licit-ai-cli 0.2.0

Regulatory compliance for AI-powered development teams

licit

Regulatory compliance for AI-powered development teams.

licit is a standalone CLI tool that tracks AI-generated code provenance, evaluates compliance against regulatory frameworks (EU AI Act, OWASP Agentic Top 10), generates required documentation (FRIA, Annex IV), and works as a CI/CD gate — all without requiring external services or infrastructure.

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The Problem

Teams using AI coding assistants (Claude Code, Cursor, Copilot, Codex) face three gaps:

1. No provenance tracking — Can't distinguish AI-generated code from human-written code at scale.
2. Regulatory requirements — The EU AI Act mandates documentation (FRIA, Annex IV), risk management, and transparency that no existing tool automates.
3. Agentic security risks — Autonomous AI agents introduce risks (prompt injection, privilege escalation) not covered by traditional security tools.

The Solution

licit init       →  Auto-detect project: languages, frameworks, CI/CD, agent configs
licit trace      →  Track code provenance (human vs AI) from git history
licit changelog  →  Monitor agent config changes (CLAUDE.md, .cursorrules, etc.)
licit fria       →  Generate Fundamental Rights Impact Assessment (EU AI Act Art. 27)
licit annex-iv   →  Generate Annex IV Technical Documentation
licit report     →  Unified compliance report (Markdown, JSON, HTML)
licit gaps       →  Find compliance gaps with actionable recommendations
licit verify     →  CI/CD gate: exit 0 (pass) or exit 1 (fail)
licit status     →  Quick compliance overview
licit connect    →  Configure optional connectors (architect, vigil)

Quick Start

Installation

pip install licit-ai-cli

Or from source:

git clone https://github.com/Diego303/licit-cli.git
cd licit-cli
pip install -e ".[dev]"

Requires Python 3.12+

Initialize

cd your-project
licit init

This auto-detects your project and creates .licit.yaml:

  Project: my-app
  Languages: python, typescript
  Agent configs: 2 detected
  CI/CD: github-actions
  Testing: pytest
  Security tools: vigil, semgrep

  Agent configurations found:
    - CLAUDE.md (claude-code)
    - .cursorrules (cursor)

  Created .licit.yaml
  Created .licit/ directory

Track Provenance

licit trace                      # Analyze full git history
licit trace --since 2026-01-01   # Since specific date
licit trace --stats              # Show AI vs human stats
licit trace --report             # Generate Markdown report

Output example:

  Analyzing git history...
  Records: 45 files analyzed
  AI-generated: 18 (40.0%)
  Human-written: 22 (48.9%)
  Mixed: 5 (11.1%)

  AI tools detected: claude-code (15), cursor (3)
  Models detected: claude-sonnet-4 (12), claude-opus-4 (3), gpt-4o (3)

  Stored in .licit/provenance.jsonl

Evaluate Compliance

licit report                         # Full compliance report
licit report --framework eu-ai-act   # EU AI Act only
licit report --format html -o r.html # HTML output
licit gaps                           # What's missing?

CI/CD Integration

# .github/workflows/compliance.yml
- name: Compliance check
  run: |
    pip install licit-ai-cli
    licit verify

Exit codes: 0 = compliant, 1 = non-compliant, 2 = partially compliant.

Frameworks Supported

Framework	Version	Status
EU AI Act	Regulation (EU) 2024/1689	V0
OWASP Agentic Top 10	2026	V0
NIST AI RMF	AI 100-1	Planned (V1)
ISO/IEC 42001	2023	Planned (V1)

EU AI Act Coverage

licit evaluates deployer obligations across key articles:

• Art. 9 — Risk management (guardrails, quality gates, security scanning)
• Art. 12 — Record keeping (git history, audit trails, provenance)
• Art. 13 — Transparency (technical documentation, config changelog)
• Art. 14 — Human oversight (review gates, dry-run, intervention capability)
• Art. 26 — Deployer obligations (agent configuration, monitoring)
• Art. 27 — FRIA (interactive 5-step questionnaire)
• Annex IV — Technical documentation (auto-generated from project metadata)

OWASP Agentic Top 10 Coverage

Maps project security posture against all 10 agentic AI risks including prompt injection, unauthorized code execution, data exfiltration, and privilege escalation.

Agent Configs Detected

licit monitors configuration files for AI coding agents:

File	Agent
CLAUDE.md	Claude Code
.claude/settings.json	Claude Code
.cursorrules	Cursor
.cursor/rules	Cursor
AGENTS.md	GitHub Agents
.github/copilot-instructions.md	GitHub Copilot
.architect/config.yaml	architect
.prompts/**/*.md	Generic

Provenance Tracking

licit uses a multi-method approach to determine code provenance:

Methods

Method	How it works	Confidence
git-infer	6-heuristic scoring of git commits (author, message, bulk, co-authors, file patterns, time)	60-95%
session-log	Parse agent session files (Claude Code JSONL logs)	95%

Heuristics Engine

The git-infer method applies 6 weighted heuristics to each commit:

1. Author pattern (weight 3.0) — Known AI agent names (claude, copilot, cursor, devin, bot)
2. Message pattern (weight 1.5) — Conventional commits, "implement", "generated by", [ai]/[bot]
3. Bulk changes (weight 2.0) — Large file/line counts typical of AI generation
4. Co-author trailer (weight 3.0) — Co-authored-by: with AI keywords
5. File patterns (weight 1.0) — All files are test files
6. Time pattern (weight 0.5) — Commits between 1am-5am

Only heuristics that fire (score > 0) contribute to the weighted average, preventing silent heuristics from diluting strong signals.

Cryptographic Attestation

Provenance records can be cryptographically signed:

provenance:
  sign: true                              # Enable HMAC-SHA256 signing
  sign_key_path: /path/to/key             # Optional: explicit key path
  # Default: auto-generates .licit/.signing-key

• Individual records signed with HMAC-SHA256
• Batch records signed with Merkle tree (root hash)
• Timing-safe verification via hmac.compare_digest

Configuration

All configuration lives in .licit.yaml:

provenance:
  enabled: true
  methods: [git-infer, session-log]       # Methods to use
  session_dirs: []                         # Custom session log directories
  confidence_threshold: 0.6               # Minimum confidence (0.0-1.0)
  sign: false                             # HMAC-SHA256 signing
  sign_key_path: null                     # Custom key path (auto-generates if null)
  store_path: .licit/provenance.jsonl     # Append-only JSONL store

changelog:
  enabled: true
  watch_files:
    - CLAUDE.md
    - .cursorrules
    - AGENTS.md
    - .architect/config.yaml

frameworks:
  eu_ai_act: true
  owasp_agentic: true

connectors:
  architect:
    enabled: false
  vigil:
    enabled: false

reports:
  default_format: markdown
  include_evidence: true
  include_recommendations: true

Optional Connectors

licit is fully standalone — connectors enrich, they don't enable.

Connector	What it reads	Value added
architect	.architect/reports/, config	Guardrails, quality gates, budget limits, audit trail
vigil	SARIF files	Security findings with severity levels

licit connect architect --enable
licit connect vigil --enable

Project Structure

src/licit/
├── cli.py                              # 10 CLI commands (Click)
├── config/                             # Pydantic v2 config schema + YAML loader
├── core/                               # Models, project detection, evidence collection
├── provenance/                         # ✅ Phase 2 — complete
│   ├── heuristics.py                   # 6-heuristic AI commit scoring engine
│   ├── git_analyzer.py                 # Git history analysis + agent/model inference
│   ├── store.py                        # Append-only JSONL provenance store
│   ├── attestation.py                  # HMAC-SHA256 signing + Merkle tree
│   ├── tracker.py                      # Orchestrator (git + sessions + sign + store)
│   ├── report.py                       # Markdown report generator
│   └── session_readers/                # Agent session log parsers
│       ├── base.py                     # SessionReader Protocol
│       └── claude_code.py              # Claude Code JSONL reader
├── changelog/                          # Agent config monitoring (Phase 3)
├── frameworks/                         # EU AI Act, OWASP (Phase 4-5)
├── connectors/                         # architect + vigil (Phase 7)
├── reports/                            # Unified reports, gap analysis (Phase 6)
└── logging/                            # structlog configuration

Documentation

Full documentation (in Spanish) is available in the docs/ directory:

• Quick start — Get licit running in 5 minutes
• Architecture — System design, modules, and phases
• CLI guide — Complete command reference
• Configuration — All .licit.yaml fields
• Data models — Enums, dataclasses, and Pydantic schemas
• Security — Threat model, signing, data protection
• Compliance — EU AI Act and OWASP framework details
• Best practices — Integration recommendations
• Development — Contributing guide
• FAQ — Troubleshooting and common questions

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests (280 tests)
pytest tests/ -q

# Lint
ruff check src/licit/

# Type check
mypy src/licit/ --strict

Philosophy

• Standalone — Works without any external tools. Connectors are optional.
• Filesystem-first — Everything is files in .licit/. No database, no server.
• Developer-first — CLI that fits into existing git/CI workflows.
• Language-agnostic — Detects Python, JS/TS, Go, Rust, Java projects.
• Provenance-first — Understands code origin (AI vs human) for more accurate evaluations.

Roadmap

Version	Key Features
V0 (current)	CLI, provenance tracking (git + Claude Code sessions), EU AI Act, OWASP, FRIA, Annex IV, CI/CD gate
V0.x	Cursor/Codex session readers, PDF reports, GitHub Action
V1	NIST AI RMF, ISO 42001, plugin system, Sigstore, MCP Server
V2	Web dashboard, multi-project, trend analysis, AI remediation

License

MIT — Copyright (c) 2026 Diego Alba Ruiz