Microsoft Enterprise AI Governance, Ethics & Security

This course covers the foundational governance and compliance tasks every AI program leader needs to perform. You’ll assign AI governance accountability using a RACI matrix, benchmark existing policies against Microsoft’s Responsible AI Standard v2, manage a live remediation backlog, apply ISO/IEC 42001 controls, assess audit evidence for SOC 2 readiness, author AI security policy, and evaluate policy exception requests—giving you full coverage of the governance and compliance lifecycle. Familiarity with foundational frameworks such as the NIST Cybersecurity Framework, ISO/IEC 27001, and privacy regulations such as GDPR will provide useful context, though formal certifications in these areas are not required. By the end of the course, learners can evaluate governance maturity, align AI systems with recognized standards, communicate audit readiness, and produce governance documentation suitable for executive review, regulatory scrutiny, and organizational deployment. This course is designed for AI program leaders, governance and compliance professionals, IT security leaders, and enterprise architects responsible for establishing or maturing AI governance in regulated environments.

This standalone short course prepares learners to translate their program skills into career-ready materials and interview confidence. Learners will articulate their AI governance expertise, build a targeted resume and professional portfolio, and practice for interviews in security manager, compliance lead, and AI governance roles at the CB3 level.

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.

This course focuses on the ethical and data governance dimensions of AI deployment. You’ll apply Microsoft’s six Responsible AI principles to score new use cases, detect and escalate bias in model outputs, vet third-party datasets for ethical sourcing, and classify and monitor data assets to enforce retention and quality standards. This course bridges technical controls with ethical decision-making at the managerial level. Some familiarity with AI model concepts is helpful. By the end of this course, you will be able to assess ethical AI risks, justify mitigation decisions, evaluate data quality and lineage, and produce governance documentation suitable for responsible AI review, audit preparation, and deployment approval.