Responsible AI & Governance

Overview

Responsible AI & Governance focuses on the frameworks, controls, and documents an organization needs to use AI responsibly. You will work through case analysis, regulatory…

What you will learn

  • Articulate responsible AI principles and the business case for governance (Module 1).
  • Classify AI systems by regulatory risk and apply NIST AI RMF and ISO/IEC 42001 (Module 2).
  • Design a governance operating model with roles, intake, and lifecycle gates (Module 3).
  • Conduct an AI impact assessment and recommend risk treatments (Module 4).

Prerequisites

  • Required: Basic understanding of AI concepts and enterprise processes. No technical background. No coding required.
  • Helpful: Exposure to risk management or compliance frameworks. If you are new to AI, begin with Enterprise AI Literacy.

Curriculum

  1. Module 1: Foundations of Responsible AI

    • Topics: Fairness, accountability, and transparency; safety and reliability; human oversight; harms and failure cases; trust and adoption; business case for governance.
    • Deliverable: Case analysis. Completion check: Module 1 assessment.

    Hands-on:

    • Practice: Case Analysis. Three real AI incidents and their governance failures.

  2. Module 2: Regulatory and Standards Landscape

    • Topics: EU AI Act risk tiers; US and regional approaches; sector rules and data protection laws; NIST AI RMF; ISO/IEC 42001; mapping frameworks to controls.
    • Deliverable: Regulatory map. Completion check: Module 2 assessment.

    Hands-on:

    • Practice: Regulatory Mapping. Classify sample systems and list obligations.

  3. Module 3: AI Governance Operating Model

    • Topics: Roles, committees, and accountability; policies and standards hierarchy; integration with risk functions; AI inventory and intake; use case approval; lifecycle gates.
    • Deliverable: Charter. Completion check: Module 3 assessment.

    Hands-on:

    • Practice: Governance Charter. Roles, intake process, decision rights.

  4. Module 4: Risk and Impact Assessment

    • Topics: Risk taxonomy for AI; generative AI specific risks; third-party and vendor AI; algorithmic impact assessment; risk scoring and treatment; documentation.
    • Deliverable: Impact assessment. Completion check: Module 4 assessment.

    Hands-on:

    • Practice: AI Impact Assessment. Assessment and recommended controls for a proposed system.

  5. Module 5: Fairness, Bias, and Explainability

    • Topics: Sources of bias; fairness metrics; mitigation strategies; explainability techniques; model cards and system cards; user-facing disclosure.
    • Deliverable: Audit and model card. Completion check: Module 5 assessment.

    Hands-on:

    • Practice: Bias Audit. Fairness analysis on a sample model plus a model card.

  6. Module 6: Privacy, Security, and Data Governance for AI

    • Topics: Data provenance and consent; training data rights; retention and minimization; prompt injection and model attacks; access control and secrets; incident response.
    • Deliverable: Review report. Completion check: Module 6 assessment.

    Hands-on:

    • Practice: Data and Security Review. Gap assessment of a generative AI deployment.

  7. Module 7: Governing Generative AI and Agents

    • Topics: Acceptable use policies; output review and guardrails; content provenance; autonomy levels and approvals; tool permissions; audit trails.
    • Deliverable: Policy and guardrail spec. Completion check: Module 7 assessment.

    Hands-on:

    • Practice: GenAI Policy and Guardrails. Policy plus guardrails for an agentic system.

  8. Module 8: Monitoring, Audit, and Assurance

    • Topics: Performance and drift monitoring; incident management; change control; internal and external audits; board and regulator reporting; continuous improvement.
    • Deliverable: Assurance plan. Completion check: Module 8 assessment.
    • Capstone (independent work, [CAPSTONE_HOURS] hours): A complete AI governance package: charter, policies, intake and risk process, an impact assessment, a model card, and a monitoring and audit plan.

    Hands-on:

    • Practice: Assurance Plan. Monitoring and audit plan with KPIs and cadence.

Course fee

$699 $399

What's included

  • Live cohort + recordings: Attend every session live and revisit the recordings for one year.
  • Real lab environment: Practise on a hands-on environment that mirrors real projects.
  • Live doubt-clearing support: Ask questions and get answers in real time during every session.
  • Assessments and lab activities: Apply what you learn through graded assessments and guided labs.
  • Learning assets: Keep the slides, notes, and reference material for good.
  • Role-readiness guidance: Mentor guidance to get you ready for the role you are targeting.
  • Verifiable certificate: Earn a shareable certificate on completing the hands-on capstone.
  • Global peer community: Learn with a global cohort and stay connected after the program.

In short

Responsible AI & Governance is a 24-hour instructor-led course covering Module 1: Foundations of Responsible AI, Module 2: Regulatory and Standards Landscape, Module 3: AI Governance Operating Model. It includes guided practical labs.

Key facts

  • Course: Responsible AI & Governance
  • Duration: 24 hours
  • Modules: 8
  • Format: instructor-led live training

Frequently asked questions

Is this a legal course?

No. It covers the regulatory landscape and how to map obligations to controls, but it is not legal advice. Legal review of your policies remains your organization's responsibility.

Do I need technical skills for the bias audit?

No. Module 5 uses a prepared toolkit; you interpret results and choose metrics rather than write code.

Which frameworks are covered?

NIST AI RMF, ISO/IEC 42001, and EU AI Act risk tiers, with guidance on mapping to sector and regional rules.

How is this different from LLMOps & Production GenAI?

LLMOps implements technical guardrails and monitoring in code. This course builds the organization-wide governance model, policies, assessments, and assurance.

Does it cover AI agents?

Yes. Module 7 covers autonomy levels, approvals, tool permissions, and audit trails for agentic systems.

Can I use the documents at work?

Yes. All deliverables are templates built on a synthetic organization and are designed to be adapted.

What do I keep?

A regulatory map, charter, impact assessment, model card, review report, policy and guardrail spec, assurance plan, and the assembled governance package.

Is a corporate version available?

Yes, producing draft governance documents for your organization.

Topics covered

Responsible AI & Governance, Artificial intelligence, Corporate training