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GRC Lake — collect, evaluate, resolve, and export: read-only evidence from cloud, identity, code, and data sources, evaluated through a common control framework and framework packs.

PyPI version Python 3.11+ CI status License: Apache 2.0 OpenSSF Scorecard

Turn security evidence into traceable control assessments and reviewable audit workpapers. GRC Lake is open-source and self-hosted, running in infrastructure you control. Read evidence from existing systems and security lakes, evaluate versioned rules, and preserve the evidence and review decisions behind each result.

Quick start · Product tour · Self-host · Coverage · Documentation

Quick start

Published v0.3.3, with Python 3.11+ on macOS or Linux:

python -m venv .venv
source .venv/bin/activate
pip install "grc-lake[server]==0.3.3"
grc-lake fixtures load --company golden --out ./lake --rebase-times
grc-lake serve --server --allow-insecure-no-auth --lake ./lake --host 127.0.0.1 --port 8787

Open localhost:8787/console/dashboard/. This loopback demo uses synthetic data and disables authentication. First task: find a failing control, inspect its evidence and mapping, then export the frozen result. Follow the sample-data tutorial; continue with the synthetic workpaper example to inspect a scoped test plan, population gaps, and a verified export. Installation time depends on your machine and connection.

Docker, source development, or MCP

The public Docker image needs no registry login. Use the published templates:

git clone --branch v0.3.3 --depth 1 https://github.com/koda-ai-studio/grc-lake.git
cd grc-lake
docker compose up

For current source, with uv and Node 22+:

git clone https://github.com/koda-ai-studio/grc-lake.git
cd grc-lake
uv sync --frozen --extra dev --extra server
make demo-local

For MCP over stdio:

pip install 'grc-lake[mcp]'
GRC_LAKE_LAKE=./lake grc-lake-mcp

See headless GRC for agent credentials and authority.

Product tour

Collect → Evaluate → Resolve → Export. Read source evidence, run version-pinned control tests, investigate findings, and export a scoped assessment.

Start with the assessment overview

See assessed coverage, open findings, evidence freshness, and priority actions together. Framework posture distinguishes assessed results from requirements that still need evidence.

Overview showing assessment coverage, findings, stale evidence, framework posture, and priority actions

Explore framework coverage

Browse framework packs and their requirements, compare mapped and reviewed coverage, and open the controls or mappings that need attention. Catalogued, mapped, reviewed, and assessed requirements remain separate measures.

Frameworks showing requirement coverage, mapping-review status, and the framework roster

Inspect control results and evidence

Filter the control worklist by result, owner, or framework. Open a control to inspect its evidence, findings, freshness, and test confidence.

Controls worklist showing test results, evidence freshness, confidence, and owners

Navigate control families and the CCF

The Common Control Framework (CCF) organizes reusable safeguards into categories and control families. Explore their links to framework requirements and see reviewed and proposed mappings separately. Proposed mappings are not attestable.

Control families grouped by CCF category, with safeguard counts and reviewed versus proposed requirement mappings

How common safeguards work.

Trace a finding through the evidence graph

Follow exact records between controls, safeguards, evidence, repositories, and assets. Select a node to inspect it; drill into its connections or roll up to the previous view. Depth and layer controls keep the investigation focused.

Evidence investigation graph connecting a SOC 2 control to evidence, a repository, an asset, and a safeguard, with a selected-node inspector

Review AI assets and their next actions

The model and agent worklist brings owners, environments, governance signals, and next actions together. Open an asset's linked controls or filtered evidence; recorded lineage remains distinct from freshly evaluated controls.

AI model and agent worklist showing owners, governance signals, and next actions

Screens use the bundled synthetic company, captured for v0.3.3 from source 27b21b16 on 2026-10-12. They show recorded relationships and demo workflows, not a customer deployment or an audit opinion.

Try the five-minute console tour · How evidence investigation works

Architecture

Read-only collection feeds deterministic evaluation. Each published assessment retains verified artifact hashes; snapshots form a hash-chained ledger. Console, REST API, CLI, and MCP expose the same evidence and assessment model.

flowchart LR
  Sources["Cloud · identity · code · existing lakes"] --> Collect["Read-only collection"]
  Collect --> Evaluate["Version-pinned control tests"]
  Evaluate --> Assessments["Assessment generations"]
  Assessments --> Review["Console · API · CLI · MCP"]
  Assessments --> Export["Snapshots · workpapers · OSCAL"]

Use direct connectors or read an existing lake. Both materialize assessment evidence into storage you operate. Reader compatibility, schema mapping, and live qualification vary; check the connector catalog.

Queued assessments retain tenant-scoped result receipts. Pagination, queue admission, response storage, and retries have explicit bounds. See the assessment contract and architecture guide for recovery behavior and API limits.

Self-host

Deploy with Docker or Helm on your infrastructure:

  • Local: one writable replica with a filesystem lake.
  • Distributed: API replicas and tenant workers share PostgreSQL and S3-compatible storage. CLI assessments use a filesystem lake; MCP uses the authenticated API for distributed deployments.
  • Production: configure SSO and scoped credentials, retention, and backup/restore.

See deployment compatibility and the distributed guide. Production capacity and provider failover require deployment-specific qualification.

Release packages include signed provenance and an SBOM. Follow artifact verification. Upgrading from TrustOps? Read the migration guide.

Frameworks

  • 20 framework packs, 2,252 catalogued requirements (1,965 distinct; FedRAMP Moderate's 287 rows restate NIST SP 800-53 controls), linked through 96 common safeguards. 1,417 requirements have safeguard mappings; 339 have reviewed mappings and the other 1,078 are proposed.
  • 25 read-only source adapters (7 in preview), plus OCSF presets for existing security lakes.
  • Fails closed: missing, stale, or partial evidence never produces a pass, and proposed mappings are not attestable.
  • One engine, four surfaces: console, REST API, CLI, and MCP server. Agents propose; humans approve.

Catalog counts above are generated from source. Mapping-review decisions do not change access at a provider.

Framework coverage · Common safeguards · Qualification and test coverage

Fixture tests and screenshots do not establish live-provider accuracy, production capacity, or certification. See audit readiness for qualification boundaries and the roadmap for remaining gaps.

Documentation

Task Guide
Try the console Product walkthrough
Connect evidence Connector setup · Existing lakes
Integrate an agent Headless GRC · Agent API
Operate a deployment Deployment · Operations
Contribute Contributing · Security

All documentation. Regenerate the header and counts with make readme-header; capture demo screens with make demo-screenshots-full. License: Apache 2.0.

Metadata

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