Sovereign security for SMEs

From log noise,
to incident response.

Harold reads your application logs, flags suspicious activity, groups related alerts into incidents, and shows your team the evidence for a response.

From the team behind European Commission · EBSI DataDiggers NATO DIANA
Coral-red streams of digital log events converging upward into a faded TVL Tech mark, with a few alerts flaring out of the noise.

The problem for SMEs

Attacks scale.
Small teams cannot.

As threats accelerate, web logs, application errors and security alerts arrive in separate streams. A small team has to connect them before it can respond.

Sources: UK NCSC · ENISA — SMEs Cybersecurity

Three separate vertical streams — web, application and security events — with a coral thread connecting the related anomaly signal across them.

One 24-hour deployment

A day of logs.
A focused review queue.

Harold deduplicates, detects, groups and enriches — so a day of raw telemetry becomes a short list a person can actually work.

Input 15

connected sources

24 hours of telemetry 394,630

raw log lines read

Human review 10

incidents to examine

Pipeline Deduplicate Detect Group Enrich

Supported source types

DigitalOcean Google Cloud Run Google Cloud SQL AWS Fargate Heroku Docker journald Log files Cloudflare Ollama LM Studio

SOC · Authentication

Failed logins.
Then admin access.

Eight addresses try one account. A later login succeeds, then the session touches an admin route.

Local checks first.
AI only when needed.
Authentication logs 186 failed logins

One account · 8 source IPs · 12 minutes

01

Match the pattern

A local sequence check counts failures for the account across IPs.

02

See what followed

A new-IP login succeeds; the session requests /admin/users.

03

Open one incident

Failures, login and route events stay together. No model call for the initial detection.

NOC · Application performance

Checkout slows.
Find the cause.

Query logs, database pressure and failed requests point to one issue your team can investigate.

One incident · one owner

Assign the application owner. Check the query and connection pool, then verify latency after the fix.

Query slows

Slow-query log on /checkout

Connections wait

Database pool wait starts to rise

Responses lag

Checkout p95: 180 ms → 2.4 s

Requests fail

HTTP 500 appears · 42 requests affected

Harold incident management: open incidents, each with a status, a severity and the number of alerts it collapsed into one item.
IncidentsFindings that describe the same failure are grouped into one item — the alert count on each row is the noise that was removed.

Human-approved response

See the evidence.
Stay in control.

Harold recommends what to examine. Your team makes the decisions that affect production.

01

See why it was raised

Inspect source events and related signals before recording a verdict.

02

Approve any edge block

A Cloudflare block requires a separate operator action.

03

Choose where data goes

Run in your environment with local or hosted model analysis.

Tune recurring noise with scoped exceptions and replay before rollout.

A single Harold alert: severity, source and component, a plain-language diagnosis against the application's own baseline, recommended actions, affected routes, client addresses and the controls an engineer needs next.
AlertAn alert is a complete explanation, not a line of red text: the diagnosis, the evidence one click away, and the next action in reach.

Pricing

€100 a month.
Up to a million alerts.

Pricing counts processed alerts, not raw log lines — so the bill tracks what reaches a verdict, not your traffic volume.

€100 / month

Up to one million alerts processed per month.

The whole pipeline runs on your own infrastructure, with a locally hosted model when log content must not leave the perimeter. Pricing counts processed alerts; the deployment figures above count raw log lines.

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See it for yourself

See Harold
work live.

Bring one application and a sample of its logs. In a 30-minute demo, follow a finding from log line to incident and discuss a pilot on your data.