Alankrit Chona

Alankrit Chona

Builder & researcher · verifiable AI for security

Some work you can check in seconds. Defending a company sits at the other end: nobody can prove a dismissed alert was benign. Whether work can be checked decides how far you can trust AI with it, and that's the problem I work on. I led the Cyber Defense Benchmark, which scores whether AI agents can find known attacks hidden in raw logs. I also build AI agents for security operations as co-founder and CTO of Simbian.

Research

The Cyber Defense Benchmark

Alankrit Chona (lead author), Igor Kozlov, Ambuj Kumar · arXiv, April 2026

It's the first public benchmark for open-ended threat hunting by AI agents. The agent gets raw Windows event logs recorded from real attack techniques, with no alert and no guiding questions, and has to find the malicious events on its own. Passing takes 50% recall on every ATT&CK tactic. No model has passed yet.

You can't prove a log is clean, but you can score whether an agent finds the attacks you know are in it. That's how the benchmark turns hunting into work you can check.

What I write about

AI Systems & Agents

How agents work, where they break, and what it takes to trust them with work nobody can easily check.

Security & Verification

Why so much of defense resists measurement, and how to test a defense instead of trusting it.