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AI Act

AI Risk Manager

AI Risk Manager. Review available formats, prerequisites, current inclusions and certification terms before booking.

PECBRisk Manager5 daysLiveSelf-pacedIn-house
  • Practitioner-led, taught by a working CISO
Christophe Mazzola

Taught by

Christophe Mazzola

Practicing CISO · Founder of Cyber Academy

See full profile →

For

Right fit if you are.

  • ✓Risk professionals responsible for identifying and managing AI-related risks across their organizations
  • ✓IT and security professionals who want to build expertise in AI risk management
  • ✓Data scientists, data engineers, and AI developers involved in designing, deploying, or maintaining AI systems
  • ✓Consultants who advise organizations on managing and mitigating AI risks
  • ✓Legal and ethical advisors working on AI compliance and societal impact issues
  • ✓Managers and executives overseeing AI implementation and responsible AI adoption initiatives

Honest call

NOT for. When to skip it.

We'd rather you keep your money than buy the wrong path.

  • ✗Individuals with no prior exposure to AI or risk management concepts may find the pace demanding without some self-study beforehand
  • ✗Those seeking a purely technical AI engineering or model-development course will find the focus is on governance and risk rather than coding
  • ✗Professionals looking for a short introductory overview rather than a structured five-day programme may prefer a shorter format

Outcomes

What you'll be able to do

  • 1Explain core AI risk management concepts, approaches, and terminology with confidence
  • 2Identify and categorize AI-related risks including bias, security vulnerabilities, transparency gaps, and ethical concerns
  • 3Analyze and evaluate AI risks using structured assessment techniques
  • 4Design and apply risk treatment and mitigation strategies tailored to AI systems
  • 5Develop incident response measures that address AI-specific threats and vulnerabilities
  • 6Apply the NIST AI Risk Management Framework to governance and compliance scenarios
  • 7Interpret and implement requirements drawn from the EU AI Act within an organizational context
  • 8Monitor AI risk performance and communicate findings through structured reporting

Programme

Day by day

Day 1Introduction to AI Risk Management

  • AI Risk Fundamentals

    This module establishes core AI risk management concepts, including key terminology, the nature of AI-related risks, and why a structured risk management approach is essential for organizations adopting AI.

  • Overview of Key Frameworks and Regulations

    Participants are introduced to leading AI governance instruments, including the NIST AI Risk Management Framework and the EU AI Act, and how they shape organizational obligations.

By end of day

  • • Articulate what AI risk management means and why it matters at the organizational level
  • • Distinguish between the NIST AI RMF and EU AI Act in terms of scope and application
Day 2Organizational Context, AI Risk Governance, and AI Risk Identification

  • Organizational Context and Governance Structures

    This module covers how to define the internal and external context for an AI risk management program and how to establish appropriate governance roles, responsibilities, and accountability structures.

  • AI Risk Identification Techniques

    Participants learn systematic methods for uncovering AI-related risks, including bias in training data, model transparency issues, and security vulnerabilities across the AI lifecycle.

By end of day

  • • Map the organizational context relevant to an AI risk management program
  • • Apply structured techniques to identify a broad range of AI-specific risks
  • • Define governance roles that support responsible AI oversight
Day 3Analysis, Evaluation, and Treatment of AI Risks

  • AI Risk Analysis Methods

    This module examines qualitative and quantitative approaches to analyzing AI risks, including likelihood and impact assessment, and considers factors unique to AI systems such as model drift and adversarial attacks.

  • Risk Evaluation and Prioritization

    Participants learn how to compare analyzed risks against defined criteria to prioritize treatment efforts and determine which risks require immediate action.

  • Risk Treatment and Mitigation Strategies

    The module covers the selection and implementation of controls and mitigation measures to reduce or eliminate AI-related risks, including technical safeguards and process-level interventions.

By end of day

  • • Conduct a structured AI risk analysis using both qualitative and quantitative inputs
  • • Prioritize risks based on evaluation criteria aligned with organizational objectives
  • • Select appropriate treatment options for identified AI risks
Day 4AI Risk Monitoring, Reporting, Training, Awareness, and Performance Optimization

  • Monitoring and Reporting on AI Risks

    This module addresses how to establish ongoing monitoring mechanisms for AI risks and how to prepare clear, actionable reports for leadership and relevant stakeholders.

  • Training, Awareness, and Cultural Readiness

    Participants explore how to build organizational awareness of AI risks and design training programs that equip staff at all levels to act responsibly when working with AI systems.

  • Optimizing AI Risk Performance

    This module focuses on continual improvement practices, including using incident learnings, performance metrics, and feedback loops to strengthen the AI risk management program over time.

By end of day

  • • Design a monitoring and reporting structure that keeps leadership informed about AI risk status
  • • Build an internal awareness program that addresses AI risk at multiple organizational levels
  • • Use performance data to drive continual improvement of AI risk management practices
Day 5Review and Exam Preparation

  • Competency Domain Review

    Participants consolidate knowledge across all five competency domains covered during the week, including AI risk principles, governance, identification, evaluation, and organizational learning.

  • Exam Guidance and Q&A

    This session provides practical guidance on the structure and expectations of the PECB Lead AI Risk Manager exam, giving participants an opportunity to clarify any remaining questions before sitting the assessment.

By end of day

  • • Identify personal knowledge gaps across the five exam competency domains
  • • Approach the certification exam with a clear understanding of what each domain covers

Public sessions

Upcoming public sessions

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Deep dive

Everything inside this certification

The detail behind the headline. Read at your own pace. Each section answers a buyer question we get on discovery calls.

  • Domain 1: Fundamental principles and concepts of AI risk management
  • Domain 2: AI risk identification and assessment
  • Domain 3: AI risk measurement
  • Domain 4: AI risk mitigation, governance, and incident response
  • Domain 5: AI risk monitoring and continual improvement strategies
  1. Establishing an AI risk management framework
  2. Defining AI risk management objectives and scope
  3. Identifying and assessing AI-related risks
  4. Developing an AI risk mitigation and response strategy
  5. Defining AI risk evaluation and acceptance criteria
  6. Supporting compliance with industry frameworks and regulatory requirements
  7. Monitoring, reviewing, and continuously improving the AI risk management program

Certification Rules and Policies

  • Certification and examination fees are included in the price of the training course.
  • Participants will be provided with training course materials containing over 400 pages of information, practical examples, exercises, and quizzes.
  • An attestation of course completion worth 31 CPD (Continuing Professional Development) credits will be issued to the participants who have attended the training course.
  • Candidates who have completed the training course but failed the exam are eligible to retake the exam once for free within a 12 month period from the initial date of the exam.

Educational Approach

  • The training course combines theoretical knowledge with practical applications, using real-world examples to illustrate the identification and mitigation of AI risks.
  • The course includes various interactive activities, such as scenario-based exercises and multiple-choice quizzes, designed to deepen understanding of AI risk management principles.
  • Participants are encouraged to engage in discussions and collaborate during exercises and quizzes.
  • The quizzes are structured similarly to the certification exam, helping participants familiarize themselves with the exam format and key concepts.

FAQ

Buyers always ask

What is the difference between completing this training and becoming a certified Lead AI Risk Manager?+

Completing the five-day training course means you have attended and engaged with all the instructional content. It does not automatically confer a certification. To pursue the PECB Certified Lead AI Risk Manager credential, you must separately sit and pass the PECB Lead AI Risk Manager exam and then apply for the credential by demonstrating that you meet the relevant professional experience requirements set by PECB.

Cyber Academy provides the training programme. The exam and the certification process are administered by PECB independently, and candidates should consult PECB directly for current exam rules, credential requirements, and application procedures.

Which frameworks does this course draw on?+

The course applies two principal frameworks: the NIST AI Risk Management Framework and the EU AI Act. These are used throughout the programme to ground participants in internationally recognized approaches to AI governance, compliance, and ethical AI deployment.

Participants will learn to interpret requirements from both frameworks and apply them to practical risk management scenarios within their own organizations.

What types of AI risks are covered during the training?+

The programme addresses a broad spectrum of AI-related risks, including algorithmic bias, security vulnerabilities in AI systems, transparency and explainability concerns, ethical risks, and issues related to model drift and adversarial attacks. Both technical and governance dimensions of these risks are examined.

Is this course suitable for someone who works primarily in legal or compliance roles rather than a technical AI role?+

Yes. The course is designed for a mixed audience that includes legal and ethical advisors, compliance officers, and executives alongside technical professionals. The focus is on risk governance and management rather than on programming or model development, making it accessible to professionals who approach AI from a regulatory or organizational policy perspective.

That said, some familiarity with basic AI concepts will help participants follow discussions about specific risk types. Reviewing introductory AI materials before the course is advisable for those with limited technical background.

What competency domains does the PECB Lead AI Risk Manager exam assess?+

According to PECB, the exam covers five domains: AI risk principles, concepts, and regulations; AI risk management program and governance; AI risk identification and analysis; AI risk evaluation, treatment, and monitoring; and organizational learning and performance improvement.

For precise details on exam format, available languages, and sitting arrangements, candidates should refer to the official PECB List of Exams and Exam Rules and Policies.

Next step

Where this leads

Pillar guide

Read the full AI Act guide

EU AI Act readiness work for providers and deployers across the four risk tiers.

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