In early 1900s, factory owners bolted the new electric dynamo onto their old, central-shaft-and-pulley systems. They thought they were modernizing, but they were just doing a “retrofit.” The massive productivity boom didn’t arrive until they completely re-architected the factory around the new unit-drive motor (metaphor source).

Today’s AI agent slapped onto a broken, 1990s-style SOC process stack is the same. Everyone is chasing the shiniest LLM or agentic system to “AI-enable” their existing, often sclerotic, processes. The result is an AI retrofit that instantly slams into deeper, systemic bottlenecks.

So, how to tell if your SOC is AI ready?

Five Pillars of MODEL OF AI-ready SOC:

1. SOC Data Foundations.
2. SOC Process Framework and Maturity
3. SOC Human Element and Skills
4. Modern SOC Technology Stack
5. SOC Metrics and Feedback Loop

Now, the details:

#1 SOC Data Foundations. Security context (why context?) and data are available and can be queried by machines (API, MCP, etc) in a scalable and reliable manner (Both! If unreliable, humans will need to fix it and the project dies). Scalable, fast and reliable matter, as agents can screen scrape well, but you probably won’t use this to get a gig of mainframe logs via tn3270. Federated often also means “not scalable and reliable” BTW. Because access to cheap/slow storage is, well, slow.

Of course, while availability and reliability are crucial, “AI ready SOC” also means data quality, structure, and governance. GIGO is still law! Scalability is necessary, but the quality of the ingested security context is the difference between this AI thing working … or not.

Questions to ask yourself:

1. Can all security telemetry and context data (logs, asset inventory, user context, etc) be quickly and reliably queried at scale?
2. Do we have formal data quality, structure, and governance processes in place to prevent “Garbage In, Garbage Out” from sabotaging our AI efforts?

#2 SOC Process Framework and Maturity. Common SOC workflows do NOT rely on human-to-human communication (“nobody knows what server4 does, let’s see if John knows, well, he does not, but he suggested Joanna does, and — WIN! — she really does” workflows are not agent-friendly) are essential for AI success. If your SOC has a lot of ad hoc activities, agents will (at least initially!) have trouble. Worse news: weak process (this pillar #2) is very often a close friend of weak data access (pillar #1) so they “double-team” your agentic effort to oblivion. This has sunk plenty of SOAR projects in its time.

Ultimately, “If your teams don’t know who owns what, neither will your Agents.” (source). Your SOC processes must be documented, validated, and capable of being scaled and learned from (see pillar #5 below). This includes a way to train AI on past work and SOC history.

Questions to ask yourself:

1. Can our most common investigation and response workflows be followed by an agent based purely on documentation, without the need for ad-hoc human-to-human queries?
2. Do we have a system in place to train AI agents on our past work/history of alert triage and resolution to enable learning and continuous process improvement?

#3 SOC Human Element and Skills: Cultivating a culture of augmentation, redefining analyst roles, providing training for human-AI collaboration, and embracing a leadership mindset that accepts probabilistic outcomes. You really, really need executives who support “augmented” AI SOC vision, not those who seek to “kill off” the humans.

Also, they should accept that machines will make mistakes, and that is OK. In fact, leaders must not just accept “probabilistic outcomes,” but explicitly be comfortable with the machine resolving some alerts, even if it’s sometimes wrong. This acceptance of necessary imperfection is a core readiness indicator. If they expect perfection, you will have AI SOC for a month. And then go back to printing logs and reviewing them with sad little human eyes 🙂

Questions to ask yourself:

1. Are our leaders explicitly comfortable with the AI / machine autonomously closing alerts, even if it introduces an acceptable, measured error rate?
2. Have we redefined our analysts’ roles and provided training to shift their focus from manual alert triage to creative problem-solving and AI ‘shepherding’?

#4 Modern SOC Technology Stack: Implementing integrated and interoperable technologies that support intelligent systems and embed AI into existing workflows. This one is least critical of my pillar batch, but still it matters. Also, it is often a dependency for #1, so this matters as well.

The criticism is that a single “AI tool” is not the goal. The technology stack must ensure the entire security ecosystem is interoperable and flexible enough to support the other pillars. This means you can remediate, mitigate, etc.

Questions to ask yourself:

1. Is our security stack interoperable and flexible enough to allow new AI capabilities to integrate seamlessly, or are we reliant on siloed, single-function security tools?
2. Will any of our tools be overrun with agentic AI request volumes?

#5 Metrics and Feedback Loop: You are ready for AI if you can, after adding AI, answer the “what got better?” question. You need metrics and a feedback loop to get better. And to know you got better. If you “add AI” to a bad, old SOC, not only you won’t get better, you won’t even know you didn’t get better.

Metrics are a must here. Without a defined way to measure value and feed the results back into the AI models and processes, the transformation risks stalling into at best a “retrofit”, a nothing or even a worse situation…

Questions to ask yourself:

1. Can we quantify the value of AI by measuring the improvements that resulted from it?
2. Do we have an automated, continuous feedback loop to ensure AI model decisions and performance metrics are fed back into process documentation and model retraining?

The pillars should be framed not just as pre-requisites for AI adoption, but as the building blocks for a completely re-architected Security Operations Center. The transformation is about reimagining the whole way of doing things, not just accelerating one piece of an old process.

What do you think?

Related blogs:

• A Brief Guide for Dealing with ‘Humanless SOC’ Idiots
• WTH is Modern SOC, Part 1
• The Return of the Baby ASO: Why SOCs Still Suck?
• SOC Visibility Triad is Now A Quad — SOC Visibility Quad 2025
• SOC is Not Dead Yet It May Be Reborn As Security Operations Center of Excellence

Simple to Ask: Is Your SOC AI Ready? Not Simple to Answer! was originally published in Anton on Security on Medium, where people are continuing the conversation by highlighting and responding to this story.

*** This is a Security Bloggers Network syndicated blog from Stories by Anton Chuvakin on Medium authored by Anton Chuvakin. Read the original post at: https://medium.com/anton-on-security/simple-to-ask-is-your-soc-ai-ready-not-simple-to-answer-858d6789b9fa?source=rss-11065c9e943e------2