AI Product Security: Building Strong Data Governance and Protection

This course aims to enable enterprise security professionals to develop strong data governance frameworks and security practices that protect sensitive data, ensure model integrity, and maintain compliance with global regulations. Learn how to secure AI data pipelines, implement zero-trust and least-privilege access models, and address emerging threats such as adversarial attacks and model poisoning. Join instructor Meghan Maneval to find out how to establish, manage, and evolve AI data governance and security policies that protect against risks while ensuring compliance and accountability.

Learning objectives
Design and implement robust data governance frameworks that ensure data security, privacy, and ethical use throughout the AI product lifecycle in compliance with international regulations.
Identify and mitigate security risks in AI data pipelines and model environments, including adversarial attacks and data breaches, using best practices for encryption, access control, and integrity monitoring.
Analyze internally developed and third-party AI tools for security risks, ensuring compliance with regulatory requirements and contractual obligations.
Apply continuous monitoring to security and governance frameworks to align with evolving regulatory standards and security threats, ensuring compliance and resilience over time.