Lead AI Governance, Policy, and Continuous Compliance

Overview

This course equips data scientists, ML engineers, and AI risk professionals with the strategic tools to sustain responsible AI programs at scale. You will build KPI frameworks to measure and benchmark governance maturity, design feedback loops that strengthen compliance over successive model iterations, and create executive-level dashboards that make governance performance visible to senior stakeholders.

You will also develop the collaboration skills to bridge engineering, legal, and compliance teams where you will be establishing shared accountability structures, ethics committees, and documentation workflows that embed responsible AI into your organization's culture. In the final module, you will apply regulatory foresight techniques to anticipate emerging standards across the EU AI Act, NIST RMF, and global jurisdictions, and build adaptive compliance policies that evolve with the landscape, not behind it.

Learners with experience in ML, data science, or AI project management, and a working familiarity with compliance or risk concepts, will be best positioned to apply these frameworks immediately in their organizations.

Syllabus

• Measure and Validate Governance Effectiveness
Governance policies only prove their value when you can measure whether they work. In this module, you move from designing AI governance frameworks to quantifying their effectiveness. You will learn how to define key performance indicators that capture risk coverage, process adherence, and harm outcomes across your AI portfolio, then use maturity benchmarks drawn from frameworks like the NIST AI Risk Management Framework and ISO 42001 to validate policy performance against internal and external standards. You will also build structured feedback loops and monitoring dashboards that keep governance current as models, regulations, and business conditions change. By the end of this module, you will be able to design a governance measurement system that tracks, validates, and continuously improves responsible AI performance.
• Cross-Functional Stewardship and Accountability
This module equips you with the governance, accountability, and compliance skills needed to lead responsible AI programs in complex, regulated environments. You will explore how to define and measure governance performance through meaningful metrics and key performance indicators, build continuous improvement cycles that keep governance in step with evolving models and regulations, and construct cross-functional coalitions that align technical, legal, and business teams around shared accountability. You will also develop the regulatory foresight needed to adapt compliance policies across overlapping frameworks—including the EU AI Act, NIST AI Risk Management Framework, and ISO 42001—and learn to benchmark your organization's AI maturity against global standards. By the end of this module, you will be able to design defensible, audit-ready governance structures and translate regulatory obligations into actionable operational controls.
• Future-Proof AI Governance

Taught by

LearnQuest Network

Subjects

Data Science