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Instructor guide

Workshop
In Progress

Module overview​

In this 4-week module, apprentices will learn essential skills to navigate the complex landscape of data security, privacy, and governance in machine learning. You'll learn to implement robust security measures, design compliant data governance strategies, and develop effective risk management approaches for ML projects. These capabilities will help you address critical organizational challenges, fostering trust in your ML systems and protecting your organization's reputation in an increasingly data-sensitive world.

CompetencyLearning objectivesKSBsSecurity Fundamentals in Machine Learning- Analyze the full spectrum of vulnerabilities across the ML lifecycle, including threats to confidentiality, authentication, non-repudiation, and service integrity.

  • Critically assess the security implications of different ML infrastructure choices, considering local, remote, and distributed solutions in terms of scalability, governance, and cost.
  • Design comprehensive security strategies that address vulnerabilities at each stage of ML model development, from inception through deployment.
  • Implement security-conscious practices within ML team to mitigate threats and risks to assets, data, and cybersecurity in ML systems.
  • Develop protocols for continuous security assessment and improvement throughout the ML lifecycle, adapting to evolving threats and changing system architectures.
  • K27: Understanding Cybersecurity Culture
  • S8: Mitigating System Risks Data Privacy and Governance in Machine Learning- Analyze comprehensive data and information security standards, ethical practices, and policies relevant to ML data management activities.
  • Critically assess the implications of various data governance frameworks on ML systems, considering regulatory requirements, data privacy, security, trustworthiness, and quality control.
  • Design ML data management strategies that ensure compliance with relevant regulations while maintaining data lineage, retention, and metadata management best practice
  • Design and implement a comprehensive data quality control framework tailored for ML pipelines, addressing issues of data bias, completeness, and consistency.
  • S11: Addressing Regulatory and Ethical Issues
  • K25: Applying Data Governance Frameworks Regulatory Compliance and Risk Management in Machine Learning- Analyze the landscape of legislation, regulation, and governance frameworks applicable to ML and AI, understanding their implications for safe and interoperable data use.
  • Develop risk assessment and mitigation strategies for ML projects, addressing both digital and physical supply chain vulnerabilities.
  • Design and implement comprehensive audit processes for ML systems that ensure compliance with industry regulations, standards, and organizational policies.
  • S32: Applying ML Standards and Principles
  • S17: Conducting Compliance Audits
  • S6: Documenting ML Asset Lifecycles
  • K6: Risks in New ML Deployments
  • K3: Identifying Security Vulnerabilities
  • S21: Monitoring Data Quality

Module breakdown​

TimingEvent (Links to Ariel Modules)PurposeWeek 1Module Kickoff WorkshopIntroduce the module, skills covered, and projectAsync unit(s):

In this unit, you’ll explore what it takes to design privacy-aware ML systems that meet both legal and ethical standards. You’ll learn how to interpret regulations like GDPR, apply governance frameworks such as AREA and SAFE-D, and create data strategies that protect people, ensure fairness, and support auditability.

Workshop 1: Designing Secure ML Systems: A Threat Modeling and Risk Mitigation LabThis workshop gives apprentices a hands-on opportunity to apply security principles to a realistic ML system. Apprentices will identify vulnerabilities across the ML lifecycle, map them to core security principles, and design actionable mitigation strategies. The instructor will guide a threat modeling demo, then support apprentices as they work in groups to build a Secure ML Plan for a logistics system. The session reinforces critical thinking, practical security design, and team-based problem-solving.Week 2Async unit(s):

  • Module 8 Unit 3: Regulatory Compliance and Risk Management in Machine Learning In this unit, you’ll bridge the gap between regulatory expectations and real-world implementation. You’ll learn how to anticipate risks, manage compliance proactively, and design processes that keep your ML systems not just innovative, but also secure, ethical, and legally sound.Workshop 2: Governance and Compliance Simulation: Building a Regulatory-Ready ML SystemThis workshop gives apprentices a hands-on opportunity to apply governance frameworks, regulatory requirements, and compliance practices to a realistic ML system. Apprentices will work in small groups to analyze an EdTech ML system under regulatory scrutiny, identify risks, align operations to governance frameworks (e.g., AREA, SAFE-D), and design key compliance artifacts like a risk matrix, audit checklist, and escalation workflow. The instructor will prompt apprentices to think critically about data use, model transparency, and accountability in ML operations.Module Wrap Up WorkshopIn this 45-minute workshop, you will recap what is covered in the Module, how they will apply those skills on the job, and what they will complete for their module project/milestone.Weeks 3-4Group Coaching Receive coach and peer support on the module projectProjectApply skills from the module to a real-world problem## How to prepare for live workshopsModule Kickoff Workshop####Workshop overview

In this 45-minute workshop, you will introduce apprentices to what is covered in the Module, how they can apply those skills on the job, and what they will complete for their module project/milestone.

By the end of this workshop, apprentices will be able to:

  • Identify the skills they will learn in the module and how they can apply them to their roles
  • Understand the elements of the module and how they fit together
  • Locate and review their module project/milestone

Delivery preparation​

  • Make a copy of the workshop slides
  • Read the speaker notes in the workshop slides to understand what to cover during this workshop.
  • Ensure you understand what will be covered in this module and how the elements fit together.
  • Review the module project/milestone overview provided below.

Apprentice prerequisites​

There are no apprentice prerequisites for Kickoff workshops.

Workshop 1: Designing Secure ML Systems -A Threat Modeling and Risk Mitigation Lab####Workshop overview

In this 1-hour workshop, you will review what was covered in async unit 1. Then, apprentices will apply core security principles to realistic machine learning systems. The session begins with a coach-led walkthrough of a threat model for an ML platform, highlighting vulnerabilities across data ingestion, model training, and API deployment. Apprentices will then work in teams to develop a secure ML plan for the PackSure system, identifying threats, mapping them to security principles, and proposing mitigation strategies. The workshop supports applied security thinking, collaborative problem-solving, and actionable planning across the ML lifecycle.

By the end of this workshop, apprentices will be able to:

  • Analyze vulnerabilities across the ML system lifecycle, mapping them to core security principles.

  • Design secure ML workflows that address identified risks, including infrastructure, API, and model-level threats.

  • Apply threat modeling techniques to a real-world ML scenario, identifying mitigation strategies at each stage.

  • Develop basic team protocols for monitoring, incident response, and continuous security assessment. Delivery preparation

  • Make a copy of the workshop slides here.

  • Read the speaker notes in the workshop slides to understand what to cover during the workshop.

  • Review async unit 1: Security Fundamentals in Machine Learning: Ariel link.

  • Review the Skills application demo guide.

  • Review the Skills application challenge guide.

Apprentice prerequisites​

Apprentices should complete async unit 1 before attending this workshop. However, if apprentices did not complete the required async unit, they are encouraged to attend.

Workshop 2: Governance and Compliance Simulation -Building a Regulatory-Ready ML System####Workshop overview

In this 1 hour workshop, you will review what was covered in async units 2 and 3. Then, apprentices will then take part in a group-based compliance simulation, working as the compliance team for LearnSmart—an EdTech ML system. They will identify relevant regulations, choose an appropriate governance framework, and design a compliance plan that includes a risk matrix, audit checklist, and escalation workflow. The workshop builds practical skills in applied compliance, collaborative risk planning, and audit readiness for real-world ML systems.

By the end of this workshop, apprentices will be able to:

  • Analyze how governance frameworks (e.g., AREA, SAFE-D) shape compliant ML data strategies and operations.
  • Apply regulatory requirements (e.g., GDPR, AI Act) to design compliant ML system practices, including data minimization, access control, and transparency measures.
  • Create compliance artifacts (e.g., risk matrices, audit checklists) that demonstrate readiness for external audits and regulatory reviews.
  • Design escalation protocols for responding to compliance breaches in ML systems.

Delivery preparation​

  • Make a copy of the workshop slides here.
  • Read the speaker notes in the workshop slides to understand what to cover during the workshop.
  • Review async units 2 and 3:Unit 2:** **Data Privacy and Governance in Machine Learning - Ariel link.
  • Unit 3: Regulatory Compliance and Risk Management in Machine Learning - Ariel link.

Apprentice prerequisites​

Apprentices should complete async units 2 and 3 before attending this workshop. However, if apprentices did not complete the required async units, they are encouraged to attend.

Module Wrap-Up Workshop####Workshop overview

In this 45-minute workshop, you will review what was covered in the module and how those skills can drive impact. Additionally, you will preview what apprentices are expected to complete for their module project/milestone.

By the end of this workshop, apprentices will be able to:

  • Synthesize key takeaways of the module
  • Identify areas of impact that they can apply their module skills
  • Understand the requirements of their module project/milestone

Delivery preparation​

  • Make a copy of the workshop slides.
  • Read the speaker notes in the workshop slides to understand what to cover during the workshop.
  • Review the Project overview provided below. Apprentice prerequisites

Apprentices should complete all async units and attend required workshops before the Wrap-Up module. However, if apprentices did not complete the required async units or attend previous workshops, they are encouraged to attend the Wrap-Up module.

Group Coaching####Session overview

In this 1 hour session, you will:

  • Review the project requirements
  • Describe how apprentices can ensure they pass the KSBs associated with the project
  • Pick 1 of the below discussion options:Lead a round of project presentations for all apprentices to share their project progress.
  • Lead apprentices in a reflection surrounding how to apply their skills within their roles and organizations.
  • Pick 1 of the below review/feedback options:Facilitate peer review of the project presentations and provide your own review of the projects.
  • Facilitate peer feedback on application opportunities and blockers.

Coach preparation​

  • Make a copy of the workshop slides.
  • Read the speaker notes in the workshop slides to understand what to cover during the workshop.
  • Review the Project overview provided below.
  • Watch the how to create groups in the Ariel module video. Only create the group for your group coaching session when the session has started and you know who will be in attendance.

Apprentice prerequisites​

Apprentices should have made some progress on the project/milestone in order to actively discuss with their peers. Encourage apprentices to attend Group Coaching whether or not they have made progress on their project/milestone. If they have not made progress, they will hopefully be inspired by some of their peers.