On paper, AI has been “part of” security operations for years. Every SIEM, EDR, and SOAR vendor has a story about machine learning. More recently, there’s obviously even more hype about “AI SOC.” And yet, if AI had really transformed the way we defend, we’d be talking about fewer breaches, less downtime and better detection - not just shorter investigations.
The reality is: AI has been constrained by the environment it was dropped into. It wasn’t the algorithms that fell short. It was the operating model.
The Limits of the Old Approach #
Most AI in security has lived in one of three places:
- Bolted onto SIEMs as a feature module
- Wired in through tactical API calls to a product or two
- Positioned as an “assistant” to help (or replace) analysts and deliver faster triage
That last one is where most of the recent value has focused on - and it’s not nothing. But it’s reactive. You’re still waiting for an alert to fire before AI gets involved.
It’s a bit like hiring world-class engineers and asking them to sweep sawdust after sloppy construction. They’ll make the clean-up faster and more efficient, but the beams are still crooked. The structure is still weak.
Vendors working the “AI SOC” angle have made serious attempts. Some impressive technology came out of it. But there’s a hard ceiling when the foundation underneath was never built to let AI do more than observe pieces of the system after the fact.
What AI Really Needs #
The thing about AI is that it only becomes transformative when it sees everything. Partial visibility leads to partial impact.
For security, that means AI can’t be confined to post-alert investigation. It has to cut across the whole lifecycle:
- Continuously assessing the environment
- Driving detections that adapt as threats evolve
- Turning threat intel into proactive hunts, not static feeds
- Powering triage and enrichment with context that comes from the full dataset
In other words, AI should be part of the blueprint process - ensuring the building is square and the foundation is solid before the first brick is laid. Not just a clean-up crew for the dust left behind.
Why That Wasn’t Possible Before #
Legacy SIEMs and stitched-together APIs kept AI in a narrow lane. A SIEM only contains the data that’s been ingested (and paid for). APIs expose fragments, not the full picture. The result: AI is given a narrow, delayed view of the world.
That’s why so much “AI in the SOC” feels like assistive tooling - good for shaving minutes off investigations, not reshaping outcomes.
And that’s why a new foundation was needed before the story could really change.
A New Operating System for Security Data #
Enter Vega. Think of it less as another “AI-powered tool” and more as the operating system that allows AI to finally stretch across the full security lifecycle.
Instead of limiting access to whatever a SIEM has ingested, Vega makes all data available for analysis - wherever it already lives. Data gets normalized and optimized on the fly, so AI isn’t tripped up by inconsistent formats. And the operating environment itself is built with continuous assessment, adaptive detection, and entity-centric context as the baseline.
That’s the difference between a bolt-on and an OS.
Access to the full spectrum of security data provides value in many ways; however, such an abundance of data can be a double-edged sword. Vega addresses this challenge by creating a fully contextualized semantic data layer that encapsulates knowledge about a wide variety of entities and their relationships - optimized and prepared for Large Language Models to access effectively.
This, combined with the right tools for LLMs to interact with real-time security logs in the environment through a normalized interface, allows Vega to create any type of model or AI agent to assess, tune, and generate the security stack with high precision and fidelity.
With this kind of foundation, AI isn’t relegated to sweeping up sawdust. It’s shaping the blueprint.
From Reactive to Preventive #
This shift reframes what “AI in the SOC” even means. Instead of helping analysts clean faster, AI can help prevent the mess in the first place.
It’s the leap from reactive to preventive. From janitor to architect.
And it’s what finally turns AI from a clever assistant into a force that can actually bend the curve of security outcomes.
Why Now? #
The timing isn’t an accident. For the first time, the right ingredients are in place:
- LLMs (Large Language Models) have enabled a huge leap forward in enabling us to process and understand large quantities of contextualized data
- Federated access to all security data, not just the subset you can afford to ingest
- Cheap, flexible storage that keeps full histories online
- An OS layer designed for AI to live in the middle, not at the edges
The revolution didn’t fail before. It just never had the environment to succeed.
The Blueprint is Ready #
Security teams don’t need another tool claiming “AI inside.” They need a foundation where AI can operate across the entire system, not in one reactive corner.
That’s what Vega brings. And it’s why the real story of AI in security is only just beginning.
Ready to see how AI can transform your security operations? Request a demo to explore how Vega’s AI-native platform can move your team from reactive to preventive security.