with AI, organisations face a compounding problem. Poor data quality in training sets produces biased or unreliable models, and the absence of data lineage makes it impossible to diagnose why a model fails or to demonstrate compliance with data protection obligations. Consent mechanisms designed for databases do not account for personal data embedded in model weights. Right to erasure requests have no clear technical path when the data is not stored in a retrievable record but learned into a model's parameters.

Cross-border transfer obligations become significantly more complex when data flows through foundation model supply chains involving multiple jurisdictions. These are not edge cases. They are the daily reality for privacy and data governance professionals working with AI systems. The APGP certification exists because RESAIA believes these professionals need knowledge that is specific to how AI systems consume, transform, and retain data, not an extension of frameworks designed for traditional data processing.

Learning Outcomes

What You Can Expect

  • Assess data quality across dimensions relevant to AI systems, including representativeness, labelling accuracy, and temporal relevance.
  • Design data lineage and provenance tracking systems that trace data from source through transformation to model training and inference.
  • Implement data protection controls that satisfy obligations for consent, data subject rights, and cross-border transfers in AI contexts.
  • Evaluate privacy risks specific to AI, including re-identification from model outputs, memorisation in generative systems, and inference attacks.
  • Establish data handling requirements for third-party AI components, including foundation models where training data provenance is limited.
  • Integrate data governance requirements into AI lifecycle gates so that data quality and compliance are verified before models proceed to deployment.

Study Material

RESAIA Body of Knowledge

The APGP draws primarily from the BOK's Data Governance and Privacy chapter, with supporting material from AI Ethics, AI Risk Management, AI Lifecycle Management, and AI Supply Chain and Third-Party Management. The BOK teaches these disciplines not as independent topics but as parts of a connected governance system, so candidates understand where data governance sits within the broader governance architecture and how it depends on and feeds into other disciplines.

Explore RESAIA BOK

Application Process

  1. 01

    Choose Program

    Confirm the privacy and data governance specialization fits your work.

  2. 02

    Learning

    Study data accountability, lawful use, minimization, quality, and lifecycle controls.

  3. 03

    Take Exam

    Complete the 110-minute online certification assessment.

  4. 04

    Earn Certification

    Receive a three-year professional credential after passing.

Resources

Your one-stop solution for responsible AI development learnings.

RESAIA exists to make that practice measurable, teachable, and accountable. It gives every discipline that touches AI a shared structure, a shared language, and a shared standard to measure against.

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