Winning the AI Cyber Race: Verifiability Is All You Need

If AI offense can break it in hours, your SOC won’t stop it in weeks. Run continuous AI red teaming. Let attack results guide your roadmap. The best way to understand your verifier gaps is to let an attacker — even an AI one — show you.

3. Replay Harnesses (Detection Engineering) This is massively underinvested. Take your SIEM logs, your IaC configs, replay them offline. Test your detection rules against known attacks. Get binary outcomes: did the rule fire correctly or not?

SIEM = Suspicious Information Every Minute. Run the math: 1 million events per day, 1 in 100,000 malicious, a detector with 99% true positive rate and 1% false positive rate. Result? 9 true positives buried under ~10,000 false positives. Precision: 0.09%. If you train AI models on this signal, you get garbage.

After the NXS supply chain attack, Anthropic’s report on nation-states weaponizing AI, and DARPA’s AIxCC cyber challenge results, I’m convinced: spring is here, and offense is winning.

When evaluating AI security vendors, ask one question: How are you mechanically verifying your outputs? If they can’t answer clearly, walk away. Precision beats coverage every time.

AI security copilots promising “99% accuracy” are fundamentally misleading. Without crisp verifiers, they’re just serving you spaghetti alerts at machine speed.

AI will win where verification is easy. Defense will lose where verification is hard.

Are we entering an AI winter, or is this the beginning of springtime for AI in cybersecurity?