Snehal Antani
Co-founder and CEO of Horizon3.ai, an autonomous cybersecurity company. Previously served as CTO of JSOC, CTO at Splunk, and CIO at GE Capital.
AI Accelerates Exploits in the Patch Gap, Not Full Cyberattacks
Horizon3.ai chief executive Snehal Antani argues that frontier AI models are accelerating established offensive tasks—especially turning software patches into exploits while organizations remain unpatched—rather than enabling autonomous, end-to-end cyberattacks. But he says current agents are also unusually brittle in defended networks, clicking planted decoys far more often than human testers do. The defensive advantage, he argues, depends not on a “magic” AI tool but on fast patching, compensating controls, deception, containment and rehearsed incident response.
Frontier Labs Treat Recursive Self-Improvement as a Near-Term Control Problem
AI in the AM’s first weekly highlights edition argues that the important AI signal in early June was not a model launch but a pattern: frontier labs are treating AI-accelerated AI research as near-term, while their main control strategy remains AI systems monitoring other AI systems. Nathan Labenz presents that as a safety concern, and the source contrasts thin recursive-self-improvement plans with OpenAI’s more concrete tax-agent example, where the harness improves from practitioner corrections rather than from changes to model weights. The through-line is that value and risk are moving into the layers around the model: tax harnesses, private data and expert judgment in cyber, real-time moderation guardrails, and safety architecture in mental-health deployments.