As AI becomes increasingly utilized throughout the security space, it’s important to consider how it should be used most effectively. We’ve all seen companies adding AI into every offering and function they perform, often as much as possible.
In security operations, this creates a divide between two types of companies:
- Companies that rely primarily on AI to do the work
- Companies that enable their operators to become higher performers through the use of AI
Both types address a clear problem: volume. Anyone who has worked in a SOC knows that alert volume results in burnout, limits the number of customers you can support, and takes time away from more important tasks. Using AI to accelerate triage of high-volume, lower-complexity alerts is a practical and effective strategy. It frees analysts to focus on the ambiguous cases that require human judgment. With the right context and data, AI can meaningfully assist in distinguishing malicious from benign activity — but “most of the time” isn’t good enough when a miss means a breach. AI augments the analyst; it doesn’t replace their judgment.
That said, keeping a human in the loop is critical for the fringe cases: AI hallucination, highly complex technical investigations, and even situations where attackers attempt to fool the models with injection or evasion techniques.
Let’s talk about some ways you can be the second type of company: the one that enables operators rather than replaces them.
AI for Triage Enhancement
Utilizing AI for enhancement of the triage process is something all security operation centers should be doing right now. While AI may occasionally be wrong or may hallucinate, anyone not taking advantage of it to work across the huge amount of data that SOCs process will fall behind their competitors.
I’m not suggesting that an AI model should be constantly closing alerts. Instead, consider this: Could an AI elevate alerts that would otherwise not have been investigated?
All SOCs process swathes of low-fidelity or low-severity alerts that are usually suppressed, deduplicated, or closed out through automation. What if you could use an AI model to review these alerts that would’ve been closed anyway and elevate those requiring human response to higher priorities? The worst-case scenario—a false negative—isn’t applicable here. These alerts wouldn’t have received much attention anyway. In the ideal scenario, you could intelligently review alerts your analysts wouldn’t have looked at otherwise, escalating truly concerning activity for a human to look at.
AI for Quality Assurance
Another clear use case for AI in SOCs is quality assurance. Many QA checks on investigations are random, but what if you could curate a set of prompts that tasks AI with looking for common mistakes within your investigations and notifies a human to review them?
The AI could highlight the causes for concern, providing a starting point for the human analyst. It could even offer suggestions to both the reviewer and the analyst, making note of possible improvements. The higher quality the QA prompts, the better the feedback.
AI for Validation
Similar to the first use case mentioned, AI could validate analyst investigations and look for logical gaps or potential blind spots. Effectively, it could be used to provide top cover for analysts, giving them more space to triage quicker without chasing down every lead, knowing that the model (ideally trained on past cases!) is fact-checking and logically validating their investigation, freeing them to confidently triage without second-guessing themselves.
Human Augmentation
While AI excels at handling repetitive high-volume tasks, human expertise remains essential for complex investigations, fringe cases, and maintaining accountability. Rather than viewing AI as a cost-cutting tool to reduce headcount (or to take on many more customers with the same headcount), think of it as an investment that multiplies the effectiveness of your existing team. By strategically deploying AI to handle or fact-check routine work, you free your analysts to focus on the sophisticated threats that truly require human judgment and critical thinking.
If you take one thing from this article, I want it to be this:
Using AI to augment your operators may not show the same immediate “return” as replacing them, but, over time, it will increase your service quality tenfold.
