AiSOC

What is AiSOC about?

AiSOC provides a self‑hosted AI‑driven Security Operations Center. It pulls logs and events from any security tool, applies thousands of executable detection rules, groups matching events into incidents, and hands each incident to an AI agent that investigates, reasons, and suggests mitigation actions. All interactions are captured in a replayable decision ledger for full auditability.

How to use AiSOC?

1. Clone and start – git clone https://github.com/beenuar/AiSOC && cd AiSOC && make up (or use the provided PowerShell/Bash installers).
2. Access the console – after the containers start, the command prints a URL; open it in a browser.
3. Load data – use the wizard to ingest real telemetry via the REST ingest API, configure one of the 91 built‑in connectors, or run make demo for synthetic data.
4. Observe the pipeline – the console shows alerts, AI triage verdicts, threat‑intel feeds, and correlation views.
5. Approve actions – playbooks can be previewed and require an explicit human approval before any response is executed.

Key Features of AiSOC

• Rule‑engine: 2,966 executable detection rules (out of a 7,407‑rule library) with sliding‑window logic.
• AI triage & investigation: Local Llama‑3.2 model (via Ollama) runs on‑premise; every prompt, tool call, and token cost is logged.
• Replayable decision ledger: Immutable audit chain stored in Postgres; can be exported as signed evidence bundles.
• Threat‑intel integration: Automatic pull of CISA Known Exploited Vulnerabilities catalog (no API key).
• Extensible connectors: 91 click‑and‑connect ingest adapters; generic fallback mapping for unknown sources.
• Full deployment profiles – core (alert pipeline), full (adds ClickHouse lake, Neo4j graph, OpenSearch), demo (synthetic dataset).
• Governed response: Playbooks require multi‑level approval; actions are only executed after explicit tenant permission.
• Zero‑API‑key operation: All components run locally; no external LLM provider keys are required.
• Self‑hosted, MIT licensed – can be deployed on any Docker‑compatible host with 8 GB RAM and 20 GB disk.

Use Cases of AiSOC

• SOC automation – augment analysts with AI‑driven triage, reducing alert fatigue.
• Threat hunting – retro‑hunt historic telemetry when new intel appears.
• Incident response – AI agents investigate correlated alerts and draft response playbooks for human approval.
• Compliance auditing – immutable ledger provides tamper‑evident proof of all investigative steps.
• Red/Blue team exercises – simulate attacks, evaluate detection coverage, and iterate on rule sets.

FAQ

Q: Do I need any cloud API keys? A: No. AiSOC ships with a local Llama model and a public CISA intel feed; everything runs offline.

Q: Can I use a different LLM? A: Yes. The model can be swapped via the LLM gateway; use make up-gpu or configure a custom provider in the console.

Q: How much hardware is required? A: The core profile needs ~8 GB RAM and 20 GB disk; the full profile adds ~4 GB RAM for ClickHouse, Neo4j, and OpenSearch.

Q: Is my data encrypted? A: Secrets are generated per deployment and never committed. Connector credentials are encrypted at rest, and all datastore connections use least‑privilege roles.

Q: What if I want to integrate with existing SIEMs? A: Use the REST ingest endpoint or any of the pre‑built connectors (Splunk, Sentinel, Elastic, CrowdStrike, etc.) to push events into AiSOC.

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.

Technical Guide (PDF) · Docs · Architecture · What actually works · Discussions

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What AiSOC does

Telemetry arrives from your security tools. AiSOC normalizes it, runs the 2966 executable rules of its 7407-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 from a v20.1.0 run under the same rules — the recording is older than they are. (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

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 | bash on 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):

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 91 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). 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).

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 2966 executable detection rules, of 7407 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)

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)

Where state lives. Twenty service trees (14 Python, 4 Go, 2 TypeScript) behind the Next.js console. Kafka is the spine; Postgres holds the alerts, cases and the tamper-evident audit chain; Redis and Qdrant are in CORE too, and full adds ClickHouse (the event lake), Neo4j (the entity graph) and OpenSearch (full-text search).

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.

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), 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). 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 — 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 · real vs synthetic data)

• 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)

Capability	Status	Tested	Production ready
Ingest → detect → correlate → alert	Stable	E2E + unit	Yes
Detection engine (2966 executable rules) of 7407	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)

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 · access governance)

A service with no credential refuses to serve rather than serving unauthenticated. The changelog records each fix; report 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. 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)

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