Privacy must be embedded into products, services, and systems from the start. This module introduces Privacy by Design and its seven principles, shifting focus from reactive responses to proactive safeguards. Learn how default settings, system architecture, and lifecycle planning minimize risks while delivering value. Case studies show the costs of late privacy integration versus the advantages of designing with trust. Understand how Privacy by Design acts as a strategic advantage, strengthening resilience and customer loyalty. *Tip: Analyze a product you use and identify its privacy by design elements.*

Systems can carry risks in practice. This module introduces Privacy Impact Assessments (PIAs), a structured process for addressing privacy concerns before they become problems. Learn how PIAs originated and why regulators view them as vital for high-risk projects. Gain practical guidance on scoping assessments, engaging stakeholders, evaluating risks, and proposing mitigation strategies. Examples show how PIAs strengthen governance, reduce compliance failures, and build public trust. PIAs enable informed choices and demonstrate accountability. *Tip: Practice scoping a PIA for a hypothetical project.*

Artificial intelligence systems influence decisions, requiring oversight. This module explores Algorithmic Impact Assessments (AIAs), a framework for identifying risks, biases, and unintended consequences in automated decision-making. Examine how AI systems can amplify human bias and compromise transparency. Understand why regulators demand accountability. Case studies show how flawed algorithms harm individuals and erode trust. Learn how AIAs provide a systematic way to detect and minimize harms. This module equips you to evaluate technical and ethical dimensions of algorithmic systems for responsible deployment. *Tip: Analyze a real-world AI system for potential biases.*

Sharing data is vital for innovation, but not at the cost of privacy. This module introduces data de-identification principles and techniques, from anonymization to differential privacy. Explore the balance between data utility and reducing re-identification risk. Understand why this balance is complex. Examples of re-identification attacks show how "anonymized" datasets can expose individuals. Learn to apply de-identification techniques and assess data vulnerability. Determine safeguards needed for responsible data sharing. *Tip: Research different de-identification methods and their limitations.*

Breaches are inevitable; effective incident response protects privacy. This module guides learners through steps to prepare for, detect, and respond to privacy incidents. Learn to build and test response plans, establish clear responsibilities, and engage regulators and affected individuals. Case studies show how poor responses amplify damage, while strong responses limit harm and preserve trust. Explore simulations, lessons learned reviews, and continuous improvement to strengthen resilience. *Tip: Develop a basic incident response plan for a hypothetical data breach.*