Overview

Data Privacy: Design Trust. Safeguard Rights. Lead Responsibly. Privacy is a foundation of trust and a competitive advantage. With AI adoption and regulatory scrutiny increasing, organizations need professionals who embed privacy into decisions, systems, and strategy. This course, from the Cyber Skills Academy at Macquarie University, offers a practical exploration of modern data privacy. Gain expertise to manage privacy risks. You will gain skills to: - Build and maintain data inventories to track and govern personal data. - Apply Privacy by Design principles to minimize risk. - Conduct Privacy and Algorithmic Impact Assessments for bias, fairness, and compliance. - Use de-identification techniques and understand re-identification risks. - Design and execute privacy incident response plans. This interdisciplinary course blends legal, technical, managerial, and ethical perspectives. Practice applying global frameworks like GDPR, CCPA, and ISO 27001 to real scenarios. This course is for data professionals, compliance officers, managers, or executives. It empowers you to lead responsibly, safeguard identities, and build a privacy-first culture. To succeed, learners should have a basic understanding of data concepts and organizational processes.

Syllabus

Data Inventories

This module covers building and maintaining data inventories, a core element for privacy programs. Learn how organizations collect, store, and process personal information. Understand risks from incomplete records. Classify data by sensitivity and legal obligations. Examine data flows across systems and third parties to identify vulnerabilities. Scenarios show how inventories support GDPR and CCPA compliance, transparency, and prepare for privacy by design and incident response. This module provides a structured map for responsible data management. *Tip: Practice data classification exercises to apply concepts.*

Privacy by Design

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.*

Privacy Impact Assessments

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.*

Algorithmic Impact Assessments

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.*

Data De-identification & Re-identification Risks

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.*

Privacy Incident Response

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.*