Privacy and Secure AI Operations

This module moves from element-by-element classification to operational completeness. Learners apply retention rules and DPO sign-off triggers to AI training data documentation, then populate a full RoPA, including retention periods, DPO routing, and the documentation that survives an audit cycle.

This module builds the analytical skill of reading a DPIA the way a regulator reads one—finding what isn't there. Learners study Article 35 triggers and the structural anatomy of a defensible DPIA, then practice gap-detection methodology against a sample DPIA for a healthcare patient triage AI chatbot to identify missing mitigations and complete the mitigation plan.

This module moves from finding gaps to making the de-identification decision that closes one. Learners study the mechanisms and trade-offs of pseudonymization and differential privacy, examine re-identification risk thresholds, and use a comparison matrix to evaluate both techniques against the healthcare chatbot's training data, producing a recommendation a product owner can act on.

This module gives governance and security professionals enough transformer architecture to ask the right questions of engineering teams. Learners trace how a transformer processes tokens through the embedding and attention layers, then study the attack surfaces each layer creates and practice annotating a provided architecture diagram for a deployed customer-service AI assistant.

This module moves from architectural understanding to applied response. Learners study the major adversarial test categories and the robustness metrics each produces, then interpret a pre-run adversarial test report against the customer-service AI assistant and submit a prioritized hardening ticket. The work mirrors the real CB3 task: not running the tests, but reading the results well enough to drive the right hardening response.

This module builds the review skill that closes the gap between engineering execution and security accountability. Learners study the defence-in-depth model as applied to Azure ML deployments—network isolation, secrets management, and pipeline hardening—then practice reviewing a completed AML security configuration report for an internal financial reporting AI assistant, identifying configuration gaps and documenting them for the engineering team.

This module moves from configuration review to operational telemetry analysis. Learners study the major telemetry sources for deployed AI models — Defender for Cloud alerts and model-endpoint logs — and the signatures of prompt-injection attempts in each, then analyze two high-severity Defender for Cloud alerts against the internal financial reporting AI assistant to assess whether prompt-injection is the right diagnosis and whether the escalation response was appropriate.

This module builds the analytical foundation for vulnerability-response decisions on deployed AI models. Learners study how to read a CVE for AI model dependency relevance, the decision framework comparing patch and retrain paths across timeline, business impact, and residual risk, then practice making the call on a published CVE affecting a fintech fraud detection AI.

This module moves from making the decision to defending it where it matters. Learners study Change Advisory Board dynamics and the structural elements of a defensible change request, then practice presenting the patch-vs-retrain recommendation to a skeptical CAB chair in a Coach Role Play, handling pushback on timeline, resource cost, and residual risk while landing a decision the organization can act on.

Integrate everything you've built into a single regulator-facing Privacy and Secure Operations Compliance Package. You'll produce a portfolio-ready document for an Insurance Claims Triage AI deployment that combines GDPR documentation (RoPA + DPIA + de-identification recommendation), model security posture (transformer attack surfaces + AML defense-in-depth review), operational security analysis (telemetry findings + CVE response status), and risk disposition—the kind of integrated artifact a CB3 AI governance lead is expected to produce ahead of a regulatory readiness audit.