I spend a lot of time studying how AI goes wrong.
Not theoretically. I read incident reports. I follow AI failures in the news. I evaluate AI model outputs daily as part of my work. I practice adversarial thinking through red team exercises. And I've spent the last year researching what separates AI deployments that work from ones that become cautionary tales.
The patterns are consistent. And most of them aren't technical.
The "Ship Fast, Fix Later" Problem
Software development culture has trained teams to move quickly. Ship the MVP. Get feedback. Iterate.
This works for most software because bugs are usually contained, rollbacks are straightforward, and users understand that software can have issues.
AI breaks this model.
When an AI system fails publicly, the failure can go viral before anyone detects it. You can't roll back reputation damage. And users hold AI to different standards than regular software. when AI does something harmful or embarrassing, it becomes a story in ways that a crashed app never would.
Look at the AI failures that made news in the past year. Almost none of them were sophisticated attacks. Most were simple cases of: someone tried something obvious, the AI did something it shouldn't have, and screenshots spread before anyone noticed.
What I See in the Incident Reports
I've been cataloging AI incidents. not just the headline failures, but the smaller ones that get disclosed in security advisories and vulnerability reports. Here's what shows up repeatedly:
No definition of "wrong"
Teams deploy AI without clearly defining what failure looks like. They know what they want the AI to do, but they haven't mapped out what it should never do. So when it crosses a line, there's no system in place to catch it.
Testing that matches training, not reality
AI gets tested with inputs similar to what it was trained on. But users are creative in ways training data doesn't capture. The gap between "works in testing" and "works in production" is bigger for AI than traditional software.
No kill switch
When things go wrong, teams scramble to figure out how to turn off the AI. The incident reports often mention delays between detection and shutdown because there was no clear process for disabling the system.
Monitoring that watches the wrong things
Teams monitor uptime and response times. They don't monitor output quality or safety violations. So the AI can be "working" from a technical standpoint while producing harmful outputs that nobody catches.
The Red Team Mindset
I practice red team thinking through platforms like HackTheBox, and I apply the same adversarial mindset when I evaluate AI systems. The core question is always: "How would someone break this?"
Most AI deployments aren't tested this way. They're tested by people who want them to work, using inputs designed to make them work. That's not how attackers think. It's not even how curious users think.
When I evaluate AI outputs, I'm not trying to make the system succeed. I'm trying to find the edges. where does it break? What inputs produce unexpected behavior? What would happen if someone tried to misuse this?
This isn't paranoia. It's the only way to find problems before users do.
What Actually Prevents Failures
Based on what I've studied, the organizations that deploy AI without drama share some characteristics:
They define failure before they build. They create specific lists of outputs that would be embarrassing, harmful, or legally problematic. These become test cases.
They test adversarially. Someone on the team actively tries to break the AI. Not just with edge cases, but with malicious intent. If your testing doesn't include someone trying to make the AI do bad things, you're not testing.
They monitor outputs, not just uptime. They have systems that flag concerning responses for human review. They track patterns that might indicate drift or abuse.
They have a shutdown plan. Before launch, they know exactly how to disable AI features, who has authority to do it, and what fallback users will experience.
They assume it will fail. Not if, when. Planning for failure isn't pessimism. it's the only realistic approach for systems that can behave unpredictably.
Why This Matters Now
We're in a window where AI adoption is outpacing AI security. Companies are rushing to ship AI features because competitors are shipping AI features. The incentive is speed. Security is friction.
That gap is going to close. either through regulation, through high-profile incidents, or through the market penalizing companies that deploy irresponsibly. The question is whether you're ahead of that or behind it.
I study this because I think someone needs to be paying attention. Not selling fear, not hyping capabilities. just watching what actually happens and trying to learn from it.
The patterns are clear. The question is whether teams will learn from other people's failures or insist on making their own.