Privacy and Secure AI Operations

Microsoft via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

11 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

Microsoft Enterprise AI Governance, Ethics & Security

Overview

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This course addresses data privacy compliance, transformer-based AI security, and the operational security of deployed AI systems. You'll apply GDPR lawful-basis mapping to training data; conduct DPIA reviews and evaluate de-identification techniques; identify transformer attack surfaces and interpret adversarial test reports for hardening prioritization. You will also review defence-in-depth control implementation in Azure ML environments; assess security telemetry outputs for prompt-injection detection; and make patch-vs-retrain decisions when model dependency vulnerabilities are published. Technical content is scaffolded for managerial and governance audiences — no coding or model engineering required. Familiarity with GDPR and Azure fundamentals is recommended.

Syllabus

GDPR RoPA: Map Lawful Bases to AI Training Data
- This module establishes the GDPR foundation for AI training data work. Learners examine the six lawful bases under Article 6 and the additional protection layer for special category data under Article 9, then practice applying a structured decision tree to assign the correct lawful basis to individual data elements in a Record of Processing Activities.
GDPR RoPA: Populate the RoPA with Retention and DPO Sign-Off
- 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.
DPIA & De-ID: Analyze a DPIA for Missing Mitigations
- 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.
DPIA & De-ID: Evaluate Pseudonymization vs Differential Privacy
- 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.
Transformer Security: Map Attack Surfaces in the Architecture
- 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.
Transformer Security: Review Adversarial Tests and Submit a Hardening Ticket
- 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.
AML Security: Evaluate Defence-in-Depth Controls and Identify Configuration Gaps
- 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.
AML Security: Analyze Telemetry for Prompt-Injection Detection and Escalation
- 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.
Patch vs Retrain: Analyze the CVE and Make the Call
- 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.
Patch vs Retrain: Defend the Decision to the Change Advisory Board
- 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.
Project Module: Privacy & Secure Operations Compliance Package
- 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.