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AI & Data Protection compliance training course

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AI & Data Protection compliance training course

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AI & Data Protection compliance training course

Master the intersection of artificial intelligence and data protection law with a training program built for compliance professionals who need practical, enforceable answers. This course covers everything from legal bases and privacy-by-design to breach response and AI governance. Walk away with the tools, frameworks, and confidence to manage AI compliance across your entire organization.

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What you will learn:

You will learn how AI systems collect, process, and retain personal data, and how data protection principles apply at every stage. The course covers valid legal bases for AI processing, privacy-by-design techniques, and how to conduct Data Protection Impact Assessments for high-risk systems. You will also learn how to fulfill individual rights requests within AI environments, build transparent governance structures, and manage data breaches specific to AI pipelines. Additional modules address cross-border data transfers, cybersecurity controls, bias and fairness obligations, and emerging AI regulations. By the end, you will be equipped to design and lead a sustainable AI compliance program.

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Course content

8 Chapters • 38 LessonsDuration between 4 and 360 hours (you decide)

Chapter 1See details

Foundations of AI and Data Protection

Lesson 1 • Personal Data in AI Contexts
Defines personal data, sensitive categories, and pseudonymous data as they appear in AI pipelines. Clarifies scope so professionals know when protection rules apply.
Lesson 2 • Core Data Protection Principles
Introduces lawfulness, purpose limitation, data minimization, accuracy, storage limits, and accountability. These principles form the compliance baseline for every AI use case.
Lesson 3 • Roles and Responsibilities Overview
Distinguishes controllers, processors, and joint controllers in AI deployments. Establishes who bears which obligations before deeper compliance work begins.
Lesson 4 • What AI Systems Do With Data
Explains how AI ingests, processes, and outputs personal data. Grounds all subsequent compliance analysis in technical reality.

Chapter 2See details

Legal Bases for AI Data Processing

Lesson 1 • Legal Obligations and Vital Interests
Addresses processing required by law and life-safety scenarios. Clarifies narrow applicability so professionals avoid misusing these bases.
Lesson 2 • Special-Category Data Legal Bases
Identifies the additional legal grounds required for sensitive data processing in AI. Builds on the sensitive data definitions from Chapter 1.
Lesson 3 • Consent as a Legal Basis
Covers valid consent requirements, withdrawal mechanisms, and consent fatigue risks in AI contexts. Connects to the principle of lawfulness introduced in Chapter 1.
Lesson 4 • Documenting and Reviewing Legal Bases
Establishes processes for recording, reviewing, and updating legal basis decisions over the AI system lifecycle. Supports the accountability principle from Chapter 1.
Lesson 5 • Legitimate Interests and Contracts
Explains legitimate interest assessments and contractual necessity as alternatives to consent. Guides professionals in balancing organizational needs against individual rights.

Chapter 3See details

Data Protection by Design and by Default

Lesson 1 • Integrating Privacy Into Development Workflows
Embeds privacy checkpoints into agile sprints, model versioning, and deployment pipelines. Operationalizes design principles across cross-functional AI teams.
Lesson 2 • Privacy by Design Principles
Introduces the seven foundational privacy-by-design principles and their application to AI development cycles. Connects proactive design to accountability obligations from Chapter 1.
Lesson 3 • Data Minimization in AI Architecture
Applies the minimization principle to feature selection, training sets, and inference inputs. Reduces exposure by limiting data collection at the architectural level.
Lesson 4 • Anonymization and Pseudonymization Techniques
Covers technical methods for reducing re-identification risk in AI datasets. Builds on the pseudonymization concepts introduced in Chapter 1.
Lesson 5 • Default Privacy Settings in AI Products
Defines privacy-by-default requirements for user-facing AI features and system configurations. Ensures the most protective settings are active without user action.

Chapter 4See details

Data Protection Impact Assessments for AI

Lesson 1 • Scoping and Describing the Processing
Guides professionals in documenting the nature, purpose, scope, and context of AI data processing. Accurate scoping determines the quality of the entire assessment.
Lesson 2 • Mitigation Measures and Sign-Off
Defines controls to reduce identified risks and establishes approval and review workflows. Closes the assessment loop with documented accountability.
Lesson 3 • Risk Identification and Scoring
Identifies privacy risks to individuals and scores them by likelihood and severity. Produces the risk register that drives mitigation planning.
Lesson 4 • When an Impact Assessment Is Required
Identifies triggers for mandatory assessments, including systematic profiling, large-scale processing, and novel technologies. Builds on legal basis and design concepts from prior chapters.
Lesson 5 • Necessity and Proportionality Analysis
Evaluates whether processing is necessary and proportionate to its stated purpose. Applies legal basis and minimization principles from Chapters 2 and 3.

Chapter 5See details

Individual Rights in AI-Driven Environments

Lesson 1 • Erasure and Restriction in AI Models
Addresses the technical and legal complexity of deleting or restricting personal data embedded in trained models. Connects to minimization and design principles from Chapter 3.
Lesson 2 • Rights Landscape for AI Systems
Maps all individual rights—access, rectification, erasure, portability, objection, and restriction—onto AI processing contexts. Sets the scope for the entire chapter.
Lesson 3 • Rights Related to Automated Decisions
Explains the right not to be subject to solely automated decisions and the right to human review. Directly addresses AI-specific rights obligations.
Lesson 4 • Handling Access and Portability Requests
Covers procedures for locating, compiling, and delivering personal data held in AI systems. Addresses technical challenges of extracting data from complex models.
Lesson 5 • Building a Rights-Fulfillment Infrastructure
Designs the systems, roles, and escalation paths needed to handle rights requests at scale. Integrates with accountability structures from Chapter 1.

Chapter 6See details

Transparency, Explainability, and AI Governance

Lesson 1 • AI Governance Frameworks
Establishes internal governance structures including policies, roles, and oversight committees for AI. Operationalizes accountability across the organization.
Lesson 2 • Model Cards and Data Sheets
Introduces standardized documentation artifacts that capture model purpose, performance, and data provenance. Supports both internal governance and external transparency.
Lesson 3 • Communicating AI Use to Stakeholders
Develops communication strategies for employees, customers, regulators, and the public about AI use. Translates technical governance into accessible messaging.
Lesson 4 • Explainability Methods and Standards
Introduces technical and non-technical methods for explaining AI model behavior and individual decisions. Supports the automated decision rights covered in Chapter 5.
Lesson 5 • Transparency Obligations for AI
Defines what organizations must disclose about AI processing to individuals and regulators. Grounds disclosure requirements in the accountability principle from Chapter 1.

Chapter 7See details

Data Breach Management in AI Systems

Lesson 1 • Post-Breach Remediation and Review
Guides root cause analysis, control improvements, and lessons-learned processes after a breach. Closes the incident loop and strengthens future prevention.
Lesson 2 • Breach Detection in AI Environments
Identifies how breaches manifest in AI pipelines, including model inversion and data poisoning. Builds on data flow mapping skills from Chapter 4.
Lesson 3 • Notification Obligations and Timelines
Explains when and how to notify regulators and affected individuals, including required content and timing. Applies legal basis and accountability principles from earlier chapters.
Lesson 4 • Breach Containment and Initial Assessment
Covers immediate containment actions and the initial triage process to determine breach scope and severity. Speed and accuracy here determine notification obligations.

Chapter 8See details

Building a Sustainable AI Compliance Program

Lesson 1 • Vendor and Third-Party AI Risk Management
Establishes due diligence, contractual, and ongoing monitoring requirements for third-party AI providers. Extends controller-processor obligations from Chapter 1 to procurement.
Lesson 2 • Compliance Culture and Training Strategy
Develops organization-wide training plans and culture initiatives that sustain compliance behaviors. Converts program design into lived practice across all AI-involved roles.
Lesson 3 • Regulatory Change Management
Builds processes for tracking, assessing, and implementing emerging AI and data protection regulations. Keeps the compliance program current as the legal landscape evolves.
Lesson 4 • Compliance Monitoring and Auditing
Designs internal audit programs and continuous monitoring processes for AI compliance. Produces evidence of accountability required by regulators.
Lesson 5 • Compliance Program Architecture
Defines the structural components of an AI compliance program: policies, procedures, controls, and metrics. Integrates all prior chapter outputs into a unified framework.

Certification

Your valid completion certificate

This course is for you:

Data protection officers: managing AI tools without a clear compliance roadmap.
Privacy lawyers: advising clients on AI deployments that outpace existing guidance.
Compliance managers: building internal programs that now must account for AI risk.
HR and procurement professionals: evaluating AI vendors with personal data implications.
Risk analysts: assessing AI-related threats without a structured privacy methodology.
Legal operations specialists: translating AI governance obligations into enforceable policies.

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