AI 101: What every GRC professional needs to know

You can't manage what you don't understand. If AI is a risk, and it is, then GRC professionals need a working knowledge of what AI actually is, how it works, and what makes it different from other technologies they've managed before.

The good news: you don't need to be a data scientist. You just need a solid enough foundation to ask the right questions, identify the right risks, and evaluate the answers you're given.

So let's start at the beginning.

Artificial Intelligence is, at its core, about creating systems that can perform tasks that usually require human intelligence. Learning, problem-solving, reasoning, decision-making — these are the capabilities AI is designed to replicate or augment. The history of AI stretches back to 1950, when Alan Turing first proposed the concept of a machine that could think. But the real explosion in usable AI didn't happen until the 2010s, when advances in computing power — especially graphics processing units, or GPUs — made it possible to train models on massive datasets in reasonable time.

Every AI system rests on three pillars: algorithms, hardware, and data.

Algorithms are the instructions that tell a machine how to learn. Unlike traditional software, which follows fixed rules, AI algorithms learn by finding patterns in data. They adjust themselves over time to get better at their task. The three major categories are supervised learning (where the algorithm learns from labeled examples), unsupervised learning (where it finds patterns in unlabeled data), and reinforcement learning (where it improves by trial and error, getting rewards or penalties for different outcomes).

Hardware provides the computing power to run those algorithms at scale. Modern AI depends on GPUs, Tensor Processing Units, and increasingly specialized chips designed to handle the enormous calculations involved in training and running AI models.

Data is the fuel. Without large volumes of high-quality, relevant data, no AI system can function. The quality and quantity of training data has a direct impact on how accurate and fair an AI system's outputs will be. This is also where many of the risks we'll discuss in later posts originate.

From a GRC perspective, what matters most is understanding the AI development lifecycle — the eight stages from defining a problem to ongoing monitoring. Each stage introduces potential risks: biased data collection, poorly designed models, insecure deployments, model drift over time. A strong GRC program watches all of these stages, not just the output.

AI isn't magic. It's math done at scale, guided by data. Understanding that simple truth is the foundation of managing it responsibly.