As AI transforms the digital landscape, the intersection of data privacy and machine learning has become a critical battleground for security professionals. In this episode, we dive into the core tenets of Privacy Engineering through the lens of the Certified Information Privacy Technologist (CIPT). From the seven principles of Privacy by Design to the deployment of Privacy Enhancing Technologies (PETs), learn how organizations are building privacy into the SDLC rather than "bolting it on" as an afterthought.

📘 What You’ll Learn:

The AI-Privacy Intersection: How personal data touchpoints in training, testing, and output trigger global privacy laws like GDPR.
Privacy by Design & Default: Incorporating privacy considerations from the early stages of AI architecture and the model training phase.
Privacy Enhancing Technologies (PETs): A deep dive into Homomorphic Encryption, Trusted Execution Environments (TEE), and Federated Learning.
Managing AI Risks: Strategies for mitigating data poisoning, membership inference attacks, and algorithmic bias.
The Role of the Privacy Technologist: Why the CIPT certification is becoming essential for navigating the complex regulatory landscape of the AI era.

🎧 Dive in to explore how AI can actually enhance compliance through automated data classification and anomaly detection.

Privacy Engineering in the AI Era A CIPT Perspective on Data Protection