The world of cybersecurity is relentless. Threats continue to evolve, data keeps on growing, detection rules continue to get stale, and finding enough skilled engineers is an impossible task. 15 years ago, we had been promised that technology would help even the odds. SOC Automation, in particular, was supposed to be our silver bullet. SOARs promised the moon but grossly under delivered. They burst onto the scene promising to streamline operations, automate daily tasks, and reduce manual work. SOAR under-delivered with predefined playbooks that were too narrow, very hard to maintain and even harder to scale. . Building and maintaining these required expensive human engineering resources. They could handle the basic, standard daily tasks but fell flat against real-world use cases and sophisticated attacks. That is where humans are still required to live. The investigation logs can be collected but the queries were the same. 

 The big disruption that was promised was not delivered. Many of the most common "playbook" tasks, like blocking a phishing address or resetting a password, are things the tools themselves can already do.  

How to use LLMs for Cybersecurity?  

There is a new kid on the block that is dominating the conversation, Large Language Models (LLMs). We're talking about seriously powerful AI, designed to understand and process human language on a massive scale. While AI and machine learning have been part of cybersecurity for decades. Spam filters worked wonders. AI based attack detections on endpoints worked very well. What are the LLMs doing now? They're not just automating tasks; they're fundamentally changing what is possible.  

AI Agents, particularly AI SOC Agent sifts through mountains of logs in L1 SOC, help run structured and unstructured hunting campaigns, help with vulnerability scans, or even fill out parts of those endless security questionnaires. Humans guide AI to dive into the really complex investigative and strategic work. That alone boosts efficiency and can even slash costs, potentially saving millions on data breach expenses and drastically cutting down the time it takes to find and contain breaches. But the true "redefinition" comes from LLMs' uncanny ability to go beyond simple automation. These models are brilliant at processing vast amounts of complex data, identifying patterns, and adapting over time in ways older systems simply can't. 

We're already seeing this in action at Simbian. LLMs are helping with triage, investigation, autonomous response in the SOC, automating vulnerability discovery, in the vulnerability management and streamlining security questionnaires, powering faster incident response (like isolating affected systems), enabling AI-powered remediation with custom instructions, and even assisting with patch management and penetration testing. 

 Generative AI, specifically, is supercharging threat intelligence and automating complex processes. There's even work underway to use AI for dynamic deception tactics to mislead attackers.  

Implement AI SOC Now!

The biggest reality check? The attackers are using AI too. Attackers are leveraging AI to generate code to exploit zero-day vulnerabilities. Social engineers are crafting incredibly convincing phishing emails with tools like ChatGPT. Attackers are using machine learning for sophisticated password cracking and CAPTCHA bypassing. We're staring down the barrel of autonomous AI agents developed by malicious actors that could identify vulnerabilities, plan attacks, and evade defenses all on their own. So, the need is urgent. This time fire can be fought fire! 

AI SOC Analysts & SOC Analysts 

Finally, there's the human element. While AI can eliminate human errors in similar tasks, our training efforts are yielding amazing results in the rapid adoption of AI SOC. Both seasoned pros and new recruits appreciate the value AI brings. Average becomes better. Good becomes great. Great can do much more and also train AI.