[Adlor CAIQ] — AI Vendor Risk Assessment CLI & Schema
Open-Source AI Governance & Due Diligence Toolkit
Aligned to CSA AI-CAIQ v1.0, NIST AI RMF 1.0, ISO/IEC 42001:2023, and EU AI Act (2024/1689).
📌 Overview
adlor-caiq is a lightweight, zero-dependency CLI and schema evaluator that automates third-party AI vendor risk assessments. It provides an auditable 34-question structured framework across 6 core risk domains, generates executive markdown scorecards, detects critical compliance gaps, and provides remediation roadmaps before enterprise procurement.
Key Capabilities
- 📋 Standardized 34-Question Bank: Mapped directly to CSA AI Controls Matrix, NIST AI RMF functions, ISO 42001 clauses, and EU AI Act articles.
- ⚖️ Weighted Scoring Engine: Domain-level and composite risk calculation (0–100) with risk tier classification (
LOW_RISK,MEDIUM_RISK,HIGH_RISK,CRITICAL_RISK). - 🔍 Gap Analysis & Remediation: Automatically flags missing, unanswered, or weak-evidence responses with actionable procurement recommendations.
- 📊 Multi-Format Reporting: Export reports in Markdown (Executive Scorecard), JSON (CI/CD integration), and CSV (Procurement spreadsheets).
- ⚖️ Side-by-Side Vendor Comparison: Benchmark two AI vendors head-to-head with delta scoring.
- ⚡ Model Context Protocol (MCP): FastMCP server exposing scoring and question bank tools to Claude, Cursor, and Windsurf AI agents.
🚀 Quick Start
Installation
pip install adlor-caiq
Or install with MCP server support:
pip install "adlor-caiq[mcp]"
🛠️ CLI Usage
1. Initialize a Blank Assessment Template
Generate a pre-filled vendor-assessment.yaml with all 34 questions and guidance notes:
adlor-caiq init -o my-vendor.yaml
2. Score a Completed Assessment
Evaluate the completed questionnaire and view domain-level risk scorecards in the terminal:
adlor-caiq score my-vendor.yaml
3. Export Formatted Reports
Generate an Executive Summary in Markdown, JSON, or CSV:
# Export Markdown scorecard
adlor-caiq export my-vendor.yaml --format md -o report.md
# Export JSON for automated CI/CD gating
adlor-caiq export my-vendor.yaml --format json -o report.json
# Export CSV for procurement teams
adlor-caiq export my-vendor.yaml --format csv -o gaps.csv
4. Benchmark Two Vendors
Compare two competing AI SaaS solutions:
adlor-caiq compare vendor-a.yaml vendor-b.yaml
🏛️ Assessment Domains & Framework Alignment
| Domain | Questions | CSA AI-CAIQ | NIST AI RMF | ISO 42001 | EU AI Act |
|---|---|---|---|---|---|
| AI Governance & Accountability | 6 | AIS-01 to 06 | GOVERN 1.1–4.1 | Clauses 5.2, 5.3, 6.1.2 | Articles 9, 14, 60 |
| Data Provenance & Privacy | 6 | DSP-01 to 06 | MAP 2.1–3.1 | Annex A.6, A.8, A.9 | Articles 10, 15 |
| Model Transparency & Explainability | 5 | AIE-01 to 05 | MAP 1.1–1.2, MEASURE 2.6 | Annex A.5, A.7 | Articles 11, 13, 53 |
| Security & Adversarial Resilience | 6 | SEC-01 to 06 | MEASURE 2.7–2.8 | Annex A.8.1–A.8.5 | Articles 12, 15 |
| Operational Reliability & SLA | 5 | OPS-01 to 05 | MANAGE 1.1–4.2 | Clauses 7.4, 8.1–8.3 | Articles 9, 12, 15 |
| Regulatory Compliance & Ethics | 6 | REG-01 to 06 | MAP 5.1, MEASURE 2.10 | Annex A.7.3, A.9.3 | Articles 10, 27, 50, 82 |
📄 License & Attribution
- License: MIT
- Maintained by: Adlor Labs
- Primary Source Standard: Cloud Security Alliance (CSA) AI Controls Matrix (AICM) & Consensus Assessment Initiative Questionnaire (AI-CAIQ).
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