About Author
Hannah Lloyd
Hannah Lloyd is CRO and co-founder of enhanced.io. She leads global new business generation and works directly with MSP partners to build and sell security practices.
enhanced.io, the channel-only Open XDR SOCaaS for MSPs
TL;DR
Prioritizing incidents by likely severity matters more than just handling raw alert volume faster.
Escalation prediction usually works by grouping related signals into a graph and scoring how connected and unusual that group looks.
These predictions mislead in specific, predictable ways: false negatives, opaque scoring and drift over time.
A short set of questions helps you sanity-check any escalation-prediction claim before you rely on it.
A few partners have asked me lately about tools that claim to predict which incidents are going to escalate before they do. It is a genuinely useful idea, and it is also the kind of claim that is easy to oversell.
enhanced.io is a channel-only Open XDR SOCaaS built exclusively for MSPs, with 400+ integrations across endpoint, network, cloud, identity and IoT/OT. I want to walk through how this actually works, because understanding the mechanism is what lets you sanity-check the claim.
Why prioritization beats raw alert volume
An analyst working through alerts 1 at a time in the order they arrived is not necessarily working on the right thing first. What tends to happen is the incident that will turn serious sits in a queue behind 10 things that will not, purely because of timing. Prioritizing by likely severity, not arrival time, is the whole point of triage.
How escalation prediction usually works
Most tools doing this build a graph connecting related signals, a login here, a file change there, a network connection somewhere else, and look for patterns that resemble past incidents that did escalate. The scoring is essentially asking how much this cluster of activity resembles trouble we have seen before, weighted by how unusual it is for this specific environment. That is a reasonable approach. It is also only as good as the incidents it learned from.
Where 'likely to escalate' predictions mislead
The failure modes are consistent across vendors, in my experience.
False negatives. A genuinely new attack pattern will not resemble anything the model learned from, so it can score low when it should score high.
Opaque scoring. If you cannot see why a score was assigned, you cannot tell the difference between a well-reasoned score and a lucky one.
Drift. Attacker behavior changes. A model that is not retrained regularly gets quietly less accurate over time.
How to sanity-check an escalation-prediction claim
Ask for the false negative rate specifically, not just overall accuracy.
Ask whether you can see the reasoning behind a specific score, not just the number.
Ask how often the model is retrained, and what triggers a retrain.
Ask for an example of an incident the model got wrong, and what happened next.
How enhanced.io approaches triage
Correlated detection across every surface feeds our prioritization, so the signal going into a score is broader than any single tool's view. Your named Fractional Security Director validates and owns the triage decision. The model helps surface what deserves attention first. A person still decides what happens next, and that is by design, not a limitation we are working around.
FAQ
How do SOC tools decide which incidents matter most?
Most build a graph of related signals and score how closely that pattern resembles past incidents that escalated, weighted by how unusual the activity is for that specific environment. It is pattern matching against history, not a guarantee.
Can a tool reliably predict which security incidents will escalate?
What should I check before trusting an incident-prioritization model?