AI Risk & Compliance in Regulated Industries

Why This Course

Artificial Intelligence is transforming industries such as finance, healthcare, energy, and government, offering new opportunities for efficiency and insight — but also introducing new categories of risk, bias, and compliance challenges.

In highly regulated sectors, AI systems must not only perform accurately but also comply with laws, standards, and ethical frameworks.

This program provides participants with the knowledge and tools to design, manage, and govern AI systems within compliance-driven environments, ensuring safety, transparency, and accountability while supporting innovation.

What You’ll Learn and Practice

By joining this program, you will:

• Understand AI-specific risks and compliance challenges in regulated industries.
• Learn to apply risk management frameworks to AI design and deployment.
• Align AI systems with global standards and regulatory expectations (NIST, ISO, EU AI Act, GDPR).
• Develop strategies to mitigate bias, enhance explainability, and ensure fairness.
• Build governance models that balance innovation with legal and ethical responsibility.

The Program Flow

Day 1: The AI Regulatory Landscape

• The rise of AI regulation across industries — key trends and implications.
• Overview of major frameworks: NIST AI RMF, EU AI Act, OECD AI Principles, ISO/IEC 42001.
• Industry-specific regulations:
  • Healthcare: FDA’s Good Machine Learning Practices (GMLP)
  • Finance: Model Risk Management (SR 11-7) and algorithmic transparency
  • Energy & Public Sector: Critical infrastructure protection and AI ethics codes
• Workshop: mapping regulatory requirements for your industry.

Day 2: AI Risk Management Frameworks and Controls

• Categories of AI risk: data, model, operational, ethical, and reputational.
• Integrating AI into enterprise risk management (ERM) systems.
• The AI Risk Lifecycle: identification, assessment, mitigation, and monitoring.
• Risk control frameworks — applying the NIST AI RMF “Map–Measure–Manage” approach.
• Practical exercise: performing a risk assessment for an AI system in a regulated environment.

Day 3: Compliance, Audit, and Governance Structures

• Building AI compliance programs within corporate governance frameworks.
• Roles of compliance officers, auditors, and technical teams in AI oversight.
• AI documentation and transparency requirements (model cards, datasheets, impact assessments).
• Conducting AI audits and ensuring traceability in algorithmic decisions.
• Case study: analyzing AI compliance breaches and lessons learned.

Day 4: Ethics, Fairness, and Accountability

• Ethical considerations in AI development — bias, discrimination, and explainability.
• Implementing human oversight and accountability mechanisms.
• Methods for bias detection, fairness testing, and model explainability.
• Communicating AI risk transparently to regulators and stakeholders.
• Simulation: managing an ethical compliance issue in an AI deployment scenario.

Day 5: Operationalizing Responsible AI Compliance

• Building a compliance-by-design approach for AI systems.
• Developing AI policies, governance committees, and escalation processes.
• Cross-functional collaboration — legal, IT, risk, and business integration.
• Future outlook: harmonizing AI compliance globally (EU AI Act, U.S. AI Executive Order, GCC initiatives).
• Action workshop: designing an AI risk and compliance governance roadmap for your organization.

Individual Impact

• Gain in-depth understanding of AI risks, controls, and regulatory expectations.
• Strengthen analytical and leadership skills in compliance and governance.
• Build confidence in managing AI audit readiness and policy alignment.
• Learn to balance innovation with safety and legal responsibility.
• Enhance professional credibility in AI ethics, risk, and compliance management.

Work Impact

• Strengthen compliance with evolving AI regulations and standards.
• Reduce risk exposure from AI bias, inaccuracy, and non-compliance.
• Improve transparency, accountability, and governance structures.
• Increase stakeholder trust through responsible AI practices.
• Foster sustainable innovation within ethical and regulatory boundaries.

Training Methodology

This program combines regulatory insight with real-world practice and cross-industry case analysis.

Learning methods include:

• Case studies from finance, healthcare, and government sectors.
• Workshops on AI risk assessment and compliance implementation.
• Interactive simulations of ethical and legal dilemmas.
• Group discussions on governance and policy integration.
• Templates and checklists for AI compliance auditing and reporting.

Beyond the Course

Upon completion, participants will be equipped to design and manage AI risk and compliance frameworks tailored to regulated industries.

They will leave ready to lead responsible AI initiatives that align with global laws and ethical standards — driving trust, innovation, and resilience in AI-powered organizations.