AI Governance, Risk & Compliancein Seattle

40 Hours/5 daysLive virtual trainingIntermediate

Classroom training and venue arrangements depend on the availability of a suitable conference hall in Seattle, United States. Dates, seating capacity, facilities and the final venue are confirmed before booking.

Learning in Seattle, United States

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Seattle is known for diverse industries, professional networks and internationally connected business communities.

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Industry, innovation and professional networks

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Explore AI Governance, Risk & Compliance

Explore responsible AI practices, risk assessment, regulatory compliance, and governance frameworks with StepMerit's AI Governance, Risk & Compliance course. Learn to identify AI-related risks, establish accountability, evaluate ethical concerns, and implement policies that support transparent, secure, and compliant AI systems. Develop practical knowledge of AI risk management, data privacy, regulatory requirements, and responsible AI implementation through industry-focused training in Seattle, United States.

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Your learning location

Learning from Seattle

Seattle is known for diverse industries, professional networks and internationally connected business communities.

Course curriculum

Module 1: Introduction to AI Governance, Risk & Compliance
- Fundamentals of AI governance and responsible AI management.
- Importance of risk management and compliance in AI adoption.
- Key challenges in governing traditional AI and generative AI systems.
- Roles and responsibilities of AI stakeholders.
Module 2: AI Governance Frameworks and Principles
- Core governance principles, including accountability, transparency, fairness, and human oversight.
- Overview of organisational AI governance frameworks.
- Establishing AI policies, standards, and governance structures.
- Aligning AI initiatives with organisational objectives.
Module 3: AI Risk Identification and Assessment
- Identification of technical, operational, ethical, legal, and reputational risks.
- AI risk classification and prioritisation techniques.
- Risk assessment matrices, risk registers, and impact analysis.
- Evaluating risks across the AI system lifecycle.
Module 4: Responsible AI and Ethical Considerations
- Principles of ethical and responsible AI development.
- Identifying bias, discrimination, and unfair outcomes.
- Transparency, explainability, and accountability in AI systems.
- Human oversight and responsible decision-making.
Module 5: AI Regulatory Landscape and Compliance
- Overview of relevant AI laws, regulations, standards, and regulatory developments.
- Introduction to the EU AI Act and its risk-based approach.
- Understanding applicable data protection and privacy requirements.
- Mapping organisational obligations to AI use cases and jurisdictions.
Module 6: AI Security, Privacy, and Data Governance
- Data privacy and protection throughout the AI lifecycle.
- Secure handling of training data, prompts, outputs, and sensitive information.
- Access controls, data minimisation, and retention policies.
- Security risks involving AI applications and generative AI systems.
Module 7: Generative AI Governance and LLM Risk Management
- Governance considerations for large language models and generative AI applications.
- Identifying hallucinations, prompt injection, data leakage, and misuse risks.
- Evaluating model outputs and establishing acceptable-use policies.
- Human review, escalation procedures, and output validation.
Module 8: AI Impact Assessment and Risk Mitigation
- Conducting AI impact assessments for proposed and deployed systems.
- Evaluating potential effects on individuals, organisations, and stakeholders.
- Designing risk treatment plans and mitigation controls.
- Defining residual risk acceptance and escalation criteria.
Module 9: AI Transparency, Explainability, and Documentation
- Documenting AI use cases, model limitations, assumptions, and intended purposes.
- Maintaining AI inventories, model cards, and system documentation where appropriate.
- Recording decisions, approvals, and changes to AI systems.
- Supporting traceability and audit readiness.
Module 10: AI Compliance Monitoring and Auditing
- Designing AI compliance monitoring processes.
- Evaluating governance controls and collecting supporting evidence.
- Conducting internal reviews and preparing audit documentation.
- Tracking compliance gaps, corrective actions, and review outcomes.
Module 11: AI Vendor and Third-Party Risk Management
- Assessing risks associated with external AI models, tools, and service providers.
- Reviewing third-party documentation, data handling, and security practices.
- Understanding contractual responsibilities and shared governance obligations.
- Establishing vendor review and ongoing monitoring procedures.
Module 12: AI Governance Policies and Operational Controls
- Developing AI acceptable-use policies and internal governance procedures.
- Defining roles, approval workflows, and accountability structures.
- Establishing model access, change management, and incident reporting controls.
- Integrating AI governance into existing organisational processes.
Module 13: AI Incident Management and Continuous Improvement
- Identifying and documenting AI-related incidents and policy violations.
- Developing escalation, investigation, and remediation workflows.
- Monitoring governance performance and control effectiveness.
- Updating policies and controls based on review findings.
Module 14: Capstone Project – AI Governance and Risk Assessment
- Select an AI use case and document its intended purpose, stakeholders, and potential impacts.
- Create an AI risk register covering technical, ethical, privacy, security, and compliance risks.
- Develop a governance plan with controls, responsibilities, and review procedures.
- Prepare an AI impact assessment and supporting documentation.
- Present a risk mitigation and compliance monitoring plan through StepMerit's AI Governance, Risk & Compliance training in Seattle, United States.

Exam details

Exam Level: Intermediate – AI Governance, Risk & Compliance.
Exam Type: Online computer-based assessment featuring multiple-choice and scenario-based questions.
Number of Questions: 40 questions.
Exam Duration: 90 minutes.
Proposed Passing Score: 70% (28 out of 40 questions).
Exam Language: English.
Scenario-Based Evaluation: Assess the ability to identify AI risks, recommend mitigation measures, interpret governance requirements, and select appropriate compliance controls.
Practical Knowledge Assessment: Evaluate understanding of AI risk registers, impact assessments, governance policies, audit documentation, and monitoring procedures.
Evaluation Criteria: Conceptual understanding, risk identification, compliance awareness, ethical decision-making, and application of governance practices to business scenarios.
Certification: Eligible learners may receive a StepMerit course-completion certificate after satisfying the applicable course and assessment requirements.
Exam Preparation: Review AI governance frameworks, AI lifecycle management, risk classification, regulatory requirements, privacy controls, generative AI risks, and compliance monitoring.
Training Support: StepMerit provides structured learning and assessment preparation through its AI Governance, Risk & Compliance training in Seattle, United States.

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Who this course is for

AI Governance Professionals AI Risk Management Specialists Compliance Officers and Managers Cybersecurity Professionals Data Privacy and Protection Specialists Responsible AI Practitioners Legal and Regulatory Professionals IT Risk and Audit Professionals AI and Machine Learning Engineers Data Scientists and Analysts Business Leaders and Decision-Makers Technology Consultants Enterprise Risk Management Professionals AI Policy and Governance Professionals in Seattle, United States

Before you begin

Basic Artificial Intelligence Knowledge – Understand fundamental AI concepts, applications, and how AI systems support business operations. Generative AI Fundamentals – Familiarity with generative AI, large language models (LLMs), and AI-powered applications is beneficial. Risk Management Fundamentals – Understand basic risk identification, risk assessment, risk mitigation, and risk monitoring principles. Governance and Compliance Awareness – Have a basic understanding of organisational governance, internal policies, regulatory requirements, and accountability. Data Privacy and Protection – Familiarity with data privacy, sensitive information handling, data protection principles, and access…

Learning outcomes

Problem Statement A hypothetical telecommunications company introduces AI-powered systems to improve customer support, detect network faults, predict service disruptions, analyse customer feedback, and personalise service recommendations. Although these systems improve operational capabilities, they also introduce governance challenges involving customer data privacy, algorithmic bias, inaccurate AI-generated responses, security vulnerabilities, regulatory obligations, and accountability for automated decisions. The organisation lacks a consistent framework for identifying AI-related risks, documenting system limitations, assigning responsibility, and monitoring compliance throughout the AI…

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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Professional Experience: 5 years of experience in relevant technology, governance, risk management, compliance, or related domains. Technical Training Experience: 150+ hours of technical or professional training delivered. Candidates Trained: 200+ learners trained across relevant technology, risk, compliance, or AI-related programmes. AI Governance Knowledge: Understanding of AI governance principles, accountability structures, organisational policies, and responsible AI practices. AI Risk Management: Knowledge of risk identification, risk assessment, risk registers, mitigation strategies, and control monitoring. Regulatory and Compliance Awareness: Familiarity with applicable AI…

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