Generative AI: Governance, Policy, and Emerging Regulation

Learn how to govern Generative AI responsibly with a understanding of AI policy, risk management, regulatory developments, and enterprise oversight. This course helps professionals build effective governance frameworks, manage compliance risks, and support the safe and responsible adoption of Generative AI across organizations.

$49.99
  • 2.5 hours
  • English
  • Certificate
  • Online
  • Last Updated on 23 Sep, 2026
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Generative AI governance, policy and emerging regulation online course featuring AI compliance, responsible innovation, risk management and regulatory frameworks.

Course Overview

Generative AI: Governance, Policy, and Emerging Regulation is a professional training course designed for individuals and organizations that need to understand how generative AI creates legal, operational, reputational, compliance, and business risks. As generative AI becomes more embedded in content creation, customer interaction, decision support, workflow automation, vendor platforms, and enterprise systems, organizations need clear governance structures before AI use creates unmanaged exposure.

This Generative AI: Governance, Policy, and Emerging Regulation course covers the hidden risks behind AI deployments, common governance failures in generative AI projects, executive awareness, AI policy design, roles, responsibilities, accountability structures, approval processes, monitoring, escalation, and risk management. Participants will also examine U.S. federal oversight expectations involving agencies such as the FTC, EEOC, DOJ, SEC, and FDA, along with state-level AI regulations, privacy, consumer protection, biometric compliance, and future regulatory readiness.

The curriculum is especially relevant for professionals working in compliance, legal, risk management, privacy, internal audit, governance, technology oversight, vendor management, executive leadership, and responsible AI program development.

What’s Included in the Generative AI Governance Course

Participants will receive structured learning aligned with the course curriculum and focused on generative AI governance, policy, regulation, and enterprise risk control, including:

  • Curriculum-based professional training

  • Generative AI governance risk knowledge

  • AI policy and accountability concepts

  • U.S. federal and state regulatory awareness

  • AI risk management and vendor risk coverage

  • Responsible AI culture and oversight principles

  • Certificate upon successful completion

What You'll Learn

  • Understand why generative AI governance has become a critical business, compliance, and executive oversight priority.
  • Identify common governance failures in generative AI projects and how they create organizational exposure.
  • Analyze legal, operational, reputational, and compliance risks connected to AI deployment.
  • Assess how AI governance frameworks, policies, roles, responsibilities, and accountability structures support safer AI use.
  • Apply NIST and federal guidance concepts to approval, monitoring, escalation, and control processes.
  • Evaluate federal AI oversight expectations, state-level AI regulations, privacy, consumer protection, and biometric compliance challenges.
  • Monitor high-risk AI use cases through model governance, testing, validation, performance monitoring, and vendor risk controls.
  • Improve responsible AI culture through bias prevention, transparency, explainability, human oversight, and future-ready governance planning.

Requirements for Generative AI Governance Training

No specific prior experience or qualifications are required to participate in this course. A general interest in generative AI, AI governance, compliance, risk management, legal oversight, privacy, vendor management, policy, or responsible technology adoption may be helpful, but it is not mandatory.

Why Choose Us

This Generative AI Governance course is designed to help learners understand AI policy and emerging regulation through a structured, professional, and workplace-relevant curriculum.

  • Curriculum aligned with generative AI governance, policy, regulation, risk management, and executive oversight priorities
  • Clear coverage of AI deployment risks, governance failures, accountability structures, and escalation processes
  • Professional training suitable for individual learners, business teams, and organizational decision-makers
  • Balanced focus on federal oversight, state-level regulation, privacy, consumer protection, and biometric compliance
  • Workplace-relevant knowledge for compliance, legal, risk, privacy, audit, procurement, and technology functions
  • Learner-focused structure that supports both technical and non-technical professional audiences
  • Professional development support through structured learning and successful-completion certification

Career Paths in Generative AI Governance

This course supports professional development across roles that require knowledge of generative AI governance, AI policy, emerging regulation, and enterprise risk management.

Relevant career areas and responsibilities include:

  • AI Governance Management
  • Compliance and Regulatory Risk Support
  • Legal and Policy Advisory Support
  • Enterprise Risk Management
  • Privacy and Consumer Protection Governance
  • Internal Audit and Assurance
  • Third-Party AI Vendor Risk Management
  • Responsible AI Program Oversight

Certification

Certification

A certificate is issued upon successful completion of the course. This certificate demonstrates that the participant has completed structured professional training in Generative AI: Governance, Policy, and Emerging Regulation and has developed knowledge of the main subject areas covered in the curriculum.

Certification

Course Curriculum

6 sections20 lectures2.5 hours

Module 1: The Hidden Risks Behind Every AI Deployment

4 Lectures30 minutes
▶ The AI Revolution: Why Governance Became a Business Survival Issue
▶ The Seven Biggest Governance Failures in Generative AI Projects
▶ How AI Creates Legal, Operational, Reputational, and Compliance Risks
▶ Building Executive Awareness Before AI Creates a Crisis
▶ Quiz
▶ Designing an AI Governance Framework That Actually Works
▶ Policies, Roles, Responsibilities, and Accountability Structures
▶ Using NIST and Federal Guidance to Build Practical Controls
▶ Creating AI Approval, Monitoring, and Escalation Processes
▶ Quiz
▶ Federal AI Oversight: FTC, EEOC, DOJ, SEC, FDA, and Emerging Expectations
▶ State-Level AI Regulations and What They Mean for Your Organization
▶ Privacy, Consumer Protection, and Biometric Compliance Challenges
▶ Preparing for Future AI Regulations Before They Arrive
▶ Quiz
▶ Identifying High-Risk AI Use Cases Before They Cause Harm
▶ Model Governance, Testing, Validation, and Performance Monitoring
▶ Third-Party AI Vendors: Hidden Risks in External AI Solutions
▶ Building AI Risk Management Programs That Protect the Organization
▶ Quiz
▶ Preventing Bias, Discrimination, and Unfair AI Outcomes
▶ Transparency, Explainability, and Human Oversight in Decision-Making
▶ Creating a Responsible AI Culture Across the Workforce
▶ Future-Proofing Your Organization for the Next Generation of AI Governance
▶ Quiz

Frequently Asked Questions

Generative AI governance is the system of policies, responsibilities, processes, controls, and oversight used to manage how generative AI is selected, developed, purchased, deployed, monitored, and used. It connects AI innovation with risk management, accountability, privacy, security, compliance, and responsible AI practices.

Generative AI can create legal, operational, reputational, privacy, compliance, and business risks as it becomes embedded in workplace tools, customer interactions, content creation, decision support, and enterprise workflows. Governance helps organizations establish appropriate policies, ownership, approval processes, monitoring, and escalation mechanisms.

Generative AI governance focuses specifically on governance challenges associated with generative AI systems and their uses. AI governance is broader and can cover predictive models, automated decision systems, machine learning, generative AI, and other AI technologies across the full lifecycle.

A governance framework can include AI policies, roles and responsibilities, accountability structures, risk assessment, approval processes, vendor controls, monitoring, escalation procedures, documentation, human oversight, and assurance activities. AGC's AI Governance Framework: Complete Guide for Organizations provides a broader framework perspective.

A Generative AI policy establishes organizational rules for how employees and teams may use generative AI. Depending on the organization, it can address approved tools, sensitive information, privacy, security, intellectual property, human review, prohibited uses, accountability, vendor requirements, and incident reporting.

Responsibility is usually distributed across business, technology, legal, privacy, security, risk, compliance, procurement, audit, and executive functions. A named owner should be accountable for individual AI use cases, while governance structures define how specialist teams contribute.

Common failures can include using AI without approval, unclear accountability, insufficient risk assessment, weak vendor oversight, inadequate monitoring, poor documentation, inappropriate data use, insufficient human oversight, and policies that do not reflect actual workplace AI use.

Generative AI risks can include inaccurate outputs, privacy exposure, security issues, bias and discrimination, intellectual property concerns, regulatory risk, reputational harm, operational failures, inappropriate automation, vendor dependency, and misuse of confidential information.

Not necessarily. Governance is multidisciplinary and involves technology, legal, compliance, privacy, risk, security, procurement, audit, and business functions. The course is designed for both technical and non-technical professional audiences.

Generative AI risk management is the process of identifying, assessing, prioritizing, treating, monitoring, and reviewing risks associated with generative AI systems and their use.

AGC's AI Risk Management: Complete Guide to Identifying, Assessing & Managing AI Risks provides broader coverage of AI risk management across the AI lifecycle.

The main purpose is to help professionals understand the governance, policy, regulatory, legal, operational, reputational, and compliance considerations surrounding generative AI and translate that knowledge into practical governance structures, risk controls, approval processes, monitoring, vendor oversight, responsible AI practices, and future regulatory readiness.