# 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.
[](https://opensource.org/licenses/MIT)
[](CHANGELOG.md)
[](https://github.com/beenuar/AiSOC/actions/workflows/ci.yml)
[](https://github.com/beenuar/AiSOC/actions/workflows/codeql.yml)
[](https://securityscorecards.dev/viewer/?uri=github.com/beenuar/AiSOC)
[](https://raw.githubusercontent.com/beenuar/AiSOC/main/apps/web/public/papers/aisoc-technical-guide.pdf)
**[Technical Guide (PDF)](https://raw.githubusercontent.com/beenuar/AiSOC/main/apps/web/public/papers/aisoc-technical-guide.pdf)** ·
[Docs](https://beenuar.github.io/AiSOC/) · [Architecture](docs/architecture/README.md) · [What actually works](docs/audit/REPOSITORY_REALITY.md) ·
[Discussions](https://github.com/beenuar/AiSOC/discussions)
What AiSOC does
Telemetry arrives from your security tools. AiSOC normalizes it, runs the 2534 executable rules of its 7015-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
sign-in address, the console shows real CISA KEV rows, a pushed event becomes an alert, and the cost dashboard reports the tokens triage spent" />
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 from a v20.1.0 run under the same rules — the recording is older than they are.
| | | |---|---| | 
| <img
src="apps/web/public/screenshots/ai-triage-verdict.png" alt="AI triage verdict in the Investigation Rail" /> | | Alerts — each attributed to the
connector that fed it. | Automated triage — the bundled local model's verdict, confidence and rationale, verbatim. | | <img
src="apps/web/public/screenshots/threat-intel-kev.png" alt="Threat intelligence page showing CISA KEV entries" /> | <img
src="apps/web/public/screenshots/soc-operations.png" alt="SOC operations dashboard with honest empty states" /> | | 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
On Windows, or on a machine with nothing installed yet, one line does the prerequisites too: `iwr -useb
https://raw.githubusercontent.com/beenuar/AiSOC/main/install.ps1 | iex(curl -fsSL https://raw.githubusercontent.com/beenuar/AiSOC/main/install.sh |
bashon Linux and macOS). **Without Docker at all**,pip install aisoc-sandbox && aisoc-sandbox demo` walks one alert through the agent funnel
offline in under 30 seconds. Without your own machine, open the repo in GitHub Codespaces and run make up in its terminal. All four end at the
same console; only the first three install anything.
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):
```bash 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"}]}'
Or pull, by configuring one of **90 click-and-connect data connectors** in **Settings → Connectors** — Splunk, Sentinel, Elastic, CrowdStrike, Okta,
AWS and Kubernetes audit among those with vendor-specific normalization and setup docs
([coverage](https://beenuar.github.io/AiSOC/docs/connectors/api-coverage)). Without a vendor profile a connector still ingests through a generic
mapping that resolves host, user and source IP.
**A real cloud estate does not hand you its logs over a REST call.** Four collectors read the surfaces one actually emits to: a CloudTrail
organisation trail on **S3 notified over SQS** (management events, data events and VPC flow logs), a **GCP Pub/Sub** subscription on a Cloud Logging
sink, an **Azure Event Hubs** capture, and a **syslog/CEF** listener. Each keeps a resumable cursor and a bounded per-poll budget, so a backlog
applies backpressure instead of flooding ingest.
Bringing existing detections? `packages/aisoc-migrate` translates Splunk SPL, Sentinel KQL and Elastic EQL and **refuses rather than approximating**
what it cannot carry, because an almost-right rule is harder to find than a missing one: of the 2,005 Splunk rules bundled here 1,734 translate and
1,711 of those are partial ([what that means](apps/docs/docs/migration/from-splunk.md)).
## How it works
Ingest normalizes to a common shape and stamps an **activity projection** on every event — actor, action, resource, location, outcome, with
`actor.kind` read from each vendor's own documented field rather than inferred — then Kafka carries it. Then fusion runs 2534 executable detection
rules, of 7015 on disk, plus 76 sliding-window rules (73 count across events; 3 match an ordered sequence), applies **your tenant's own tuning** on
top — the disables, floors and suppressions the console writes, so a rule you turned off actually stops firing — and decides what becomes an alert.
Correlation groups related alerts, an agent investigates and writes its reasoning to the Investigation Ledger, and a playbook may start from the
result. Separately, new threat intelligence sweeps the lake for sightings you already collected, and a hypothesis becomes a hunt without anyone
writing a query — the model fills a closed schema and every value is bound as a parameter, so it cannot express a query at all.
**A playbook triggered by an alert previews before it acts.** Three switches must agree — the deployment, the tenant, the playbook — and every default
is off; anything less runs in preview with its plan attached to the alert. An approval step is a durable pause: the run suspends to Postgres, survives
a restart, resumes after the approval, and expires with a recorded outcome rather than hanging.
**Executable is earned, not declared.** A rule joins the compiled ruleset only after a vendor-shaped event is replayed through the real connector and
engine and that rule is *watched to fire* — never inferred from a directory or an `enabled:` flag. The proof can fail: `--prove-gate` reverts the
Windows connector and requires all 1,687 Windows rules to go silent. It means reachable, not that it detects an attack. **Two** rules still cannot
fire — down from 119, because a rule that could not fire stopped being counted as executable — and both are waiting on a behavioural baseline. The
ratchet only moves down. ([why 1,362 were refused](docs/detections/sigma-compilation.md))
**Every answer carries its receipts.** The copilot cites each checkable claim to the ledger entry behind it and labels the rest *uncited* rather than
dropping them, and any investigation exports as a **signed evidence bundle** — byte-identical, prompts as digests, OCSF 1.9.0.
([how](docs/architecture/evidence-bundles.md))
**[docs/architecture/README.md](docs/architecture/README.md)** walks that path one step at a time — eleven steps, five diagrams, every box linking to
the code — and [mirrors to the docs portal](https://beenuar.github.io/AiSOC/docs/architecture).
## Deployment profiles
| Profile | Command | Services | RAM | What you get | |---|---|---|---|---| | **core** | `make up` | 16 | ~8 GB | The full alerting pipeline: ingest →
detect → correlate → alert → triage → console, plus the LLM gateway, a local model, the CISA KEV threat feed, and the connector and response services
the agent's vendor tools reach | | **full** | `make up-full` | 22 | ~12 GB | Core plus event lake, entity graph, full-text search, enrichment | |
**demo** | `make up && make demo` | 16 | ~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.**
**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 not a frontier model: over 50 alerts it gave triage usable
output 44 times before the reply was constrained to JSON and 50 after ([method](scripts/measure_triage_reliability.py)), and the rail labels which
path answered. Run it faster with `make up-gpu`, `make up-host-llm`, or your own provider from the console ([all
four](apps/docs/docs/operations/where-the-model-runs.md)). **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](docs/decisions/0006-llm-gateway-in-core.md))
**One real external feed ships too.** `services/threatintel` polls the CISA Known Exploited Vulnerabilities catalog — public, no API key — into the
console's Threat Intelligence page: the one thing in a fresh install that is neither synthetic nor yours.
## Evaluation, reflex and replay
The published numbers carry their own provenance. ([methodology](https://beenuar.github.io/AiSOC/docs/benchmark) · [real vs synthetic data](apps/docs/docs/concepts/real-vs-synthetic.md))
- **Every verdict scorecard names the model that actually served it** — gateway-attested or self-reported, with an explicit provenance status. A run
with missing attribution, zero real calls or a deterministic fallback cannot be advertised as a named model's performance, and a measurement that
cannot be bound to an attributed call cannot authorize closures. - **Denominators are published, and the uncomfortable direction wins.** Full-cohort
recall keeps abstentions and failures in the malicious denominator; closure claims ship with their contamination rate; alert reduction has three
labelled figures (75.3% legacy grouping, 33.3% production key, 24.5% on a detection-shaped cohort after a missing identifier was fixed to *narrow* the
group instead of widening it). Unmeasured reads *not measured*, never `0`. - **The reflex path is observable** — nine named stages from queue delay to
verification, and a decision contract carrying supporting evidence, contradictory evidence, missing prerequisites and the escalation reason. -
**Replay is time-frozen; re-hunting is labelled.** Historical replay enforces evidence cutoffs in the retrieval queries (never by prompt), discloses
the model-knowledge limit, and cannot execute real actions — the mutation boundary is the write adapter. Applying new intelligence to old telemetry is
a distinct, rehearsable mode, and its discoveries are never counted as predictions at the original event time. - **Model suitability is per task.**
The matrix grades each configured model on the same locked evidence against a deterministic baseline floor (balanced accuracy exactly 0.250); unfunded
rows read *not measured*, with one-command reproduction against any local OpenAI-compatible endpoint.
## 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 picks the tool and passes arguments; it never writes SQL. - **Everything is logged** to the
Investigation Ledger — prompts, tool calls, citations, verdict, token cost — and exports as a signed bundle. - **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. - **No vendor is touched without a human**, unless a tenant
has explicitly granted autonomy for that verb. Every response step is graded against its own capability contract at dispatch, so approving a playbook
never authorises whatever its steps happen to contain, and an approver must hold the required permission tier and must not be the person who requested
the action.
## Project maturity
**Stable is defined, and a gate enforces it.** It was ungated prose until three rows were found describing coverage that did not exist. A row is
Stable only with a check that runs on every pull request with no path filter, drives the real production path against real infrastructure, and has a
**negative control proven by breaking the thing and watching it go red**. ([the bar](docs/audit/MATURITY_DEFINITION.md))
| Capability | Status | Tested | Production ready | |---|---|---|---| | Ingest → detect → correlate → alert | Stable | E2E + unit | Yes | | Detection
engine (2534 executable rules) of 7015 | Stable | Replay proof | Yes | | Alert correlation into incidents | Stable | Unit | Yes | | REST API + web
console | Stable | Unit + integration | Yes | | AI triage + Investigation Ledger | Stable | Live Postgres ledger + a PR-gated local-model agent run.
No hosted provider has been exercised | Yes, copilot mode | | Event lake + hunting (ClickHouse) | Stable | Live ClickHouse on the shipped DDL, with a
negative control | Yes, `full` profile | | Retro-hunts when new intel arrives | Stable | Live ClickHouse + Kafka with the flag on, with a negative
control | Opt-in, `full` profile | | 68-hunt YAML library, compiled against tenant events in the lake | Stable | Live ClickHouse: scheduled hunts read
tenant data and refuse to fall back to the fixture; all 114 field names the corpus filters on compile, via a lake column or the stored payload | Yes,
`full` profile | | SCIM 2.0 provisioning | Stable | Live Postgres through the real app: PATCH in both providers' dialects, group membership,
deprovisioning proved by its consequences, with a negative control | Yes | | White-label branding | Stable | Live Postgres: the scheduled report and
its covering email carry the organisation's brand, with two negative controls | Yes | | Usage metering | Stable | Live ClickHouse on the shipped DDL,
with a negative control that makes an unscoped meter fail | Yes | | Entity graph (Neo4j) | Stable | Live Neo4j against the production reader, with a
negative control | Yes, `full` profile | | Governed response actions | Stable | Live socket: permits, refuses, never leaks a refusal, with a negative
control | Human-approved only | | Alert-triggered playbooks, with a durable approval pause | Stable | Live Postgres: suspend, restart, resume, expiry,
with a negative control | Yes — three opt-ins deep, preview by default | | Per-tenant detection tuning in the live engine | Stable | Live Postgres:
tuning written changes what the engine fires, with a negative control | Yes | | Scheduled connectors | Stable | Live scheduler polls a stub vendor
into ingest, with a negative control | Yes | | UEBA | Stable | Live Postgres: migrations, scoring, persistence, isolation, with a negative control |
Yes, `full` profile | | Package distribution (npm/PyPI) | Beta | The five PyPI packages resolve at the tree's versions, checked against the live
registries in both directions with a self-test; the npm half has no registry state to verify | `pip install aisoc-sandbox` (and the other four) works
today; the three npm packages still need the first-upload `NPM_TOKEN`, 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 and sample incidents are not real incidents**, and benchmark numbers are substrate self-consistency measures, not live
agent accuracy — labelled as such wherever published.
## Troubleshooting
`make doctor` checks host tools, memory, disk, every port and each datastore by *querying* it rather than asking whether its container is up, then
prints the command to run next. A container killed by a full Docker VM is named as that, not as the service that happened to die. ([the six most
common failures](https://beenuar.github.io/AiSOC/docs/installation#the-six-most-common-failures))
## 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 row-level security actually applies, 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.
SAML and OIDC sign-in with per-connection tenant and group mapping, SCIM provisioning, and console **MFA** — TOTP with recovery codes, enforceable per
tenant. Attribute conditions, time-boxed elevation and workload identities run inside the one permission path, where a condition can only narrow a
permission and never grant one. ([SSO](apps/docs/docs/operations/enterprise-sso.md) · [access governance](docs/security/access-governance.md))
**A service with no credential refuses to serve rather than serving unauthenticated.** The [changelog](CHANGELOG.md) records each fix; report via [SECURITY.md](SECURITY.md).
## Developing
```bash
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](https://beenuar.github.io/AiSOC/docs/detections/hello-hunt) · plugin lifecycle.
Every count above is recounted from the tree, and CI fails if this README disagrees.
Funding · Roadmap · Contributing · License
Development is funded and supported by Cyble, who pay for the engineering time behind AiSOC and release it under the MIT
licence rather than keeping it. That buys no special treatment here — no Cyble-only features, no gated modules, no telemetry. ([full
credits](.github/CREDITS.md))
ROADMAP.md · CONTRIBUTING.md · SECURITY.md · MIT