Regula

Find AI governance risks that may be visible in source code. Covers the EU AI Act, South Korea AI Basic Act, and Colorado SB 26-189, with questionnaires for context that code cannot show.

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Table of contents

• What it does
• Choose how to start
• Quick start
• What Regula tells you
• Key commands
• Who is this for?
• What Regula is (and isn't)
• Bias evaluation: methodology and ethics
• Important limitations
• Verified numbers
• Contributing
• Licence

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What it does

Regula scans a local source-code folder for patterns that may need AI governance review. A questionnaire records facts that code cannot show, such as where and how a system will be used. Results identify candidate risk categories and link to provisions that may be relevant. Regula does not determine legal classification, compliance, or the obligations that apply to a real deployment.

Reference material covers the EU AI Act, South Korea's AI Basic Act (Act No. 20676), and Colorado SB 26-189. The core install has no required third-party runtime dependencies. Optional extras add dependencies, and some optional commands or configured features can contact external services. Assess territorial scope and data-processing duties independently.

In plain English: give Regula a source-code folder and answer questions about how the system will be used. It returns possible issues and a review trail so a developer, governance lead, or adviser can decide what to investigate next. A scan with no findings does not prove that a system is compliant or low risk.

Choose how to start

If you want to…	Start here
Explore the questions without installing anything	Use the browser assessment. It records declared context for review; it does not inspect your code or make a legal determination.
Check a local codebase	Follow the Quick start, then run regula check ..
Evaluate Regula before adopting it	Follow the 10-minute example journey and read the documented limitations and verification evidence.
Add a repeatable team check	Use the CI/CD example and review the exit-code policy before making it blocking.
Prepare material for a human reviewer	Generate a reviewer-completable evidence scaffold, then complete and validate its contextual fields.

Quick start

pipx install regula-ai      # or: pip install regula-ai / uv pip install regula-ai

Not sure if the AI Act applies? No code needed:

regula assess               # record declared context for human review

Want to scan your code?

regula check .              # 419 tier patterns, 8 language families; runtime varies
regula check . --jurisdictions eu,korea,colorado  # all 3 jurisdictions

Need a review pack?

regula evidence-pack --project .   # reviewer-completable evidence scaffold
regula conform --sign --timestamp  # integrity metadata (requires regula[signing])

Generated files are inputs to human review, not an audit opinion, certification, or proof of compliance.

Just want to see it work? (requires the cloned repo : examples/ is not bundled in the pip package)

git clone https://github.com/kuzivaai/getregula && cd getregula
regula demo                 # scan a bundled example project

Install details

The recommended install is pipx : it isolates Regula from your system Python and avoids the externally-managed-environment error on Ubuntu 22.04+, Debian 12+, Fedora, Arch, and Homebrew Python.

If you don't have pipx yet, install it first (one-time):

Platform	Install pipx
macOS	brew install pipx && pipx ensurepath
Debian / Ubuntu	sudo apt install pipx && pipx ensurepath
Fedora	sudo dnf install pipx && pipx ensurepath
Arch	sudo pacman -S python-pipx && pipx ensurepath
Windows	python -m pip install --user pipx && python -m pipx ensurepath

Already using uv? uvx --from regula-ai regula runs it with no install step (the --from flag is required because the PyPI package name regula-ai differs from the CLI name regula). Or install it permanently with uv tool install regula-ai.

Running inside a venv or conda env? pip install regula-ai works fine there : the PEP 668 restriction only applies to system Python.

See docs/installation.md for troubleshooting (externally-managed-environment, command not found: regula after install, PATH setup per shell).

Try it against a known high-risk fixture:

regula check examples/cv-screening-app --scope all

The --scope all flag is needed because Regula's default scope (production) skips example directories. This fixture intentionally triggers an Annex III Category 4 (Employment) high-risk classification.

See examples/ for runnable reference projects covering each EU AI Act risk tier, or walk through the full 10-minute evaluation journey in examples/cv-screening-app/ : install, scan, plan, gap, conform, verify, handoff to red-team tooling.

For a deeper first-time-user walk-through (policy tuning, CI integration, baselining) see docs/QUICKSTART.md. The full documentation is indexed by type (tutorials / how-to / reference / explanation) in docs/README.md.

CI/CD

# .github/workflows/regula.yaml
name: AI Governance Check
on: [push, pull_request]
jobs:
  regula:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: kuzivaai/getregula@v1
        with:
          path: '.'
          upload-sarif: 'true'
          fail-on-prohibited: 'true'

What Regula tells you

The EU AI Act defines four risk tiers. Regula maps code patterns to each:

Tier	Action	What it means
Potentially prohibited (Article 5)	Block indicator	Reports code patterns associated with listed practices for urgent contextual review.
Potentially high-risk (Article 6 and Annex III)	Warn + review map	Reports candidate use categories and maps provisions that may be relevant if a reviewer confirms the legal classification.
Potential transparency duty (Article 50)	Transparency indicator	Reports chatbot, synthetic-content and related implementation signals; the applicable paragraph and exceptions require review.
No elevated code indicator	Log only	Means the scanner did not observe a configured elevated indicator. It does not establish minimal-risk status or absence of legal duties.

Every finding includes the relevant Article reference and explains when exceptions may apply. Regula flags patterns -- it does not make legal determinations.

Multi-jurisdiction support. Beyond the EU AI Act, Regula maps risk patterns to South Korea's AI Basic Act (Act No. 20676, in force 22 January 2026) and Colorado SB 26-189 (disclosure-focused, plus consumer correction and human-review rights, duties from 1 January 2027). Use --jurisdictions eu,korea,colorado on regula check or --jurisdiction korea on regula assess to apply the relevant framework. Each jurisdiction has its own YAML config (references/jurisdictions/) and tailored web questionnaire.

Developer guides on getregula.com: Python | JavaScript | Healthcare | Recruitment | Article 5 | Article 9 | Article 14 | Article 50

Key commands

Command	What it does
regula	Scan the current directory, show an indicator summary and next steps
regula check .	Detailed risk scan with per-file findings
regula comply	EU AI Act obligation checklist with completion status
regula gap --project .	Compliance gap assessment against Articles 9-15
regula plan --project .	Prioritised remediation plan based on gap results
regula fix --project .	Generate compliance fix scaffolds for findings
regula evidence-pack --project .	Reviewer-completable evidence scaffold with integrity metadata
regula conform --project .	Article 43 conformity assessment evidence pack
regula dpv --project .	Export the risk indication as DPV-AIAct JSON-LD (aligned to the DPVCG EU-AIAct vocabulary) for RDF/GRC tooling
regula check --ci .	CI mode -- exit code 1 on any WARN or BLOCK finding, SARIF output
regula assess	Structured declared-context questionnaire for applicability and risk review
regula demo	Scan a bundled example project -- zero-commitment trial
regula api-server	Start the REST API (localhost:8487) with web dashboard
regula conform --organisational	Governance self-assessment for Articles 9/17/27/72
regula questionnaire	Context-driven risk assessment questionnaire (also via REST API)
regula exempt	Article 6(3) high-risk exemption decision tree
regula oversight .	Article 14 human oversight analysis (cross-file flow tracing)
regula guardrails .	Article 15 guardrail implementation coverage detection
regula owasp-agentic	OWASP Top 10 for Agentic Applications assessment
regula monitor	Runtime monitoring for AI applications (Article 12)
regula gdpr	GDPR cross-reference scan (14 focused checks, 4 AI Act/GDPR hotspots)
regula bias	CrowS-Pairs bias evaluation (1,508 sentence pairs) with optional BBQ benchmark. Aligned with Digital Omnibus bias-testing safeguards (Article 4a, COM(2025)836).
regula mcp-server	MCP server (JSON-RPC stdio) exposing three tools : regula_check, regula_classify, regula_gap : for Claude Code, Cursor, and other MCP clients
regula install <platform>	Set up pre-commit hooks, git hooks, or Claude Code/Copilot/Windsurf integration

Regula has 62 commands in total. Run regula --help-all for the full list, or see docs/cli-reference.md.

REST API and web dashboard

For GRC integration or non-terminal users:

python3 scripts/api_server.py --port 8487
# Open http://localhost:8487/v1/dashboard

Seven endpoints: /health, /v1/check, /v1/classify, /v1/gap, /v1/questionnaire, /v1/questionnaire/evaluate, /v1/dashboard. All return the same JSON envelope as the CLI. No auth -- run behind a reverse proxy for remote access.

Who is this for?

• Solo founders and indie hackers building AI products who need an initial list of code patterns to investigate before contextual and legal review.
• Small teams who want to understand their compliance exposure before it becomes a sales blocker. Enterprise procurement is already asking for AI Act evidence.
• Engineering teams who want EU AI Act scanning in CI/CD to catch high-risk or prohibited patterns before they ship.
• AI governance consultants and advisors : run Regula on a client's codebase to produce code-observation reports, gap-review scaffolds, and hash-manifested documentation for completion and review within a broader governance engagement. Selected generated facts have repository checks; limitations and reproduction commands are recorded in the trust pack. Deliverables can carry engagement metadata (client, preparer, reference) via the engagement: policy section or --client/--prepared-by/--engagement-ref flags. See the consultant guide for the workflow and its boundaries.

What Regula is (and isn't)

Regula is:

• A development-time compliance tool that combines static code analysis with governance questionnaires, mapping both to obligations across 3 jurisdictions (EU AI Act, South Korea AI Basic Act, Colorado SB 26-189)
• A shift-left code-indicator scanner -- like ESLint for governance review, running in your terminal or CI/CD pipeline
• A questionnaire-based assessment tool for organisational obligations that code patterns cannot verify (Articles 9, 17, 27, 72)
• Pattern-based risk indication across 3 jurisdictions, not a legal compliance certificate
• A starting point for compliance awareness, not a finish line

Regula is not:

• A runtime monitoring system (it analyses source code, not running systems)
• A legal compliance certificate (findings are indicators, not legal determinations)
• A replacement for enterprise GRC platforms like Credo AI or Holistic AI (it complements them)
• A production fairness testing platform (regula bias runs benchmark probes against a local model as a starting point, but does not replace runtime fairness monitoring)
• Legal advice (consult qualified legal counsel for compliance decisions)

Regula helps development teams understand their EU AI Act exposure early. It does not replace the organisational, procedural, and legal work required for full compliance. For a detailed account of what falls outside Regula's scope, see docs/what-regula-does-not-do.md, and for Regula's own model card (intended use, training data, evaluation, known failure modes) see docs/MODEL_CARD.md.

Bias evaluation: methodology and ethics

regula bias runs two social-bias benchmarks against a locally-hosted language model (Ollama, llama3.2/mistral/qwen variants supported) as evidence for EU AI Act Article 10 data-governance documentation.

Benchmark	Paper	Method	What it measures
CrowS-Pairs	Nangia et al., 2020	Log-probability difference between stereotypical and anti-stereotypical sentence pairs	Intrinsic bias in masked/causal LM output
BBQ	Parrish et al., 2022	Question-answering on ambiguous-context prompts	Bias surfacing in downstream QA behaviour

Both include Wilson confidence intervals for small-sample reliability and bootstrap CIs for distribution estimates. Full methodology lives in scripts/bias_eval.py and docs/benchmarks/PRECISION_RECALL_2026_04.md.

Ethics statement. CrowS-Pairs and BBQ stereotype pairs are used solely for scientific evaluation of model behaviour under controlled conditions. Regula does not display individual stereotype pairs in terminal output or reports : only aggregated scores, confidence intervals, and benchmark-level verdicts. The pairs are distributed under the dataset's own licence (CC BY-SA 4.0 for CrowS-Pairs) and are not redistributed or modified by Regula. Opinions encoded in the stereotype pairs do not reflect the views of the maintainer, Regula contributors, or any user running the tool; their presence is instrumental, not endorsing. regula bias is a development-time starting point for bias documentation, not a production fairness monitor : see "What Regula is (and isn't)" above.

Important limitations

Regula performs pattern-based risk indication, not legal risk classification.

• The EU AI Act classifies risk based on intended purpose and deployment context (Article 6), not code patterns. Regula's findings are indicators that warrant human review.
• False positives will occur. Blind-labelled benchmark on 50 randomly selected Python AI repos measured 83.5% precision on production code (N=115, measured on v1.7.0, labelled by a single reviewer with no inter-rater agreement measurement). Per-tier: ai_security (85%), agent_autonomy (83%), limited_risk (88%), minimal_risk (100%). The high_risk tier (33%, N=6) is statistically unmeasurable at this sample size. Full methodology, corpus selection, and reproduction steps: benchmarks/README.md.
• TypeScript findings are advisory: 0% precision on the current benchmark (6 FP, 0 TP). Language-specific AST gating is not yet implemented for TypeScript.
• False negatives will occur. Novel risk patterns not in the database will be missed.
• Article 5 prohibitions have conditions and exceptions that require human judgment.
• The audit trail is self-attesting (locally verifiable, not externally witnessed).
• This is not a substitute for legal advice or DPO review.

Verified numbers

What	Count
CLI commands	62
Risk detection patterns (regexes)	419
Language families scanned	8 (Python, JS, TS, Java, Go, Rust, C/C++, Jupyter)
Compliance frameworks mapped	13
Tests (pytest --collect-only, all passing)	2,722
Required production dependencies	0

For reproduction commands, version-bounded benchmarks, known exceptions, security posture, and audit-trail design, see docs/TRUST.md. What version numbers promise, the public API they cover, and the deprecation policy: docs/VERSIONING.md.

Privacy and data handling

Regula runs entirely on your machine. No code, findings, or metadata are transmitted to any external service. There is no account system, no API key, no telemetry by default (crash reporting requires both regula telemetry enable and an endpoint you configure yourself via REGULA_SENTRY_DSN; published builds ship none, and DO_NOT_TRACK suppresses it regardless). The tool reads your source files, analyses them locally, and writes output to your local filesystem. Network access is only used when you explicitly request it (RFC 3161 timestamps via --timestamp). See SECURITY.md for the full security posture.

Contributing

Bug reports and pull requests are welcome.

• Run pytest tests/ -q before opening a PR.
• Pattern additions go in scripts/risk_patterns.py. Each pattern should have a corresponding test.
• Regula is intentionally risk indication, not legal classification. New patterns should be conservative -- false positives erode trust more than false negatives for a developer tool.
• See CONTRIBUTING.md for the full contributor guide and CHANGELOG.md for version history.

Authorship

Regula is maintained by Kuziva Muzondo. Where commits identify a co-author, that attribution records the tools or collaborators involved. The maintainer remains accountable for reviewing and accepting every merged change.

Licence

Engine and CLI: Apache License 2.0 OR European Union Public Licence v. 1.2 : at your option. Pick the one that fits your context:

• Apache 2.0 includes an explicit patent grant, making it the preferred choice for enterprise adoption, commercial redistribution, and any context where patent clarity matters.
• EUPL-1.2 is explicitly recognised inside EU institutions and public-sector procurement, is strongly-copyleft on software, and has a formal compatibility appendix (GPL v2/v3, AGPL v3, OSL, EPL, CeCILL, MPL 2.0, LGPL, CC BY-SA 3.0) for downstream projects. If you work with a European public administration, EUPL is often the required or preferred licence.

You may choose either licence for any use. You do not need to state which one you picked, but attribution (keep the copyright notice and NOTICE file) is required under both.

Risk patterns and regulatory data: Detection Rule License (DRL) 1.1. You may use, modify, and redistribute the patterns freely. Attribution is required if you redistribute the patterns or use them in a product. If your tool generates match output from these patterns, the output must credit the source.

The SPDX expression for the full package is (Apache-2.0 OR EUPL-1.2) AND LicenseRef-DRL-1.1.