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Wednesday, November 11, 2026

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Anthropic and Binance show why the speed of containment matters. Cathedral, an AI security startup, says its agents found routes into Anthropic's internal messages and employee records by exploiting third-party business software during a security test. Anthropic acknowledged limited sales, HR and IT exposure, but says its model weights and code were safe, its monitoring caught some activity, and access was cut the same day. I lean toward Cathedral on the significance of that exposure. Internal information can help attackers impersonate colleagues and target people with privileged access. The damage can extend beyond the data initially exposed. Supply-chain risk becomes harder to contain as more tools and agents gain access to company systems. Tighter permissions should help. Building more tools internally might reduce vendor exposure, but it also transfers maintenance and security work to your own team. Binance's October 29 hack involved a different suspected entry point. Its preliminary investigation says a compromised DevOps engineer's laptop may have let an attacker reach an internal service and submit unauthorized withdrawals. Binance estimates a $6.3bn impact, with roughly $3bn frozen so far. An attacker using a trusted employee's access can cause damage without any malicious insider. And TRM Labs says most of the funds leaving Binance moved in about 20 minutes. That is why I think shift-right security becomes increasingly important: detecting and stopping attacks while systems are running. Security teams need to recognize lateral movement, where an attacker moves between accounts or machines, and contain it before the attacker reaches systems that can cause much greater damage. This is part of why we like Palo Alto Networks ($PANW). Cortex combines network, endpoint and cloud data with response controls, including the ability to isolate compromised devices. Connecting an investigation to an action that stops the attack matters when the attacker can move this quickly. It also makes cybersecurity an interesting market for premium-speed AI inference. An AI security agent needs to investigate accurately and act quickly, with current data and permission to intervene. Where the model's reasoning time is delaying containment, paying even 100x more per inference call could make economic sense if the reduction in expected losses exceeds the extra cost. Faster inference becomes valuable when it lets the defender stop an attack sooner without sacrificing accuracy.

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