Defending at Machine Speed: Predict, Adapt, Stop Agentic Attacks

AI powered attacks don’t follow a script.

They continuously adapt, testing new techniques, pivoting around defenses, and exploiting the unique weaknesses of every environment. Multiple benign alerts can indicate a broader, more complex attack. Traditional defenses, though, were never designed to keep pace with attackers that learn and evolve at machine speed. But what if your security platform could defend using a defending agent that’s customized to your environment, continuously predicting multistage attack chains, and creating customized protections before attackers strike? That is the power of Cato Agentic Threat Prevention. Cato Agentic Threat Prevention begins with knowing what’s on each customer’s network. Cato shared context notes hundreds of fields from all network assets and traffic.

All of this information is used not only to make more informed security decisions, but security predictions. Attackers used to move at human speed, but now attacks are agent enabled, which means they’re adaptive and automated, so defenses must be as well. The Agentic Threat Prevention screen shows AI findings and reasonings in real time and points to the resulting behaviors, controls, and events. Here, Agentic Threat Prevention has noticed a host for the first time attempting to download PS tools, which are used in remote process execution.

Agentic Threat Prevention predicts the use of these tools as lateral movement and automatically restricts both the lateral movement and the execution of the tools. But agentic attackers are adaptive, and this attacker quickly pivots to install OpenClaw on the host. With the context of the environment in mind and having seen previous suspicious tool downloads earlier, Agentic Threat Prevention predicts additional tool usage by this attacker and instantly blocks the ability to download further OpenClaw installations, GitHub skill installations, and access to other malicious tools via NPM.

Traditional defenses would normally take hours or days to correlate, identify, and react to this attack.

By then, this attack would have been completely successful. But in the timeline of events, we see that Agentic Threat Prevention was able to analyze, predict, and block this agentic attacker autonomously within one second to prevent the attack from completing.

That is the kind of machine speed defense required to defeat agentic attacks. The new protections are enforced throughout the entire organization because the Cato global platform works from a single policy. Users don’t have to worry about ensuring protection reaching the right places because it’s applied globally. Frontier AI gives attackers new levels of speed, adaptation, and scale. To defeat an agentic opponent, organizations need agentic defenses. Cato agentic threat prevention helps stop AI driven attacks by using customer specific context to predict attacker behavior, adapt protections automatically, and enforce prevention globally automatically platform. The result is prevention at machine speed built for the pace of modern attacks.

To learn more about how Agentic Threat Prevention could help secure your network, visit us at catonetworks.com.