kimo
Case file // national-cert-incident-queue
National CERT · EU
Defense IntelligenceNational CERT · Public sector

A national CERT fused SIEM and OSINT into one incident queue.

A national CERT’s analysts faced 38,000 alerts a day from fourteen feeds, most of them duplicates of the same incident. Kimo correlates SIEM, EDR and open-source signals into one incident queue, and 71% of the noise never reaches a human.

alert noise reaching analysts38.4k → 11.1k per day
-71%
median time to acknowledgedown from 4 h 10 min
52 min
feeds fused
14
for every incident
1 queue
[01]Challenge

38,000 alerts a day, most of them the same incident

The CERT (an anonymized, fictional national CERT in this demo) coordinates incident response for public administrations and operators of essential services. Its 34 analysts work a 24/7 rotation and receive signals from fourteen feeds: two SIEM platforms, endpoint detection from constituent organizations, an Elasticsearch log archive, threat-intelligence feeds, public code repositories and monitored open channels where stolen credentials are traded.

Each feed raised its own alerts. A single phishing campaign could appear as hundreds of SIEM events, dozens of EDR detections and a handful of open-source mentions, each in a different console. On a typical day 38,400 alerts reached the analyst queues. The median time to acknowledge a genuinely new incident was over four hours, simply because it was buried.

Analyst fatigue was the real cost. Turnover in the night shift was high, and post-incident reviews kept finding that the first signal had been there hours earlier, unread.

> We did not need more alerts. We needed fewer, better incidents. That is exactly what changed.
Head of CERT
National CERT · EU
[02]Solution

How the national CERT set up Kimo

Kimo Defense Intelligence runs on the CERT’s sovereign private cloud, with no dependency on external services. Each feed is connected through native connectors or the existing Kafka pipeline, and every signal is normalized into a common schema: entity (host, account, domain, IP), technique, source, constituent and time.

A correlation model clusters signals that share entities within time windows, deduplicates repeats and attaches open-source context such as leaked credentials or exploit code published for the relevant vulnerability. Clusters are scored by severity, constituent criticality and novelty. Only clusters above threshold become incidents in the queue; the rest remain searchable, never deleted.

Analysts see one incident with its full timeline instead of hundreds of alerts. Ask Kimo answers questions like “Which constituents saw this domain in the last 72 hours?” in seconds, against data that never leaves the CERT’s infrastructure. Suppression rules are versioned and reviewed weekly, so noise reduction never becomes a blind spot.

  1. Week 101/03
    Fourteen feeds normalized

    SIEM, EDR, Elasticsearch, threat intel and open sources in one schema.

  2. Week 302/03
    Correlation & scoring

    Entity clustering, deduplication and OSINT enrichment tuned on past incidents.

  3. Week 603/03
    One incident queue

    Analysts switch from feed consoles to correlated incidents, 24/7.

Operational picture

National CERT: Incident queue · 24h

A recreation with fictional data. Hover the chart for values.

KIMO//DI
National CERT · EU · Incident queue · 24h
Raw alerts / day
38.4k
Reaching analysts
11.1k
-71%
MTTA
52 min
-3 h 18 min
Open incidents
23
Alerts per day
Weekly average, thousands
Reaching analysts
Correlated or deduplicated
Open incidents
IncidentClusterSignalsStatus
INC-2291Credential dump · health412Critical
INC-2288Phishing wave · gov.1,936High
INC-2284VPN exploit scan7,210High
INC-2279Typosquat domains58Monitoring
> ask kimo: constituents that saw this domain in the last 72h
Mock dashboard for National CERT · EU (fictional data): Incident queue · 24h
Outcome
alert noise reaching analysts
-71%
median time to acknowledge
52 min
feeds fused
14
for every incident
1 queue
[03]Results

-71% alert noise reaching analysts.

Alerts reaching analysts fell from 38,400 to around 11,100 a day, a 71% reduction, without dropping a single signal from the record. The median time to acknowledge a new incident fell from four hours ten minutes to 52 minutes, and the night shift now handles volume that previously required an extra analyst.

In the first quarter after go-live, three incidents were detected from open-source signals before any constituent reported them, including a credential dump affecting a regional hospital. Post-incident reviews now start from the Kimo timeline. All figures above are simulated for demonstration purposes.

National CERT · EU: before and after Kimo
MetricBefore KimoWith Kimo
Alerts reaching analysts / day38,40011,100
Median time to acknowledge4 h 10 min52 min
Consoles per investigation5–71
Signals discarded—None (all searchable)
> The night shift used to drown. Now it investigates.
Shift Lead
National CERT · EU
[04]Stack

From 6 sources to one answer.

Sources
  • Splunk
  • EDR Telemetry
  • Elastic Security
  • OSINT Feeds
  • Telegram Channels
  • GitHub
Kimo models
  • Signal normalization
  • Entity correlation
  • Constituent criticality
Semantic layer · one definition per metric
Dashboards
  • Incident queue
  • Campaign timeline
  • Weekly threat brief
Next step // Briefing

Request a sovereign deployment briefing.

Walk through on-premise and air-gapped deployment, accreditation support and the simulated operations picture.