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Privacy and Secure AI Operations

Microsoft via Coursera

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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

Project Module: Privacy & Secure Operations Compliance Package

Integrate everything you've built across LC 4 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.