{
  "markdown": "# dingdawg-compliance\n\n**Colorado SB 205 AI Act compliance scanner.** Run it in 60 seconds. Get your score. Know your gaps before June 30, 2026.\n\n```bash\npip install dingdawg-compliance\npython3 -m dingdawg_compliance scan\n```\n\n---\n\n## What it does\n\nColorado SB 205 requires any company using AI for consequential decisions (employment, housing, credit, insurance, healthcare, education) to:\n\n- Conduct impact assessments before deployment\n- Disclose AI use to consumers at point of decision\n- Provide appeal and human review mechanisms\n- Designate a Responsible AI Officer\n- Test for discriminatory bias\n- Maintain a 3-year audit trail\n\nThis tool scores your readiness across all 25 SB 205 controls. **Free. No signup. Runs locally.**\n\n---\n\n## Install\n\n```bash\npip install dingdawg-compliance\n```\n\nRequires Python 3.9+. No external dependencies — stdlib only.\n\n---\n\n## Usage\n\n### Interactive scan (recommended)\n\n```bash\npython3 -m dingdawg_compliance scan\n```\n\nWalk through all 25 controls. Answer y/n/skip for each. Get your score at the end.\n\n**Example output:**\n```\n──────────────────────────────────────────────────────\n  Overall Score: 44/100  [████████░░░░░░░░░░░░]  NEEDS WORK\n──────────────────────────────────────────────────────\n\n  Category Scores:\n    ~ scope                  100%\n    ✗ impact_assessment        0%\n    ✗ transparency            33%\n    ✗ appeal                   0%\n    ~ governance              50%\n    ✗ bias_testing             0%\n    ✗ data_governance          0%\n    ✗ incident_response       50%\n    ✓ audit                  100%\n\n  ⚠ Critical gaps (2) — mandatory under SB 205:\n    • CO-3   Pre-Deployment Impact Assessment\n    • CO-6   Consumer Disclosure at Point of Decision\n\n  Need the full remediation report?\n  → dingdawg.com/compliance  (CO SB 205 gap report — $199)\n```\n\n### Score from a JSON file\n\n```bash\npython3 -m dingdawg_compliance score responses.json\n```\n\nFormat for `responses.json`:\n```json\n{\n  \"CO-1\": true,\n  \"CO-2\": true,\n  \"CO-3\": false,\n  \"CO-4\": null\n}\n```\n\n`true` = implemented, `false` = not implemented, `null` = unknown (scored as not implemented).\n\n### List all 25 controls\n\n```bash\npython3 -m dingdawg_compliance controls\n```\n\n---\n\n## Use as a library\n\n```python\nfrom dingdawg_compliance import calculate_co_sb205_score, CO_SB_205_CONTROLS\n\n# Score a self-assessment\nresponses = {\n    \"CO-1\": True,   # scope: identified consequential decisions\n    \"CO-3\": False,  # impact_assessment: no pre-deployment assessment yet\n    \"CO-6\": True,   # transparency: consumer disclosure implemented\n    # ... rest of controls\n}\n\nresult = calculate_co_sb205_score(responses)\nprint(result[\"score\"])           # 0-100\nprint(result[\"gaps\"])            # list of unimplemented controls\nprint(result[\"critical_gaps\"])   # CO-3, CO-6, CO-10, CO-14 if missing\n```\n\n### Track assessments in SQLite\n\n```python\nfrom dingdawg_compliance import ComplianceStore, ComplianceScorer, ComplianceFramework\n\nstore = ComplianceStore()  # stored at ~/.dingdawg/compliance/compliance.db\n\n# Register and assess a control\nstore.assess_control(\"CO-3\", status=\"COMPLIANT\", assessor=\"legal-team\", notes=\"Completed Q1 2026\")\n\n# Score\nscorer = ComplianceScorer(store)\nprint(scorer.overall_posture_score())   # e.g. 72.0\nprint(scorer.per_framework_score())     # per-framework breakdown\nprint(scorer.gap_analysis())            # prioritized gap list\n```\n\n### Automated checks (read-only)\n\n```python\nfrom dingdawg_compliance import AutoAssessor\nfrom pathlib import Path\n\nassessor = AutoAssessor(\n    base_dir=Path(\"./src\"),\n    db_paths=[Path(\"./data/app.db\")]\n)\n\nresults = assessor.run_all_checks()\nprint(results[\"checks\"][\"access_controls\"][\"summary\"])\nprint(results[\"checks\"][\"audit_logging\"][\"summary\"])\n```\n\n---\n\n## The 25 CO SB 205 Controls\n\n| ID | Category | Control | Critical |\n|----|----------|---------|---------|\n| CO-1 | scope | Consequential Decision Identification | |\n| CO-2 | scope | High-Risk AI System Classification | |\n| CO-3 | impact_assessment | Pre-Deployment Impact Assessment | ★ |\n| CO-4 | impact_assessment | Annual Impact Assessment Review | |\n| CO-5 | impact_assessment | Impact Assessment Documentation | |\n| CO-6 | transparency | Consumer Disclosure at Point of Decision | ★ |\n| CO-7 | transparency | Disclosure Timing | |\n| CO-8 | transparency | Disclosure Content — AI Role | |\n| CO-9 | transparency | Disclosure Content — Data Used | |\n| CO-10 | appeal | Appeal Mechanism | ★ |\n| CO-11 | appeal | Human Review Option | |\n| CO-12 | appeal | Opt-Out Mechanism | |\n| CO-13 | appeal | Appeal Response Timeline | |\n| CO-14 | governance | Responsible AI Officer Designation | ★ |\n| CO-15 | governance | AI Inventory | |\n| CO-16 | governance | Vendor Due Diligence | |\n| CO-17 | governance | Policy Documentation | |\n| CO-18 | bias_testing | Pre-Deployment Bias Testing | |\n| CO-19 | bias_testing | Ongoing Bias Monitoring | |\n| CO-20 | bias_testing | Protected Class Analysis | |\n| CO-21 | data_governance | Training Data Documentation | |\n| CO-22 | data_governance | Data Minimization | |\n| CO-23 | incident_response | AI Incident Response Plan | |\n| CO-24 | incident_response | Error Notification | |\n| CO-25 | audit | Third-Party Audit Trail | |\n\n★ Critical — mandatory remediation required before June 30, 2026.\n\n---\n\n## What this doesn't include\n\nThis scanner shows **what** to check and **where your gaps are**. It does not generate remediation plans, regulatory citations, evidence templates, or audit-ready documentation.\n\nFor the full gap report with remediation guidance → [dingdawg.com/compliance](https://dingdawg.com/compliance)\n\n---\n\n## License\n\nApache 2.0 — free to use, fork, and contribute.\n\n## Contributing\n\nPRs welcome for new indicators, additional frameworks, or CLI improvements. Open an issue first for anything structural.\n",
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