Analyze, Create, and Secure Data with Zero Trust

Ever wondered why data breaches keep happening despite massive security investments? The answer lies in moving beyond perimeter defense to a comprehensive zero-trust approach that assumes breach and verifies everything. This Short Course was created to help Machine Learning and AI professionals accomplish enterprise-grade data security that protects against both external threats and insider risks. By completing this course, you'll master the critical investigative skills to identify why breaches occur, architect security systems that never trust by default, and systematically evaluate your defenses against the gold standards that regulators and customers demand. By the end of this course, you will be able to: • Analyze incident reports to determine root causes of data breaches • Create a zero-trust data security architecture • Evaluate security controls and practices against industry standards and compliance requirements This course is unique because it combines post-incident forensics with proactive architecture design, ensuring you can both respond to security failures and prevent them from happening again. You'll work with real breach scenarios, design authentication frameworks that eliminate implicit trust, and audit systems against SOC 2, NIST, and CIS benchmarks. To be successful in this project, you should have a background in enterprise security concepts, data governance principles, and basic understanding of compliance frameworks

• Effectively identify root causes of security incidents including policy gaps and design flaws
• Apply zero-trust architecture principles ensuring continuous verification for access
• Systematically evaluate security controls to identify compliance and operational risks
• Integrate forensics, proactive architecture, and continuous monitoring for sustainable data security

• Basic familiarity with cybersecurity concepts and terminology
• Readiness to practice through applied exercises or case-based work