RadarFirst Launches AI Incident Management So Companies Have Somewhere to Put the Problem

RadarFirst, a company best known for privacy and data governance software, has launched a product for AI incident management, which is the part of AI governance that handles what happens after something goes wrong.

That sounds dry. It is not. Most companies right now have no defined place to put an AI problem. A chatbot gives a customer a wrong answer. A model starts drifting. A vendor emails to say their system did something strange last week. Someone screenshots it, someone forwards it, and it disappears into a thread. The new RadarFirst product is a bet that this gap is big enough to sell into.

What the product actually is

Incident management is not new. Privacy teams have used it for years to track data breaches, run notification clocks, and prove they handled things consistently. The idea is simple: when something goes wrong, you log it, classify it, decide who needs to know, act, and keep a record.

RadarFirst is applying that same shape to AI. The pitch is that when an AI system misbehaves, you need a defined workflow rather than an improvised one. Who triages it. What counts as severe. Whether a regulator, a customer, or an internal risk committee needs to be told. How long you have to respond. What the fix was.

That is a real gap. Privacy incident tools exist because regulators demanded consistency. AI has fewer clear rules, but the operational problem is the same and arguably worse, because AI failures are often not obvious until someone complains.

Why this matters now

Three things are converging.

The first is regulation that assumes you are watching your systems after launch. The EU AI Act requires post-market monitoring for high-risk systems, meaning providers have to actively collect and review data on how the system performs once it is in use, not just at approval. That requirement is not optional and it is not a one-time exercise.

The second is that AI failures have real precedents. In 2024, a Canadian tribunal held Air Canada liable for a bereavement policy its support chatbot invented. The airline argued the chatbot was a separate entity. The tribunal disagreed. The lesson is that what your AI tells a customer can bind you. In 2021, Zillow shut down its automated home-buying arm after its pricing model misread a shifting market, costing roughly half a billion dollars. The model did not crash. It just drifted, and nobody caught it in time.

The third is that the risk frameworks companies already use, like the NIST AI Risk Management Framework, keep pointing at the same missing piece. The framework's measure and manage functions assume you have some way to detect problems and respond. Without an incident process, those functions are theoretical.

What this means for governance teams

If you work in AI governance or risk, this launch is a signal more than a solution. It says the market is starting to treat AI incidents as an operational discipline rather than a legal afterthought.

That has practical implications. If you are building a governance program, you should be able to answer a few questions without checking. When an AI system in production does something wrong, who is told first? What is the threshold for escalating to legal, to the board, to a regulator? How do you know the fix worked? If those answers live in someone's head, you do not have a process. You have a person.

The tool also matters because AI incidents rarely arrive neatly labeled. A model drift problem looks like a business metric moving. A chatbot hallucination looks like a customer complaint. A vendor issue looks like an email. An incident management system forces you to classify these as AI events and route them, which is how you find out you have a pattern instead of a one-off.

What to watch for

Two cautions. First, buying a tool is not the same as having a process. A workflow product with no agreed severity definitions and no named owners just produces well-organized confusion. Second, incident management is downstream. It catches problems after they happen. It does not replace pre-deployment review, monitoring, or the human checkpoints that stop a bad output from reaching a customer in the first place.

The companies getting this right tend to pair incident tracking with clear ownership, a small set of severity tiers, and a habit of reviewing trends rather than individual tickets. The tool is the container. The discipline is the work.

What to do

• Write down what counts as an AI incident at your company. Be specific: wrong customer-facing output, model drift past a threshold, vendor breach, unauthorized use of an AI tool. If it is not written down, it will not be reported consistently.
• Name one owner and one backup. Incident processes fail when everyone assumes someone else is logging it.
• Set three severity levels and what each one triggers. Level one is a note. Level three is a call to legal and a written record. Keep it short enough that people actually use it.
• Track two numbers from day one: how long from detection to mitigation, and how many open items are past their review date. Those two metrics tell you whether the process is real.
• Before you buy anything, run one real past AI problem through your draft process on paper. If it does not fit, fix the process first. A governance program, or a platform like autogovern.io, can help you keep the register, the owners, and the review dates in one place, but the definitions have to come from you.

More from our platforms

These sister platforms cover the parts of this problem that sit outside governance.

• Argus (argus.threatclaw.ai) records every trace an AI application produces and scans it for prompt injection, jailbreaks and data leaks, including the attacks hidden inside retrieved documents and tool results rather than in what the user typed. Governance decides what an AI agent is allowed to do. Argus shows what it actually did.
• ThreatClaw (www.threatclaw.ai) tracks the threat side of the same systems: 22 live intelligence feeds, exploitation predicted before it is officially confirmed, threat actor profiles, and detection rules you can deploy straight away. A control is only as good as the threat it is sized against.

Related reading:

• Why Checking Your Locks Once a Year Is Not Enough on ThreatClaw
• The Banking Rule That Can Stop AI Security Tools From Turning Rogue on ThreatClaw