Unlock the complexities of managing data in the era of AI.
This course unpacks how artificial intelligence reshapes data handling, transforming it from simple file storage into learned behaviors that can’t be “forgotten.”
Explore critical topics like personal data exposure, irreversibility, compliance challenges, and indirect liability. Learn why traditional data governance falls short against AI and what steps you need to take to safeguard sensitive information, all while staying compliant with evolving privacy laws like GDPR and HIPAA.
In this lesson, we will discuss what makes Artificial Intelligence different, and how it doesn’t operate like a spreadsheet, a folder of files, or a database table — and that changes everything about how we handle information. In traditional systems, data is stored in identifiable blocks, which you can access, update, or delete. AI, particularly machine learning models, doesn’t store data in blocks. Instead, it digests the data, learns from it, and reconfigures its internal structure — its weights and patterns — to reflect that learning. What you get is not a file saved in memory, but a behavior shaped by experience.
You’ve heard about AI everywhere—chatbots, self-driving cars, even Netflix recommendations. But how does it really work?
In this lesson, we will investigate how AI works. Let’s break it down in plain English.
By the end of this lesson, you will understand the risks associated with inadvertently feeding Personally Identifiable Information (PII) into AI systems, the potential consequences, and how to implement safeguards for protecting user privacy.
In this lesson, we will explore the "Irreversible" nature of AI models. In human memory, forgetting is a natural, unintentional process— same for AI, intentional forgetting is nearly impossible. Once data enters an AI model’s training pipeline, it is no longer just a discrete piece of information; it becomes an abstracted pattern, deeply coupled into the model’s behavior. Unlike a database where entries can be deleted, AI models do not "forget" in a meaningful way. Attempting to remove specific data post-training risks corrupting the model’s performance, assuming removal is even technically feasible.
By the end of this lesson, you’ll understand the risks of feeding Personally Identifiable Information (PII) into AI systems, the consequences of doing so, and how to implement safeguards that preserve privacy and regulatory compliance.
In this lesson, we will answer the billion-dollar question:
"I'm a company with customers and active contracts. I'm sending customer data to AI, AI 3rd-party decided to use it to train. Who should be found responsible?"
AI Glossary of important definitions in AI.
