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What AiSOC does

Telemetry arrives from your security tools. AiSOC normalizes it, runs the 2535 executable rules of its 6991-rule library, groups what fires into incidents, investigates each one with an AI agent whose every prompt and tool call is recorded, and proposes an action. New threat intelligence re-sweeps the history you already collected, and a human approves before anything reaches a vendor.

What it looks like running

Watch the full three minutes — install to AI verdict on one server against the published images, terminal waits shortened and the recording saying so on screen. The stills below are earlier runs under the same rules: no seeded rows, no demo mode, no mockups. (step by step · what is real)

Alerts — each attributed to the connector that fed it.	Automated triage — the bundled local model's verdict, confidence and rationale, verbatim.
Threat intelligence — the real CISA KEV catalog, minutes after boot, with no API key.	SOC operations — with nothing connected yet, and it says so rather than showing a placeholder.

Quick start

git clone https://github.com/beenuar/AiSOC && cd AiSOC
make up

The Technical Guide covers this in depth: server sizing, where the model runs, every failure mode with its cause and fix, the REST API, MCP, and every console surface.

Needs Docker Compose v2 with 8 GB memory and 20 GB free disk in the Docker VM, plus python3 (3.9+) and bash; make doctor checks all of it and Installation says what each number was measured against. The first run downloads a ~2 GB model into a volume only make clean clears.

make up also creates .env and generates the fifteen secrets in it — the credential vault, the session signing key, five service-to-service credentials and four datastore passwords — then creates an administrator and prints its password, generated on your machine, shown once and stored nowhere. Lost it? make bootstrap ARGS=--reset-password mints another.

A port already in use does not stop the install. AiSOC publishes on a free one, names what held the old one, and moves the console address with it: 64 seconds to a signed-in console on a bare clone with 5432 and 11434 both taken.

Then prove it works. make smoke posts one real event to the ingest API and follows it through Kafka, detection, correlation and Postgres, then reads the alert back out of the public API. Every stage reports PASS or FAIL:

$ make smoke
[PASS] raw telemetry accepted by ingest
[PASS] event traversed the spine and became an alert
PASS: 10/10 stages

Sign in at the address make up printed. A tenant with nothing connected lands on a setup wizard rather than an all-zero dashboard, and its state is read from your own data so it stays right if you connect a source through the API. The spec is docs/openapi.yaml — interactive docs are off in this production-class stack. Stuck? make doctor, which on a host where you have not run make up yet says exactly that.

Press Load sample data in the wizard. Five scenarios take the same ingest path a real connector uses — not inserted rows — so watching them become alerts means watching the pipeline work. They span low to critical on purpose, because a first run where everything is a crisis teaches you nothing about how triage separates signal from routine. They are attributed to AiSOC in the source column, refuse to load into a tenant that already has real alerts, and do not mark setup complete. (what each step proves) For the larger fixed corpus used by demos and evals, make demo loads a synthetic dataset — the pipeline shape, never a benchmark, a customer or an incident. Every row is is_synthetic = true and labelled in the console.

Connect real data

Push, with a credential from make ingest-token (the tenant comes from it, not a header):

curl -X POST http://localhost:8081/v1/ingest/batch \
  -H 'Content-Type: application/json' -H "Authorization: Bearer $AISOC_INGEST_TOKEN" \
  -d '{"connector_id":"edr-1","connector_type":"crowdstrike","events":[{"severity":"high",
       "title":"Encoded PowerShell from Office","host":"WIN-FIN-01"}]}'