You Can’t Bolt AI Onto the SOC and Expect It to Hold Up
A Shift in How AI Is Evaluated
There’s a shift happening in how organizations evaluate AI in the SOC.
Not long ago, most conversations focused on whether these new systems could accurately help with triage and reduce alert fatigue. The question is starting to change.
Security leaders have realized what matters more is whether a system can be trusted to make decisions consistently, at scale, and under real operating conditions.
That’s a much higher bar.
AI SOC Evaluation at Scale
In a recent evaluation, a large global provider of IT and security management solutions worked through more than 60 products with that question in mind. Their goal wasn’t incremental improvement, but to understand what it would take to radically change their SOC operating model and ultimately provide better customer experiences:
- More consistent investigation results
- Faster response times
- Legitimately review 100% of alerts
The evaluation wasn’t just about improving outcomes for customers. It was also about how the SOC itself could operate more effectively. The service provider was looking to unlock:
- Better-than-human scale in linear operations
- Resilience to staffing changes
- Autonomous triage for previously unseen detections
More teams are reaching a similar conclusion. We all know (and have known) the current SOC model cannot scale with increasing alert volume, fragmented tooling, transient staff, and growing expectations.
Trust Equation
Several weeks ago we released a LinkedIn post diagraming our thoughts on trust. Our perspective differs from what is typically talked about in security. Most of the time we see folks refer to trust as accuracy as if they are one in the same.
At Embed, we know trust is more complicated than just accuracy. It tends to come down to three things:
trust = accuracy + consistency + transparency
Teams aren’t just looking for efficiency or even accuracy in isolation. They’re evaluating how decisions are made, and whether those decisions hold up under scrutiny.
A New Decision Layer
When a platform becomes the decision layer for your SOC, connected to every system, applied to every alert, put in front of every customer, the expectations are different than selecting a point solution. You’re not evaluating a tool, you’re evaluating an investment that is a fundamental change to your entire operating model and customer experience.
It’s no longer enough to process alerts and surface information. The system has to reason through investigations in a way that can be examined, understood, and trusted.
For the aforementioned organization, that requirement was clear. They operate high-volume, multi-tenant environments where investigation results are delivered directly to customers. In that context, understanding how a conclusion is reached matters just as much as the conclusion itself.
As volumes increase, that consistency becomes harder to maintain. Not because of a lack of skill, but because today’s underlying systems make it difficult to apply the same level of reasoning across every case.
Why Architecture Matters
When teams start rethinking their operating model, early architectural decisions tend to shape everything that follows. Approaches that layer AI onto existing workflows may improve parts of the process, but often leave the core structure unchanged.
In many environments, that still means analysts are responsible for triaging and investigating alerts one at a time, even if parts of the workflow are faster.
A different approach is to treat investigation itself as a system that can be applied consistently. That requires being able to evaluate evidence, apply context, and reach conclusions in a way that mirrors how experienced analysts think through a case.
Just as importantly, it requires that each step of that reasoning can be inspected. What was evaluated, what was found, and how the conclusion was reached. In practice, that often means structuring investigations in a way that mirrors how experienced analysts think through a case.
At Embed, that shows up as iSteps™, structured investigation steps that gather evidence, evaluate behavior in context, and build toward a conclusion. Each step can be inspected, which makes the reasoning visible and allows decisions to hold up under closer scrutiny.
In this case, that combination of reasoning depth, transparency, and consistency at scale is what ultimately narrowed the field and led the organization to choose Embed Security.
The result was a significant reduction, 75% in analyst workload, not by removing people from the process, but by reducing the amount of repetitive work that requires human attention.
Where the Market Is Heading
What stands out more broadly is how the conversation is evolving.
A year ago, most organizations were asking whether AI could help with triage. Now, the leading teams are asking whether they can build an entirely new operating model around it.
That shift raises the bar.
It’s no longer about incremental gains, but about whether a platform can be trusted to sit at the center of that model.
We’re clear on what it means to earn that trust. It happens through consistent and accurate performance, transparent reasoning, and results that hold up under real operating conditions, not just in an evaluation, but every day after it.
Want to see how Embed works as the decision layer for your SOC? Request a demo.
