AI & Data Protection Compliance Training Course

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 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 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 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 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 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 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 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.