Alert Fatigue

In an Omdia survey of 300 security teams, 42% of alerts were never investigated. The queue outruns the shift: detectors fire before they are tested, every vendor raises the same event, and a silent feed looks safe. Real alerts wait behind all of it.

Noise

Anton Chuvakin’s study of alert fatigue separates four problems with four different fixes, and notes that aggregation, filtering, risk scoring and playbooks have each fallen short when used alone. The sections below take them in order.

Volume

Too many alerts, each deserving a look. The fix is classification, consolidation and caps.

False positives

The detector is wrong. The fix is the detection itself, not the queue that receives it.

Benign true positives

The detector is right and the activity is allowed. The fix is context: which baseline the activity left, and by how much.

Hard to triage

The alert may be real, and proving it takes hours. The fix is investigation capacity.

Classification

A port scan seen by three firewalls arrives as three alerts in three formats. Each is correct. Together they are noise, and a rule written for one vendor misses the other two.

ARRTECH SIEM classifies events from any vendor into common types, graded Informational to Critical, so a port scan from any firewall is one class. Unwanted records are filtered out. Correlation rules run on set intervals with alert volume capped per column and period.

When a source goes silent for a set period, the SIEM alerts. An empty feed is reported, not read as safe.

Detection

A new detector is usually switched on and tuned in your queue. Its false positives are found by the analysts who receive them, one at a time.

Cyberdroid AI Detection, the AI layer of the ARRTECH Security Suite, runs 21 detectors, each disabled, in shadow or in production. A shadow detector’s findings go to a separate channel while replay tooling measures precision, recall, stability and volume on your own telemetry. Promotion and rollback are recorded decisions. If a detector floods, a cap of 500 findings per detector and 2,000 per tenant per hour routes the overflow to shadow, and the detector is demoted.

Every finding shows its observed value, baseline, score and threshold, an ordered evidence timeline and the provenance and age of any threat intelligence. Telemetry health sits beside it, so your team can tell “nothing suspicious found” from “the telemetry was incomplete”.

Investigation

A tuned queue still runs ahead of the people who work it. The alert that waits is not less real than the one that was picked up.

Cyberdroid AI Investigation takes a Detection finding or SIEM alert through read-only queries, challenges its own verdict and writes a reviewable case. An incomplete case is parked, not concluded. The depth of an investigation never depends on who picks it up. Early access requires Detection.

Response

Nothing on this page blocks, remediates or changes your estate. Detection and Investigation decide nothing. A person approves, and ARRTECH SOAR runs the playbook, with every decision recorded. A score is a prioritization aid, not a verdict.

Limits

No product on this page promises to end false positives or cut them by a percentage. Volume is measured on your telemetry during a proof of value, with a scorecard agreed first.

Timing

Cyberdroid AI Detection is near-hourly: one hour’s activity becomes eligible after the next hour begins, plus processing. ARRTECH SIEM correlation runs on the intervals you set and alerts as rules match.

Scores

A score is a prioritization aid, never a verdict, and a graph relationship does not prove intent. The finding shows what changed against the baseline. Deciding what that means is your analyst’s work.

Evaluation

A proof of value runs beside your existing controls on labeled replay scenarios and at least one representative week of your own telemetry. Precision, recall, stability, volume and shadow-overflow counts are scored for each detector against a scorecard agreed before it begins.

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