I used to think Adversarial AI was just another buzzword cooked up by security vendors to sell more software. A solution looking for a problem. I couldn’t have been more wrong.
It was 2022. We were in the middle of closing a major round for RemoteTeam. The product was flying, customers were happy, and our metrics were all up and to the right. Then, one Tuesday morning, everything went sideways.
Our core AI model, the one that powered our automated payroll and compliance engine, started making… weird decisions. It was misclassifying employees, assigning incorrect tax jurisdictions, and spitting out payroll calculations that were off by tens of thousands of dollars. For a company handling people's money, this was a code-red, all-hands-on-deck, DEFCON 1 situation.
We spent the next 72 hours in a frantic war room, fueled by stale pizza and the sheer terror of blowing our funding round. At first, we thought it was a bug in our code. A classic engineering screw-up. But after tearing apart our entire codebase and finding nothing, we started looking at the data. And that’s when we found it.
Someone had been feeding our model subtly manipulated data for weeks. Tiny, almost imperceptible changes to employee profiles and company information. A single pixel changed in a scanned document. A weird character hidden in a text field. To a human, it was invisible. To our AI, it was poison. It was a classic adversarial attack, and it almost destroyed my company.
What is Adversarial AI, Really?
Forget the academic papers and the jargon. Adversarial AI is about one thing: making AI do something it wasn’t designed to do. It’s about finding the blind spots, the weird edge cases, and the hidden assumptions in a machine learning model and exploiting them.
Think of it like this: you can train a self-driving car to recognize a stop sign. It sees a red octagon with four white letters, and it stops. But what if I put a small piece of black tape on that sign? You’d still see a stop sign. But the AI might see a 45 MPH speed limit sign. That’s an adversarial attack.
These attacks fall into a few main buckets:
- Evasion: This is what happened to us. You feed the model bad data at decision time to get the wrong output.
- Poisoning: You corrupt the training data itself, building a backdoor into the model from day one.
- Model Stealing: You probe the model with a bunch of inputs to figure out how it works, then you create a copy of it for yourself.
For years, this was all theoretical. Fun stuff for PhDs at Stanford and Google to write papers about. Not anymore. With the explosion of generative AI and the deployment of models into every corner of our lives, the attack surface has grown a thousand-fold.
My Framework: The Paranoid Founder's Guide to AI Security
After that near-death experience, I got paranoid. I talked to dozens of experts, from red teamers at Google to cybersecurity founders in my portfolio. From those conversations, I built a framework. It’s not perfect, but it’s a hell of a lot better than what most startups are doing (which is, to be blunt, nothing).
1. Assume You're Already Compromised
This is the most important mindset shift. Don’t think about how to prevent attacks. Think about how to detect them when they happen. Your model is already making bad decisions; your job is to find them before your customers do.
- Monitor Everything: Log every prediction your model makes. Log the inputs, the outputs, and the confidence scores. Build dashboards. Set up alerts for anomalies. If your model suddenly starts classifying everything as "cat," you should know about it in seconds, not days.
- Human-in-the-Loop: For your most critical decisions, don’t let the AI fly solo. Have a human review a random sample of its outputs. This is your last line of defense. At RemoteTeam, we started doing this for 1% of all payroll calculations. It was expensive, but it was cheaper than going out of business.
2. Red Team Your Own Models
You can’t wait for the bad guys to attack you. You have to attack yourself first.
- Hire Hackers: There are now firms that specialize in red teaming AI models. They will treat your AI like a black box and try to break it, just like a real attacker would. I’ve invested in a few of these companies, and the stuff they can do is terrifying.
- Adversarial Training: This is a more technical approach, but it’s powerful. You generate your own adversarial examples and then retrain your model on them. You’re essentially teaching the model to be more robust by showing it what the attacks look like.
3. Build a Moat Around Your Data
Your training data is your crown jewels. Protect it accordingly.
- Data Lineage: Know where your data comes from. Every single data point. If you’re scraping the web or using a third-party dataset, you’re inheriting their security risks.
- Input Sanitization: Treat all input as hostile. Validate it, sanitize it, and reject anything that looks even slightly suspicious. This is basic web security 101, but it’s amazing how many AI teams forget it.
This Isn't the Future, It's Today
Still think this is science fiction? In 2020, researchers showed they could fool a Tesla’s autopilot into changing lanes by placing a few small stickers on the road. Others have tricked facial recognition systems with specially designed glasses.
This isn’t about some far-off dystopian future. It’s happening right now. And as we hand over more and more of our lives to AI, the stakes are only going to get higher.
Ignoring adversarial AI is no longer an option. It’s a liability. If you’re building an AI company, you need to be thinking about this from day one. Be paranoid. Assume the worst. Because one day, it might just save your company.
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.