You read the “AI-ready SOC pillars” blog, but you still see a lot of this:

How do we do better?
Let’s go through all 5 pillars aka readiness dimensions and see what we can actually do to make your SOC AI-ready.
#1 SOC Data Foundations
As I said before, this one is my absolute favorite and is at the center of most “AI in SOC” (as you recall, I want AI in my SOC, but I dislike the “AI SOC” concept) successes (if done well) and failures (if not done at all).
Reminder: pillar #1 is “security context and data are available and can be queried by machines (API, Model Context Protocol (MCP), etc) in a scalable and reliable manner.” Put simply, for the AI to work for you, it needs your data. As our friends say here, “Context engineering focuses on what information the AI has available. […] For security operations, this distinction is critical. Get the context wrong, and even the most sophisticated model will arrive at inaccurate conclusions.”
Readiness check: Security context and data are available and can be queried by machines in a scalable and reliable manner. This is very easy to check, yet not easy to achieve for many types of data.
For example, “give AI access to past incidents” is very easy in theory (“ah, just give it old tickets”) yet often very hard in reality (“what tickets?” “aren’t some too sensitive?”, “wait…this ticket didn’t record what happened afterwards and it totally changed the outcome”, “well, these tickets are in another system”, etc, etc)
Steps to get ready:
- Conduct an “API or Die” data access audit to inventory critical data sources (telemetry and context) and stress-test their APIs (or other access methods) under load to ensure they can handle frequent queries from an AI agent. This is important enough to be a Part 3 blog after this one…
- Establish or refine unified, intentional data pipelines for the data you need. This ma
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