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Codecademy

Enterprise Security: Leveraging AI to Protect & Validate User Data

via Codecademy

Overview

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Data protection principles are fundamental guidelines that ensure the security, privacy, and integrity of data. As organizations increasingly rely on data analytics to extract valuable insights, it becomes crucial to prioritize data protection. In this course, you'll learn how artificial intelligence (AI) can be leveraged to enhance data protection and validation. Explore the principles of data protection including data availability, data life cycle management, and information life cycle management, and discover key areas where AI can enhance user data protection and validation. Next, consider the potential benefits and risks of leveraging AI for user data protection and validation and discover how to protect sensitive data and AI models. Lastly, explore common AI security risks such as AI model attacks, data security risks, code maintainability, and supply chain complexity, and consider how data integrity measures and AI can work hand in hand to improve an organization's security posture.

Syllabus

  • discover the key concepts covered in this course
  • provide an overview of data protection and validation
  • list principles of data protection
  • differentiate between data protection and data privacy
  • identify key areas where artificial intelligence (AI) can enhance user data protection and validation
  • analyze the potential benefits and risks of leveraging AI for user data protection and validation
  • outline factors to consider when designing AI with data privacy
  • use an AI model to detect abnormal changes in user data
  • outline how to protect sensitive data and AI models
  • compare AI-powered and traditional methods of user data protection in action
  • recognize common AI security risks such as AI model attacks, data security risks, code maintainability, and supply chain complexity
  • outline how data integrity measures and AI can be used together to improve an organization's security posture
  • identify ethical considerations and potential bias when using AI for user data protection
  • formulate strategies for integrating AI into existing data protection and validation frameworks
  • ensure AI data model integrity
  • summarize the key concepts covered in this course

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