Security operations center (SOC) analysts were already stretched to their limits, with teams often unable to investigate threats at the scale and speed needed to keep their organizations completely protected against modern threats.

The surprising emergence of the Claude Mythos Preview represents an inflection point when it comes to that issue. In pre-release testing, Anthropic found this frontier model so effective at discovering and independently exploiting vulnerabilities that the company decided not to release Mythos.

Whether Mythos ever gets a full release, it is a harbinger of a step function in capabilities with large language models that will likely push the limits of SOC analysts even further – with automated attacks coming at all hours, increased volumes, and potentially better-than-human sophistication.

One of the great promises of AI agents is that of the 24/7 worker, which could play a particularly powerful role in security. But what does this look like in practice, especially in an era of Mythos-type LLMs?

In this episode, in association with Dropzone AI, ITPro is joined by Edward Wu, founder and CEO at Dropzone AI, to unpack how agentic AI can automate alert triage

“End-to-end remediation in complex organizations requires human judgment, context, and accuracy, areas where AI agents are not yet close to automating.”

“AI agents can be thought of as 'foot soldiers' managed by human 'field generals' in the SOC, handling tasks like alert investigations while humans focus on complex issues.”

“The threat from LLMs is not overblown, but rather a culmination of a gradual increase in capabilities over the past few years, with Mythos being a significant threshold.”

“The future of the SOC will involve experienced people managing armies of AI agents, similar to software development teams where engineers manage multiple AI coding agents.”

“Models like Mythos fundamentally change the situation by enabling attackers to more economically find zero-day vulnerabilities and weaponize them into exploits, impacting vulnerability management teams first.”

Footnotes

https://www.dropzone.ai/

https://www.dropzone.ai/resources/customer-case-studies

https://www.dropzone.ai/resources/learning-guide