AiSOC

An open-source, self-hostable AI Security Operations Center. It ingests your security telemetry, detects and correlates threats, investigates them with AI agents whose reasoning is fully auditable, and proposes responses a human approves.

Docs · Architecture · What actually works · Discussions

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What AiSOC does

Telemetry arrives from your security tools. AiSOC normalizes it, runs 833 executable detection rules over it, groups what fires into incidents, investigates each one with an AI agent whose every prompt and tool call is recorded, and proposes an action. A human approves before anything executes.

What it looks like running

Watch the full three minutes — install to AI verdict on one server, against the published images. Terminal waits are shortened, which the recording says on screen. (step by step)

Stills from earlier runs under the same rules — no seeded rows, no demo mode, no mockups. The events were authored to be representative; everything downstream of them is the product doing its job. (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 — 1,725 real CISA KEV entries, 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

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 language model into a named volume; only make clean fetches it again.

make up also creates .env and generates the three secrets in it — the credential-vault key, the session signing key, and the service-to-service token — then creates an administrator and prints its password. That password is generated on your machine, shown once, and stored nowhere: copy it, or mint a new one with make bootstrap ARGS=--reset-password.

Then prove it actually works. make smoke posts one real event to the ingest API, follows it through Kafka, detection, correlation and Postgres, and 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] alert is retrievable by id from the API

Open http://localhost:3000 and sign in with the credentials make up printed (API docs at http://localhost:8000/api/docs). On a server, set AISOC_CONSOLE_URL in .env — make up then prints that address rather than localhost, which is the one people can actually browse to. Stuck? make doctor.

Try it without connecting anything

make demo loads a dataset. It is synthetic: it shows the pipeline shape, not real activity. Every row is marked is_synthetic = true in the database and labelled in the console. It is not a benchmark, a customer, or an incident.

Connect real data

Two ways in. Push, with a credential from make ingest-token (the tenant comes from it, not from 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","source_format":"json",
       "events":[{"severity":"high","title":"Encoded PowerShell from Office",
                  "host":"WIN-FIN-01","process_name":"powershell.exe"}]}'

Or pull, by configuring one of 84 click-and-connect data connectors in Settings → Connectors (needs the full profile). Those with vendor-specific normalization and live setup docs include Splunk, Microsoft Sentinel, Elastic, CrowdStrike, Okta, AWS (GuardDuty / CloudTrail / Security Hub), Wiz, and Kubernetes audit logs — full list in the connector docs. Without a vendor profile a connector still ingests through a generic mapping that resolves host, user and source IP from the usual spellings.

How it works

Ingest normalizes to a common shape and Kafka carries it. Then fusion runs 833 executable detection rules and decides what becomes an alert, correlation groups related alerts into one incident, an agent investigates and writes its reasoning to the Investigation Ledger, and a human approves any response.

Both docs/architecture/README.md and the docs portal walk that path one step at a time, and every box in every diagram links to the code that implements it.

Deployment profiles

Profile	Command	Services	RAM	What you get
core	make up	14	~8 GB	The full alerting pipeline: ingest → detect → correlate → alert → triage → console, plus the LLM gateway, a local model, and the CISA KEV threat feed
full	make up-full	22	~12 GB	Core plus event lake, entity graph, full-text search, enrichment, scheduled connectors
demo	make up && make demo	14	~8 GB	Core plus labelled synthetic data

CORE is the smallest deployment that takes a real event and produces a real alert, and it needs no credentials to do either — for two reasons.

The model ships with the gateway. Ollama runs a pinned ~2 GB llama3.2:3b-instruct-q4_K_M sized for CPU-only inference, so make up produces real triage verdicts with real token counts in the Investigation Ledger — not a stub. It is also not a frontier model, and the difference shows: in a measured run of 19 auto-triages it returned schema-valid output 7 times, and the other 12 fell back to the deterministic path, which the rail labels. To upgrade, set OPENAI_API_KEY, AISOC_LLM_MODEL_FAST, AISOC_LLM_MODEL_DEEP and an empty AISOC_LLM_API_BASE. No hosted provider has ever been exercised here — there is no funded key, so per-model rows read not measured rather than zero. (ADR-0006)

One real external feed ships too. services/threatintel polls the CISA Known Exploited Vulnerabilities catalog — authoritative, public, no API key — into the console's Threat Intelligence page: the one thing in a fresh install that is neither synthetic nor yours.

Real vs synthetic data

This matters more than any feature, so it is stated plainly.

Kind	Where	How you can tell
Real	Your connectors and the ingest API	is_synthetic = false (the default)
Real, and not yours	The CISA KEV feed on the Threat Intelligence page	Every row carries source: cisa-kev; it is the public catalog, unmodified
Demo	make demo	is_synthetic = true, labelled in the console
Benchmark	services/agents/tests/eval_data/	Every published row carries substrate: true
Test fixtures	tests/, **/tests/	Never shipped in an image

Production never silently falls back to synthetic data. When a backend is unreachable the console names the failure, not an invented investigation — and an unmeasured figure reads not measured, never 0. That was not always true; see the reality audit for where it was wrong and how each case was fixed.

AI agents

Agents triage alerts and investigate incidents. What they can and cannot do:

• They read the alert, its correlated siblings, entity context, and prior verdicts for the same signature.
• They call typed tools — lake queries, graph traversals, enrichment lookups. The model chooses a tool and passes arguments; it never writes SQL.
• Everything is logged to the Investigation Ledger: prompts, tool calls, citations, the verdict, and token cost.
• Grounding is checked. A verdict citing an indicator the evidence never contained is demoted to human review rather than auto-closed.
• A prompt is validated before it is sent. Raw logs, OCSF payloads and secret-shaped values are refused, not redacted after the fact.
• Nothing executes without a human. An approver must hold the required permission tier and must not be the person who requested the action.

The bundled model means agents reason for real out of the box. When it returns something the schema rejects, triage falls back to a deterministic path and the rail shows which one answered — it never fabricates a verdict.

Project maturity

Capability	Status	Tested	Production ready
Ingest → detect → correlate → alert	Stable	E2E + unit	Yes
Detection engine (833 executable rules)	Stable	Fixture replay + unit	Yes
Alert correlation into incidents	Stable	Unit	Yes
REST API + web console	Stable	Unit + integration	Yes
AI triage + Investigation Ledger	Beta	Unit + substrate eval + local-model run	Yes, copilot mode
Event lake + hunting (ClickHouse)	Beta	Unit	Yes, full profile
Entity graph (Neo4j)	Beta	Unit	Yes, full profile
Governed response actions	Beta	Unit	Human-approved only
Scheduled connectors	Beta	Contract tests	full profile
UEBA	Beta	Unit + live migration round-trip	full profile
Package distribution (npm/PyPI)	Ready, unpublished	release.yml builds and packs all eight on every tag	Install from source — the upload is blocked on registry credentials, which is an account action

What AiSOC is not

• Not a drop-in SIEM replacement. It correlates and investigates; it does not replace long-term log retention and compliance search.
• Not able to see telemetry you have not connected. There is no discovery.
• Not autonomous by default. Response requires explicit policy authorization and a human approver.
• Demo incidents are not real incidents, and benchmark corpora are not customer telemetry.
• Benchmark numbers are substrate self-consistency measures, not live agent accuracy, and are labelled as such wherever published.

Troubleshooting

make doctor checks the host tools, memory and disk in the Docker VM, every port, each datastore by querying it rather than by asking whether its container is up, and whether .env still holds placeholders — then prints the command to run next. The six failures it is most often right about are tabulated under Installation → Troubleshooting.

Security

Secrets are generated per deployment and never committed; connector credentials are encrypted at rest. Services connect to Postgres as a DML-only role, so the row-level-security policies actually apply to them, and tenant isolation is enforced at the query layer in every store. RBAC gates every mutating route, ingest is authenticated, and the default install sends no prompt anywhere — the model runs beside it. Report issues via SECURITY.md.

Developing

make test        # unit tests for every service
make smoke       # the golden pipeline, against a running stack
make stats       # recount every figure this README publishes

Guides: add a connector · add a detection · plugin lifecycle · contributing. The connector and detection-rule counts above are recounted from the tree by scripts/project_stats.py, which CI fails if this README disagrees with it.

Roadmap · Contributing · License

ROADMAP.md · CONTRIBUTING.md · SECURITY.md · MIT