By now, we all know generative AI (genAI) as the inescapable “sparkly star” of our digital lives. From email drafting to workflow automation, genAI is always a click away. ChatGPT and its peers pulled AI out of the labs of machine learning engineers and data scientists and thrust it into the hands of millions of people. However, it’s worth remembering: AI didn’t begin with ChatGPT.
Long before genAI made headlines, predictive AI (predAI) — sometimes simply called machine learning — was already hard at work, making mission-critical decisions in industries where precision, speed, and trust matter most. And that’s exactly why securing it remains so vital.
I speak from the trenches. Back in 2016 — years before Claude and ChatGPT became household names — I had the privilege of working on IBM’s Watson for Cyber Security, an AI assistant trained specifically on the language of cyber threats. We fed Watson thousands of cybersecurity documents, painstakingly labeling them so the system could learn to parse industry-specific language. For example, Watson had to be taught that a “honeypot” was not a jar of honey for bears, but rather a decoy server designed to lure in attackers. That training process was one of the first times I saw the immense power of predAI applied to real-world security: models built to interpret, predict and act in domains where human analysts were drowning in data.
PredAI Is Everywhere and Growing
Watson wasn’t an anomaly. PredAI is not outdated, nor is it a niche technology waiting to be replaced by genAI. PredAI is the engine room of modern enterprise. Large organizations today run thousands of predictive models, quietly fueling everything from fraud detection to clinical trial design. In financial services alone, machine learning-driven fraud detection is projected to save banks over $10.7 billion globally by 2027. In pharmaceuticals, the global predAI market is scaling rapidly, with revenue projections climbing into the hundreds of millions by the early 2030s, driven by AI-assisted drug discovery and diagnostics.
Chances are, if your business relies on data, you already have a hundreds or thousands of predAI models humming away behind the scenes, models that influence who gets a loan, which supply chain route is chosen, or which molecule advances in a billion-dollar drug trial.
Ungoverned PredAI Models Lead to Bias, Breaches and Bad Decisions
Here’s the catch: predictive models don’t govern themselves. They’re a little like garden hoses left running unattended, if they’re tangled, misdirected, or forgotten, the damage can be significant. At best, you wind up with wasted water. At worst, you wake up to a flooded basement; or in enterprise terms, a breached system, corrupted analytics, bad decisions, or regulatory non-compliance.
Here are some of the ways that ungoverned models can cause risk and harm in organizations:
- Bias and discrimination: Without checks, models can embed and amplify biases hidden in training data. The result? Financial institutions approving loans with discriminatory patterns, exposing themselves to lawsuits and reputational damage.
- Model drift: Over time, predictive models degrade as real-world data shifts. What was once accurate can turn dangerously misleading. In pharmaceuticals, this could mean flawed patient recruitment for clinical trials, delaying life-saving drugs and eroding trust with regulators.
- Opaque decisioning: Many organizations cannot explain why a model made a particular call. That lack of auditability isn’t just a technical oversight, it can quickly escalate into a serious compliance failure, particularly under regulations like the EU AI Act or U.S. financial disclosure requirements.
- Security vulnerabilities: Machine learning models can themselves be attacked — through data poisoning (feeding them bad training inputs) or adversarial manipulation (crafting malicious inputs that fool the model). If you don’t know what models you have or how they’re secured, how can you defend them?
In short, ungoverned predAI models don’t just create operational risks — they open the door to systemic failures that can undermine business strategy, regulatory standing, and customer trust.
Building PredAI Governance: Inventory, Control and AIBOMs
Strong model governance isn’t a nice-to-have, it’s what keeps predAI reliable, secure and compliant over time. The good news is, getting started doesn’t have to be overwhelming. Starting with Inventory, Control and AIBOMs is a great way to put the right foundations in place.
Build Your Model Inventory
You can’t govern what you don’t know exists. Start by cataloging every predictive model across the organization. Enrich the inventory data with critical security information such as: who owns it, model provenance, reputational risk, what data feeds it and what business problem and tools it supports. Treat model inventory the same way you would a hardware or software asset inventory. Automated discovery tools can help uncover “hidden” models, both on-prem and in the cloud, living in notebooks, pipelines, or shadow projects.
Put Governance and Controls in Place
With visibility in hand, the next step is control. Define policies for training data quality, fairness standards and safety criteria. Set up approval gates before deployment to stress-test and red-team models for bias, drift, privacy leaks or misbehavior. And don’t stop at go-live. Continuous monitoring at run-time is essential to catch degrading performance, anomalous outputs, guardrail jailbreaks and toxic prompt triggers before they cause harm.
Manage Models with an AI Bill of Materials (AIBOMs)
Finally, create an AIBOM for each model — a detailed “ingredient list” that documents data sources, features, training code, versions and dependencies. Just as software BOMs help track components and vulnerabilities, AIBOMs give you the ability to trace issues back to their root. If a training dataset turns out to be tainted, or a library patch breaks accuracy, your AIBOM provides the roadmap to investigate and fix quickly.
GenAI Shines while PredAI Keeps the Lights On
GenAI commands the spotlight. But behind the scenes, it’s predAI and machine learning models that quietly keep the lights on: enabling fraud detection, predicting retail trends, optimizing logistics, and powering decisions that drive revenue, reduce risk, and keep businesses running every single day.
Securing genAI is vital, and so is safeguarding the machine learning models that power both genAI and predAI. Strengthening their governance ensures that value continues to grow. By inventorying models, putting thoughtful controls in place, and managing them with AIBOMs, organizations can build a comprehensive and balanced AI security program that supports both innovation and trust.