GRC Lake

[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] [OpenSSF Best Practices]

Turn security evidence into traceable control assessments and reviewable audit workpapers. Open-source and self-hosted: read evidence from the systems and security lakes you already run, evaluate versioned control tests, and retain evidence and recorded review decisions alongside the results.

• Traceable: follow results to source records and rule versions, and inspect the recorded mapping decisions; snapshots form a hash-chained ledger you can re-verify.
• Works with what you have: connect cloud, identity, endpoint, and code systems directly, or query OCSF tables already in your lake.
• One control, many frameworks: reusable safeguards map to SOC 2, ISO 27001, NIST, and more, with reviewed and proposed mappings kept apart.

Open the live demo → Click through the console on recorded synthetic data. The hosted demo is read-only; it does not connect to your systems or establish live-provider coverage. Nothing to install.

Quick start · Product tour · Connect evidence · Already on OCSF? · Frameworks · Performance · Self-host · Docs

Quick start

Python 3.11+ and uv:

uvx --from "grc-lake[server]" grc-lake demo

This loads a synthetic company into a temporary lake, serves the console on localhost:8787 without authentication, and opens your browser. Ctrl+C stops it and deletes the lake. With pip: pip install "grc-lake[server]", then grc-lake demo.

First task: find a failing control, inspect its evidence and mapping, and export the frozen result: follow the sample-data tutorial, then the auditor workpaper example. Their CLI steps need the lake to outlive the demo, so start it with grc-lake demo --lake ./lake.

Exploring AI assets? The AI discovery walkthrough in 0.3.21 opens a synthetic model-and-agent inventory with one command, then follows recorded evidence into the graph and assessment.

For a real evaluation, follow the bounded pilot guide: one source, one independently checked result, then a repeat collection and review.

Use it in CI. From v0.3.24, one step posts a framework readiness table to the job summary and can gate the merge (CI gate).

- uses: koda-ai-studio/[email protected]  with:    grc-lake-url: ${{ secrets.GRC_LAKE_URL }}    api-token: ${{ secrets.GRC_LAKE_API_TOKEN }}

grc-lake fixtures load --company golden --out ./lake --rebase-timesgrc-lake serve --server --allow-insecure-no-auth --lake ./lake

git clone --branch v0.3.26 --depth 1 https://github.com/koda-ai-studio/grc-lake.gitcd grc-lake && docker compose up

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

pip install "grc-lake[server,mcp]"GRC_LAKE_LAKE=./lake grc-lake-mcp

Product tour

Collect → Evaluate → Resolve → Export. These console captures use the bundled synthetic company, refreshed for the 0.3.24 release candidate. They illustrate source behavior; see the release records for published-package verification.

Start with the assessment overview

Assessed coverage, open findings, evidence freshness, and priority actions in one view.

Explore framework coverage

Catalogued, mapped, reviewed, and assessed requirements stay separate measures per framework.

Inspect control results and evidence

Filter by result, owner, or framework; open a control for its evidence, findings, freshness, and confidence.

Navigate control families and the CCF

The Common Control Framework (CCF) groups reusable safeguards into families linked to framework requirements. Proposed mappings are not attestable. How common safeguards work.

Trace a finding through the evidence graph

Follow exact records between controls, safeguards, evidence, repositories, and assets; drill in or roll up through a bounded neighborhood. Inspect control-test results, evidence freshness, and linked findings with their owners and next actions. Recorded links and mapping review states stay distinct; a passing control test does not establish framework attestation.

For node labels and inspector details, open the full-size graph: light theme · dark theme.

Review AI assets and their next actions

Models and agents with owners, environments, governance signals, and next actions. Recorded lineage is evidence presence, not a passing control (AI inventory semantics).

Five-minute console tour · How evidence investigation works

Architecture

Read-only collection feeds deterministic evaluation; console, REST API, CLI, and MCP share one 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"]

Exports: portable CCF/OSCAL bundles, framework coverage reports, and verifiable auditor workpapers. Recovery behavior and API limits: architecture guide and assessment contract.

Connect evidence

Use direct connectors or read an existing lake. Both are read-only and keep assessment evidence in storage you operate.

Already on OCSF?

If your telemetry is already normalized to OCSF, for example in Amazon Security Lake or OCSF tables on Databricks or Snowflake, point GRC Lake at those tables. Keep your existing source-ingestion pipeline: GRC Lake runs read-only queries, retains a mapped copy and evaluates the rows it imports. Built-in presets bind events to CCF safeguards, and framework requirements follow each safeguard's reviewed crosswalk:

flowchart LR  A["OCSF Authentication 3002<br/>successful Logon, is_mfa"] --> S1["SG-IDENTITY-001<br/>Logical access and MFA"]  B["OCSF Account Change 3001<br/>Attach Policy"] --> S2["SG-IDENTITY-002<br/>Access review"]  G["OCSF Group Management 3006<br/>Add User, Assign Privileges"] --> S2  V["OCSF Vulnerability Finding 2002<br/>EC2 instance, ECR image"] --> S3["SG-VULNERABILITYMANAGEMENT-001<br/>Vulnerability management"]  C["OCSF Compliance Finding 2003<br/>EC2 instance"] --> S4["SG-CONFIGBASELINE-001<br/>Hardened configuration baselines"]  S1 --> R1["SOC 2 CC6.1 · NIST 800-53 IA-2<br/>CIS AWS 1.10 · …"]  S2 --> R2["SOC 2 CC6.2 · ISO 27001 A.8.2 · …"]  S3 --> R3["SOC 2 CC7.1 · ISO 27001 A.8.8<br/>NIST 800-53 RA-5 · …"]  S4 --> R4["NIST CSF PR.PS-01 · …"]

CREDS='{"host":"dbc-example.cloud.databricks.com","warehouse_id":"abc123","catalog":"security",        "schema":"ocsf","client_id":"<sp-client-id>","client_secret_ref":"DATABRICKS_CLIENT_SECRET"}'OPTS='{"mappings":[{"preset":"ocsf/authentication","source":{"table":"ocsf_events"}},                   {"preset":"ocsf/account_change","source":{"table":"ocsf_events"}}]}'grc-lake connectors probe --lake ./lake --connector-id databricks-evidence-lake \  --credentials-json "$CREDS" --options-json "$OPTS"grc-lake connectors configure --lake ./lake --connector-id databricks-evidence-lake \  --state enabled --credentials-json "$CREDS" --options-json "$OPTS"grc-lake connectors sync --lake ./lake --connector-id databricks-evidence-lakegrc-lake frameworks assessment --lake ./lake

Frameworks

Coverage means several different things: a catalogued requirement, a reviewed mapping and an assessed result are separate. Start with the per-framework coverage matrix; proposed mappings cannot establish an attested pass.

Framework coverage · Common safeguards · Audit readiness · Qualification

Performance and qualification

The accepted local graph profile exercises two tenants with up to 10k synthetic records each and four concurrent clients. Its limits are successful warm p95 ≤500 ms, first/expired reads ≤5 seconds and process RSS ≤1 GiB, with both tenants continuing to progress under overload. Three recorded runs passed. Admission and rate-limit rejections are reported separately; this is not a promise that every request under overload succeeds.

This profile covers authenticated depth-one graph reads on local SQLite. It does not qualify 100k interactive use, browser rendering, external storage or production sizing. Check provider qualification and verified release records separately.

Bounded live runs are recorded as receipts with every verdict checked against the provider: an AWS and GitHub pilot that moved from no framework results to 103 failing requirements and one attested pass, and a Databricks run on a populated audit log. Each covers one account or workspace, not provider-wide qualification.

Self-host

Deploy with Docker or Helm on infrastructure you control:

• Local: one writable replica with a filesystem lake.
• Distributed: API replicas and tenant workers share PostgreSQL and S3-compatible storage (distributed guide).
• Production: SSO and scoped credentials, retention, and backup/restore.

Releases ship signed provenance and an SBOM (verify artifacts). Upgrading from TrustOps? Read the migration guide.

Engineering review

Start with one control and follow its evidence into a verified workpaper: the synthetic access/change workflow runs passing, failing, missing, stale, conflicting, and incomplete-population cases.

Documentation

All documentation · License: Apache 2.0