The analyst that investigates every incident — and hands you the answer.
The analyst does not pattern-match a template. It plans, gathers evidence with read-only tools, reasons over what it finds, and only commits to a conclusion the evidence supports. Here is the loop it runs on every incident.
TL;DR
The analyst does not pattern-match a template. It plans, gathers evidence with read-only tools, reasons over what it finds, and only commits to a conclusion the evidence supports. Here is the loop it runs on every incident.
The bottleneck was never the alert. It was everything after it.
Every monitoring tool ever built is good at the same thing: telling you something is wrong. None of them are good at the part that actually costs you — the slow, manual work of finding out why.
Picture the queue on a bad afternoon. A detection fires for a burst of failed logins. A monitor flips a service to degraded. A metric crosses a threshold. A host stops reporting. Each one is a notification, and each one is the beginning of an investigation that a human has to run by hand. They open a logging tool in one tab, a metrics dashboard in another, the change log in a third, and a topology diagram that is probably out of date in a fourth. They reconstruct what the incident touches, line up timestamps, rule out the obvious, and slowly assemble a story. By the time they have an answer, the next three alerts are already waiting.
This is the investigation tax, and it is paid on every single incident. It is why mean-time-to-resolution stubbornly refuses to drop no matter how many alerting integrations you add. It is why alert fatigue is real: not because the alerts are wrong, but because each one demands the same expensive ritual before anyone can act. And it is why the most experienced engineers — the ones who can read a graph and know instantly where to look — spend their days doing repetitive triage instead of the work only they can do.
Adding more alerts does not fix this. Routing them more cleverly does not fix this. Grouping them into tidier clusters does not fix this. The only thing that fixes it is doing the investigation itself — automatically, consistently, and with evidence you can trust. That is the entire purpose of the 24Observe analyst.
An alert tells you the smoke alarm went off. An investigation tells you which room is on fire, what started it, and where the exit is. We automated the second part.
It runs the investigation the way your best responder would.
The analyst does not pattern-match a template. It plans, gathers evidence with read-only tools, reasons over what it finds, and only commits to a conclusion the evidence supports. Here is the loop it runs on every incident.
Classify the incident. First it decides what kind of problem this is — a security threat or an operational failure — because the two are investigated differently. A failed-login surge and a saturated database both deserve attention, but the questions you ask about each are not the same. Getting this right up front is what lets one analyst serve both your security desk and your operations desk.
Map the blast radius. It walks the live map of how your environment fits together — which services call which, what runs on which host, who owns what — to see everything the incident reaches. For an outage it looks for the common point upstream of every failure: if a dozen services all break at once, what do they share? That shared component is almost always where the real problem lives.
Blame the change. Most operational incidents are self-inflicted: a deploy, a config edit, a rule update minutes before everything went sideways. The analyst pulls the recent changes around the incident and treats anything that landed just before it as the prime suspect — the single highest-value clue in root-cause analysis, and the first thing a seasoned engineer checks.
Confirm with the evidence. A suspicion is not a conclusion. The analyst reads the relevant metrics and logs to confirm or kill its hypothesis — is the suspect host actually saturated, did the error the change would cause actually appear, is the source address actually flagged as malicious. It cites the exact numbers and lines, so the verdict stands on evidence you can open, not on a hunch.
Deliver the verdict. Finally it writes its conclusion in plain language: what happened, how confident it is, what it affects, the evidence trail, and the recommended fix. If the evidence does not add up to a confident answer, it says so and hands the incident to a human rather than guessing. The whole loop takes seconds.
The first analyst that works your NOC and your SOC.
When it’s a threat (SOC)
For a security incident, the analyst behaves like a tier-1 SOC analyst who never gets tired and never skips a step.
It corroborates the signal across independent indicators before it calls anything real: a source address that is known-bad or appears on a threat feed, a successful login that follows a wall of failures, access to sensitive files, a privilege escalation, a sign of data leaving the building. It traces the incident outward to the identities, hosts, and assets it touches, so you see who and what is exposed — not just the single event that tripped the rule. And it is deliberately conservative: ambiguous, unsupported, or noisy signals get a measured verdict and a route to a human, because escalating a false positive erodes trust as surely as missing a real one.
When it’s an outage (NOC)
For an operational incident, it behaves like a senior SRE doing root-cause analysis under pressure — calmly.
It finds the common dependency behind a wave of failures, identifies the change that triggered it, and confirms the mechanism in the metrics — the saturated host, the climbing latency, the retry storm, the disk filling up. Its verdict is not a disposition; it is a root cause and a fix: roll back the change, restart or scale the service, fail over, free the disk. The same engine that surfaces an attacker also surfaces the two-line config edit that took down checkout. One place to look when the pager goes off, whatever the reason.
Anatomy of a verdict.
Every investigation produces the same structured, defensible result — not a paragraph of speculation, but a decision a human can act on or audit. Here is what is in it.
It learns how your team triages — and stops making the same mistakes.
A generic analyst is useful on day one and frustrating by week three, because it never adapts to your world. Ours does. Every time someone on your team corrects a verdict — downgrades a true positive to benign, fixes a disposition, adds context about why something was or was not a real problem — that correction is captured and fed back as guidance for future investigations. Over time the analyst converges on how your organisation actually reasons about incidents, not some industry average.
This matters because triage is contextual. The login pattern that is an attack in one company is a batch job in another. The error rate that is a crisis for one service is background noise for its neighbour. By learning from the people who know your environment, the analyst earns trust the way a new hire does: by being corrected, and not repeating the mistake. The corrections become an asset — a record of how your team decides — that makes every future verdict a little more like the one your best responder would have written.
It investigates on its own. It never acts on its own.
Automated investigation is safe. Automated action is where teams get nervous — and they are right to. We drew the line deliberately.
Read-only when it investigates
While the analyst gathers evidence it can only read — query logs, read metrics, walk the graph, pull recent changes. It cannot modify your monitors, rewrite a detection, or touch a system. The worst an investigation can do is read something it should not, which is why everything it sees runs through redaction first to strip secrets and sensitive data before any of it reaches the model.
Proposes, you approve
When a verdict includes a remediation — block this address, disable this account, roll back this change — it is proposed for a human to approve, queued and waiting, never executed automatically. You get the speed of an analyst that works in seconds without handing an automated system standing permission to change your environment. The judgement call stays with your team.
It refuses rather than guesses
The most dangerous thing an automated analyst can do is sound confident while being wrong. Ours is built to do the opposite: when the evidence does not support a conclusion, it returns a measured verdict and routes the incident to a human instead of inventing a cause. Plausible-but-wrong is treated as a failure mode to be engineered out, not a quirk to tolerate.
Your model, your terms
The analyst is model-agnostic. You are not married to one vendor or one opaque service — run it on the default, bring your own model, and keep your investigation data on terms you control. The reasoning layer is ours; the engine underneath is your choice, and it can change without changing your workflow.
Your responders start from an answer, not a blank page.
A tier-1 analyst seat, reframed.
The work the analyst does is the work a tier-1 responder does on the first pass of an incident. The difference is consistency, speed, and that it never runs out of hours.
This is not a replacement for your people — it is a force multiplier for them. The analyst takes the repetitive first pass off their plate so the humans spend their judgement where it counts: on the hard calls, the genuine unknowns, and the response itself. The best teams will not have fewer responders because of it. They will have responders who are no longer drowning.