Vigil

The open source AI SOC you own

Vigil is the leading open source AI SOC: Apache 2.0, skills-native, and built by the team behind StackStorm. Run it on a laptop in minutes with a local model or the provider you choose, then grow it into the autonomic layer of your SOC.

GitHub stars for VigilLatest Vigil releaseLicense Apache 2.0
Video: Vigil 1.0, autonomous SOC, 100% open source
Read the video transcript

Agentic versus autonomous. Agentic, autonomous: what's the difference, and how can it help me and my defense in cybersecurity? Agentic means the human is very much in the loop, and autonomous means the human is mostly on the loop. Let's see how a SOC can hunt faster without giving up control, and how the human shifts from being in the loop to being on the loop. This illustration will also show why owning your learning loop is important.

Let's start with a threat hunt. The first time, it takes five agents, 30 seconds, and, assuming you send the thinking to a hosted LLM, $4.50 on a first task. This drops to only two agents, 20 seconds, and $1.25 on a similar task later. Let's see how this works.

Imagine a threat hunt that starts with a fictional CISA report, Copper Lattice. This fictional report describes attackers persisting on edge routers and VPN devices, harvesting credentials, and using rotating relay infrastructure. The first time through, the analyst drops the PDF into Vigil to develop a hypothesis and hunt plan. The human is in the loop and reviews the proposed scope and evidence before execution.

This particular hunt is new to this deployment of Vigil. A look into the memory shows the hunt has little relevant experience to draw on, so it may need more agents or a more capable model you've chosen. It still checks confidence, cost, and permissions. Automatic model selection is coming soon after Vigil 1.0. For this initial hunt, actions remain read-only because Vigil's confidence in the hunt is low, and so a human is required in the loop. No actions such as credential rotation, ACL removal, or reboot can be taken until the incident response lead approves containment.

The system then reviews what the hunt found and how it got there. Each hypothesis closes as confirmed, refuted, or inconclusive, with any data gap named. The ledger keeps the trace, evidence, outcome, and cost. This gives the next investigation evidence to learn from.

If you have LogLM as well, Vigil can use semantic similarity search to look for related behavior in your telemetry, not just the report's known IP addresses. In this example, you might surface two more IPs with similar beaconing behavior that the original report didn't list. Those are candidates to scrutinize. Vigil can check the sessions, cadence, destination context, and alternative explanations for these IPs also behaving in a concerning manner.

Memory keeps the hypothesis, plan, what worked, and what it cost. Your feedback distinguishes a useful result from a mistake worth avoiding. Essentially, all feedback updates the prior for the next similar hunts. The original evidence stays available for checking. Vigil's watcher reads the record. Which steps helped, and where did agents repeat work? Could a smaller plan preserve quality? Importantly, the watcher, the ledger, and memory also check that agents stay within your guardrails. Vigil helps make sure your agents stay on your side.

Later, imagine a similar CISA alert arrives as STIX JSON through a TAXII feed. Vigil checks the memory first. The prior hunt helps it assess the new hypothesis. Because it has performed a similar hunt recently, it calculates a higher confidence score and develops a more intelligent, concise plan: less time, fewer tokens, and less model spend if you use a paid external model. So you can see that the system is starting to earn confidence. This will naturally shift the human to on the loop from in the loop, if thresholds are set to allow for autonomous operations with higher confidences. The other direction also works: when the system has a lower confidence score, it will require a human in the loop to proceed. Remember, it is us humans that set those thresholds.

Vigil 1.0 is the leading AI-native AI SOC and the preferred path for cyber defenders moving towards autonomous operations. Please take a look, give it a try, and feel free to join a talk in a city near you, weekly office hours, or our Vigil community Discord. Vigil Assured, featuring additional controls, 24x7 support, and more, is available from DeepTempo.ai as part of the Intelligent Defense Platform. Free trials of Vigil Assured and the Intelligent Defense Platform are also available.

Quick startbash
git clone https://github.com/Vigil-SOC/vigil.git
cd vigil
./start.sh

# then open http://localhost:6988

Docker must be running. No LogLM and no cloud API key are needed to reach a running console; a local Ollama model works.

What ships

A SOC in readable code

Agents you can read, workflows you edit as Markdown, and integrations on an open standard. Your team owns each layer and extends any one without touching the others.

Starts with 13 specialized agents: author your own in seconds

Triage, investigation, hunting, correlation, response, reporting, MITRE mapping, forensics, threat intel, compliance, malware and network analysis.

Workflows as Markdown

Each playbook is a WORKFLOW.md file: agent sequence, tool access, and instructions per phase, under your change control.

30+ integrations over MCP

Splunk, Sentinel, CrowdStrike, Defender, SentinelOne, Okta, Cribl, Jira, Slack, PagerDuty, and more. Add one by wrapping an API in an MCP server.

The detections you already trust, plus LogLM awareness

7,200+ community rules across Sigma, Splunk, Elastic, and KQL, plus your existing and federated detections in Splunk and Elastic. Vigil manages the rules an environment runs, which makes coverage assessment routine.

Self-adjusting harness

Autonomy is earned, and only people grant it

The lesson from a decade of StackStorm deployments: the system may demote itself, and only humans promote it. Since release 0.5 the harness applies that rule continuously. Before an automation runs, Vigil checks projected cost and confidence against thresholds your team sets. When either drifts, Vigil steps back and asks a person.

DEMOTEAutomatic, when cost or confidence crosses a threshold
PROMOTEOnly by a person on your team
APPROVEContainment above your confidence bar; everything else routes to an analyst
REPLACEStable workflows become code, not agents
Local models

Your model, your boundary

Vigil works with a local model through Ollama, or with Anthropic Claude or OpenAI through a gateway you control. Teams that cannot send data to a hosted provider run entirely local, and nothing leaves the machine. Teams that use a hosted model choose the endpoint and decide what crosses it.

Local first

Ollama models run with no API key and no egress.

Hosted by choice

Claude or OpenAI with your keys, through your gateway.

Signed releases

Release images are signed keyless and verifiable with cosign.

Secure by default

Authentication is on from first start; no default credentials ship with the repository.

Architecture

Four layers, each yours to change

Workflows
Incident responseFull investigationThreat huntForensicsRoot causeCloud incidentYour WORKFLOW.md
Agents
TriageInvestigatorThreat hunterCorrelatorResponderReporterMITRE analystand six more
Backend
DetectionsCasesApprovalsSimilarity search
MCP servers
SIEMEDRIdentityIntelLogLM (optional)
Data
LogsAlertsFindingsEmbeddingsCasesRules

Contracts endure while implementations change: finding, case, and approval tools sit behind frozen API schemas, and LogLM connects as an MCP integration you enable, not a prerequisite.

With LogLM

Investigate what rules never fire on

Vigil runs your rules-based detections on its own. Enable the LogLM integration and it also receives compound detections from the behavior of your telemetry: the concerning sequences that no signature describes. Together they form a detection and response loop your team owns end to end.

Detect

LogLM scores the full telemetry stream and emits MITRE-mapped findings.

Investigate

Vigil agents assemble evidence, context, and a recommended action.

Close the loop

Red team your own environment, turn what you learn into detections, and measure efficacy inside the loop.

Vigil 1.0

Human on the loop, not in it

Vigil 1.0 performs substantially all of a SOC's day-to-day work while your analysts supervise the system rather than staff its queue. The roadmap is public on GitHub.

Stable contracts

Versioned APIs and MCP tool schemas that integrations can depend on for the long term.

Batteries included, not required

A full console and workflow library out of the box; every piece replaceable by your own.

Policy as code

Declared intent and policies stored as Markdown under change control, per environment.

Detection coverage

Vigil manages the rules an environment runs and reports what they cover and what they miss.

Closed loop

Red team results feed new detections, with efficacy measured in the same loop.

Agent-vendor neutral

The development loop is not tied to any one coding agent or model provider.

Release notes

Current and past releases, with signed images and changelogs.

Vigil Assured

Running Vigil in production?

Vigil stays free under Apache 2.0. Vigil Assured is the maintained, hardened track for teams that run it where it matters, delivered as part of the Intelligent Defense Platform.

Talk to us about Vigil Assured
BUILDSSigned, reproducible, with SBOM and provenance
PROFILESValidated deployment profiles
SUPPORTDefined support lifetime and patch SLAs
EVIDENCEA release evidence pack for your auditors
CONTENTDetect, Hunt, and Validate content channels

Run it, read it, change it

Clone Vigil and reach a running console in minutes. Star the repository to follow releases, and join the community at vigilsoc.org.