The Next Market Disruption: Agentic SOC

Predicting a market disruption is difficult, but the vast rewards of being correct make it worthwhile. Unfortunately, prediction becomes tougher when marketing teams start labelling everything as a "market disruptor". Much like the stock market, if something is being sold to you as “the investment of a lifetime”, it almost certainly is not.

Yet market disruptors do exist, and the organizations that identify them enjoy generational success. While there is no proven technique for spotting market disruptors, there are techniques that can point you in the right direction. 

By the end of this article, you will understand why agentic SOCs are not simply an incremental step beyond the AI SOC, but an evolutionary leap that will change the market.

Jensen Huang’s prescient bet on CUDA

In 2006, Nvidia released CUDA, a software layer that let programmers harness GPU power for any computational task, not just graphics.

Wall Street punished them for it. Investors saw the company wasting capital on an endeavor where no market existed. There was no customer demand. Yet Nvidia poured $10 billion into CUDA over a decade while their stock price collapsed.

At the time, Jensen’s commitment to CUDA scared investors and emboldened skeptics. We now know that he made the right call. 

In retrospect, it's easy to say "Of course Jensen Huang could predict that something like AI would arise, and the world would need easier parallel processing." Yet none of NVIDIA's competitors followed suit.

This indicates other chip makers either lacked Huang's vision, or their risk aversion prevented them from moving forward even when they guessed what was coming.

The secret: fulfilling roles before they're defined  

Jensen Huang’s secret for identifying a market disruption was understanding where the market was headed, and fulfilling a critical role. Huang calls these opportunities "zero-billion-dollar markets". These are markets that don't exist yet, but are worth billions once the world catches up to what's inevitable. 

How did he identify the opportunity? He recognized that CPUs alone couldn't handle the massive parallel processing demands of the future. He envisioned researchers, scientists, and eventually businesses needing more processing capabilities for performing advanced tasks. 

He couldn't predict the exact form of the demand, but he understood the fundamental physics: if you want to process enormous amounts of data simultaneously, you need a different architecture.

Today, we see Huang was anticipating the needs of the AI revolution a decade before it arrived. He had the vision in 2006 and could see where computation was headed. He envisioned the capabilities, the scale, and the applications, but not the ultimate catalyst (generative AI and large language models).

So when looking at agentic AI and cybersecurity, what can we discern about the future right now?

Predicting a disruptor in SecOps

Taking a page from Jensen Huang’s book, what will SecOps need to do in the future that it cannot do now?

The obvious answer is effectively integrate AI into operations. Our industry struggles with security issues related to AI. Until this problem is solved, we cannot fully capture its phenomenal productivity gains. 

Taking this thought further: what should AI in the SOC look like in the future? Will it be a sequestered assistant living in containment that simply parses logs and advises analysts on next steps?  

Probably not. Most envision AI having an active role in SOC operations, and taking commands via prompts, as LLMs do now. How can we check our assumption on the widespread adoption of AI in SecOps? Look no further than OpenClaw. 

As described by author Larassatti D., “OpenClaw operates as a self-hosted AI agent controlled through chat interfaces, combining natural language understanding with real-world task execution. Instead of interacting through a traditional dashboard, users communicate with OpenClaw through familiar messaging platforms, turning everyday chat into a command layer for automation.”

In other words, OpenClaw executes LLM prompts as real-world actions. It is not just an advisory chatbot that helps with coding or explains complex processes. It’s an agent where you instruct it to do something (manage tasks, use tools, project manage, etc.), and the AI does it. Even more telling, OpenClaw is being widely adopted in spite of being incredibly insecure, and vulnerable to cyberattacks.

If risky AI is being quickly adopted by people for personal use, that demand exists in the private sector as well. The only thing suppressing widespread adoption of operational AI are the risks involved with integrating it into operations. 

Now you know one disruptive technology that will change the future trajectory of SecOps: governable agentic AI that performs security operations.

Early adoption, big gains

LimaCharlie has developed an agentic AI security solution that safely implements operational agents into the security stack. Their platform is API-first, meaning every tool, service, and resource communicates and interacts in a standardized way. Agentic AI becomes one more resource on the LimaCharlie platform that naturally integrates into security operations.

To prevent unwanted AI behavior, LimaCharlie created Viberails. This agentic security technology sits in the execution path of AI operations and blocks unwanted tool calls or actions. If you don't want agentic operators to use specific tools or perform certain operations, you simply prohibit AI access via Viberails.

Everywhere else, where AI operations are deemed safe, security teams enjoy massive efficiency gains. Manual processes that take hours or days are reduced to minutes or seconds. Junior analysts gain capabilities that let them assume responsibilities of senior analysts. When threat actors unleash AI-engineered attacks, defenses respond at AI speed.

It doesn't take much foresight to see this is where security operations are headed in the near future. LimaCharlie simply had the right infrastructure model to deliver governable agentic AI security capabilities today.

Bet on innovation

When Jensen Huang went all-in on CUDA, he did not know precisely what others would do with it. He simply had faith that giving innovative minds the power of affordable parallel processing would lead to great things. 

He was right.

Likewise, LimaCharlie cannot predict what people will do with governable, accountable, and auditable AI operators. We simply have faith that the power to safely incorporate operational AI into the security stack will lead to great innovation. 

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