AI IN THE SOC
AI in cybersecurity for MSPs: what it actually does, and what the line stays human
Every vendor says they use AI now. Here’s the specific, boring, verifiable version of what that means inside a real SOC, and why the boring version is the one you should trust.
THE REAL PROBLEM
Your alert volume grew faster than your headcount
You didn’t get into this business to read logs all night. But that’s where a lot of MSP security work ends up, because the tools keep multiplying and the alerts keep coming.
Phishing-driven attacks against MSPs jumped from 30% to 52% of incidents in a single year. Ransomware activity climbed 52% year over year in 2025. None of that slows down because your team is stretched thin.
So AI-in-security isn’t a marketing trend you can ignore. But “we use AI” tells you nothing about whether it’s making your operation better, or just making a vendor’s pitch deck sound current. The question that matters is narrower: what decisions is the AI touching, and who’s checking its work before it reaches you?
52%
of MSP incidents in 2025 were phishing-driven, up from 30% the year before.
+52%
year-over-year increase in ransomware attacks in 2025
WHERE WE STAND
AI should make your analysts faster. It shouldn't make the call for them.
AI is a force multiplier for a SOC, not a replacement for one.
It’s good at the parts of the job that are high-volume and pattern-based: reading raw telemetry, spotting correlations across tools, summarizing a technical event in language a client can use. It’s not good at the parts that require judgment about your specific business, your risk tolerance, your client relationship.
We built our AI usage around that line on purpose. Not because we’re cautious for its own sake, but because the moment AI starts making customer-facing security decisions without a human checking its work, you’ve traded an alert-fatigue problem for a trust problem. And trust is the only thing an MSP is actually selling.
HOW WE ACTUALLY USE IT
Two systems, one rule: AI assists, analysts decide
ENRICHMENT
AI-assisted ticket enrichment
Our SOAR platform uses AI to turn raw technical alert data into a clear, readable customer escalation that summarizes what happened and adds context based on source, asset, and severity. When AI shaped part of a ticket, we tag it. You always know what came from a model and what came from a person.
CORRELATION
XRAI, our internal operational layer
XRAI correlates activity across the tools in your stack, flags duplicate or low-value alerts before they hit an analyst’s queue, and helps prioritize investigations based on real risk rather than raw volume. Built to reduce noise, not replace the person deciding what the noise means.
WHY WE DRAW THE LINE HERE
A wrong alert is a bad day. A wrong decision is a liability.
The average global cost of a data breach hit $4.44M in 2026, and the U.S. average alone was $10.22M, the highest of any country. Those numbers exist because of missed or mishandled incidents, not typos in a ticket summary.
Standing up an internal SOC from scratch runs an MSP roughly $2.5M a year in ongoing operating cost, and even with that investment, the same alert-fatigue problem shows up eventually if the operating model underneath it doesn’t scale. AI-assisted enrichment and XRAI exist to make our analysts faster inside a model that already has human review built in at every stage, not to replace the review itself.
