How AI Works and Why It’s Different?

“I removed her pictures, erased her messages, deleted her data — but I can’t get her out of my mind.”

This popular movie’s cliche about memory and emotion in all old movies captures something fundamental about how modern AI systems handle data.

AI: Not Just Another Database 

Artificial Intelligence 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.

The Language Analogy: How AI Learns

Think of it like language. Once you learn a new word and use it in context, you don’t just remember a single definition — you start using it in sentences, associate it with feelings, visuals, even memories. That’s similar to how a model learns. The information becomes entangled in a vast mesh of interconnections. This is why removing a specific piece of training data from an AI model isn’t like deleting a line in Excel — it’s like trying to remove a single word from someone’s vocabulary without affecting the rest of their language skills.

The Complexity and Importance of AI Governance

This foundational difference is what makes AI governance so complex and critical. It’s not just about keeping systems secure or data private — it’s about understanding the new reality that data, once absorbed, can’t be simply “unseen.” This lesson will lay the groundwork for understanding why the rules of the game change when AI is involved — and why traditional data management strategies fall short.

Before AI: The Simplicity of Data Deletion

Let’s dive deeper into data deletion for example, a legal requirement in GDPR, California Act, HIPAA and many more privacy laws. Before the rise of AI-driven systems, data deletion was a clear-cut process. You could erase a customer’s presence by removing database records and deleting associated files from storage — a clean break, with no digital trace left behind.

The Impossibility of Unlearning: Data as “Memories” in AI 

Now, with AI in the picture, that simplicity is gone. These systems operate on models that absorb data like memories, weaving them into a complex web of interlinked concepts and patterns. Just as you can’t pluck a single thought from a human mind, no surgery can remove chunks of brain to erase an elective memory; Same with AI, you can’t extract one specific piece of training data from a neural network without potentially damaging the entire model.

Latent Knowledge: Data’s Shadowy Presence in AI Models

Once something slips into the training dataset of a machine learning model, it becomes part of the model’s latent knowledge — a shadow embedded deep in its neural fabric.

Real-World Implications: HIPAA and GDPR in Jeopardy

Picture this: a hospital shares sensitive patient data with a third-party AI provider. Once that data is used to train the model, there’s no practical way to unlearn it — no delete button, no rollback, no undo. This isn’t just a technical inconvenience; it’s a direct violation of regulations like HIPAA, GDPR, and other critical privacy frameworks. The data, now entangled within millions of model parameters, becomes an untraceable phantom, permanently retained. 

Looking Ahead: Navigating Data Governance in the AI Era

In the next lessons, we will explore the challenges of data governance in the age of AI — from understanding how data becomes embedded in machine learning models, to navigating compliance with global privacy laws like GDPR and HIPAA. You’ll learn how AI’s memory differs from traditional storage, why deletion is no longer a guarantee, and what this means for responsible data practices in modern organizations.