The "OWASP AI Maturity Assessment" (AIMA) is a comprehensive framework developed by the Open Worldwide Application Security Project (OWASP) to help organizations evaluate and improve the security, ethics, privacy, and trustworthiness of their AI systems. Released as Version 1.0 on August 11, 2025, this 76-page document adapts the OWASP Software Assurance Maturity Model (SAMM) to address AI-specific challenges, such as bias, data vulnerabilities, opacity in decision-making, and non-deterministic behavior. It emphasizes balancing innovation with accountability, providing actionable guidance for CISOs, AI/ML engineers, product leads, auditors, and policymakers.
AIMA responds to the rapid adoption of AI amid regulatory scrutiny (e.g., EU AI Act, NIST guidelines) and public concerns. It extends traditional software security to encompass AI lifecycle elements like data provenance, model robustness, fairness, and transparency. The model is open-source, community-driven, and designed for incremental improvement, with maturity levels linked to tangible activities, artifacts, and metrics.
Key Structure and Domains
AIMA defines 8 assessment domains spanning the AI lifecycle, each with sub-practices organized into three maturity levels (1: Basic/Ad Hoc; 2: Structured/Defined; 3: Optimized/Continuous). Practices are split into two streams:
- Stream A: Focuses on creating and promoting policies, processes, and capabilities.
- Stream B: Emphasizes measuring, monitoring, and improving outcomes.
The domains are:
| Domain | Key Sub-Practices | Focus |
|---|---|---|
| Responsible AI | Ethical Values & Societal Impact; Transparency & Explainability; Fairness & Bias | Aligns AI with human values, ensures equitable outcomes, and provides understandable decisions. |
| Governance | Strategy & Metrics; Policy & Compliance; Education & Guidance | Defines AI vision, enforces standards, and builds awareness through training and policies. |
| Data Management | Data Quality & Integrity; Data Governance & Accountability; Data Training | Ensures data accuracy, traceability, and ethical handling to prevent issues like poisoning or drift. |
| Privacy | Data Minimization & Purpose Limitation; Privacy by Design & Default; User Control & Transparency | Protects personal data, embeds privacy early, and empowers users with controls and clear info. |
| Design | Threat Assessment; Security Architecture; Security Requirements | Identifies risks, builds resilient structures, and defines security needs from the start. |
| Implementation | Secure Build; Secure Deployment; Defect Management | Integrates security in development, deployment, and ongoing fixes for AI-specific defects. |
| Verification | Security Testing; Requirement-Based Testing; Architecture Assessment | Validates systems against threats, requirements, and standards through rigorous testing. |
| Operations | Incident Management; Event Management; Operational Management | Handles post-deployment incidents, monitors events, and maintains secure, efficient operations. |
Each domain includes objectives, activities, and results per maturity level, progressing from reactive/informal practices to proactive, automated, and data-driven ones.
Applying the Model
- Assessment Methods:
- Lightweight: Yes/No questionnaires in worksheets to quickly score maturity (0-3, with "+" for partial progress).
- Detailed: Adds evidence verification (e.g., documents, interviews) for higher confidence.
- Scoring: Practices score 0 (none), 1 (basic), 2 (defined), or 3 (optimized), with visualization via radar charts. Focus on organization-wide or project-specific scope.
- Worksheets: Provided for each domain with targeted questions (e.g., "Is there an initial AI strategy documented?" for Governance). Success metrics guide improvements.
Appendix and Resources
- Glossary: Defines key terms like adversarial attacks, bias, data drift, hallucinations, LLMs, model poisoning, prompt injection, responsible AI, and transparency.
- Integration with OWASP Ecosystem: Complements resources like OWASP Top 10 for LLMs, AI Security & Privacy Guide, AI Exchange, and Machine Learning Security Top 10.
Purpose and Value
AIMA bridges principles and practice, enabling organizations to spot gaps, manage risks, and foster responsible AI adoption. It's a living document, open to community feedback via GitHub for future refinements. By using AIMA, teams can translate high-level ethics into day-to-day decisions, ensuring AI innovation aligns with security, compliance, and societal impact.
