AI Governance, Compliance, and Security for Enterprise Leaders

• An understanding of enterprise risk, legal, or technology frameworks
• Experience in executive leadership, cybersecurity, or compliance oversight
• No prior technical knowledge of AI development is required

Audience

• Chief Information Security Officers (CISOs)
• Legal counsel and compliance officers
• Chief Technology Officers (CTOs)

AI governance is the strategic management of legal, ethical, security, and operational risks associated with deploying AI in enterprise environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level enterprise leaders who wish to understand how to govern and secure AI systems responsibly and in compliance with emerging global frameworks such as the EU AI Act, GDPR, ISO/IEC 42001, and the U.S. Executive Order on AI.

By the end of this training, participants will be able to:

• Understand the legal, ethical, and regulatory risks of using AI across departments.
• Interpret and apply major AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001).
• Establish security, auditing, and oversight policies for AI deployment in the enterprise.
• Develop procurement and usage guidelines for third-party and in-house AI systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

AI in the Enterprise: Strategic and Legal Perspectives

• AI adoption in core business functions: opportunities and risks
• Executive responsibility in AI governance
• High-risk AI systems and organizational exposure

AI Risk Classification and Global Regulatory Landscape

• EU AI Act: risk tiers, requirements, penalties
• U.S. Executive Order on AI and emerging federal/state rules
• AI-related compliance within GDPR, HIPAA, and other frameworks
• Overview of ISO/IEC 42001, NIST AI RMF, OECD AI Principles

Security and Oversight of AI Systems

• AI security posture: threats, vulnerabilities, and safeguards
• Incident response and breach notification in AI-driven workflows
• Auditing and traceability of model inputs, decisions, and outputs

Responsible AI Procurement and Vendor Risk

• Due diligence when sourcing AI tools (including LLMs and APIs)
• Key contract elements: data ownership, model explainability, SLAs
• Evaluating vendor claims: bias mitigation, privacy guarantees, safety

Internal Governance Frameworks and Organizational Controls

• Creating AI use policies across departments
• Ethics committees, risk review boards, and cross-functional oversight
• Training, documentation, and compliance integration

Use Case Evaluation and Risk Scenarios

• Assessing high-impact use cases (e.g., HR screening, finance scoring, customer service bots)
• Tools and templates for AI risk assessments
• Scenarios: misalignment, drift, hallucination, discrimination

Emerging Trends and Future Considerations

• Anticipating regulatory evolution and global convergence
• GenAI-specific risks and governance extensions
• Responsible scaling of AI operations in the enterprise

Summary and Next Steps