AI Security Maturity Model
- Arjun Ramakrishnan
- AI Security
- Published: 31 Dec, 2025
- Updated: 31 Dec, 2025
In part 1 and part 2 of our AI Security Maturity series, we explored a framework for organizations to assess, benchmark, and improve their AI security posture. To wrap it up, this post introduces the AI Security Maturity Model (AISMM) — an open-source project that brings this framework to life with a practical assessment tool.
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From Framework to Implementation
Developing a maturity framework is one thing; operationalizing it is another challenge entirely. Organizations often struggle to translate abstract maturity levels into concrete, actionable assessments. The AISMM project bridges this gap by providing a comprehensive, standards-aligned maturity model with an interactive web application for conducting assessments.
The AISMM v1.1 builds upon the concepts we discussed in the earlier articles and incorporates guidance from established standards and frameworks:
- NIST AI Risk Management Framework (AI RMF 2.0) and Generative AI Profile (NIST AI 600-1)
- EU AI Act compliance considerations
- ISO/IEC 42001 (AI Management Systems)
- OWASP GenAI Security initiatives
The Three Pillars of AISMM
Unlike traditional security frameworks that treat AI as a monolithic concern, AISMM recognizes that AI security operates across three distinct but interconnected pillars:
Security for AI
Protecting AI systems throughout their lifecycle — from data collection and model training to deployment and inference. This includes securing training data, model artifacts, and inference pipelines.
AI for Security
Deploying AI as a security capability — leveraging machine learning for threat detection, anomaly identification, and automated response. This pillar focuses on using AI to enhance security operations.
Security from AI
Managing AI-specific risks and harms — addressing novel vulnerabilities like adversarial attacks, prompt injection, model poisoning, and the broader implications of AI system failures.
This three-pillar approach ensures organizations develop a holistic understanding of their AI security posture rather than focusing on isolated aspects.
Key Features of the AISMM Tool
The AISMM project provides more than just a framework definition — it includes a fully functional web application for conducting assessments:
Assessment Management
Create and manage security assessments for multiple organizations. Track progress across pillars and domains over time, enabling organizations to measure their advancement through maturity levels. The model itself can also be customized using the accompanying YAML file. For example, a financial organization would have different goals and weightages for the model’s components for its assessment as compared to a health or retail organization.
Maturity Dashboard
Visualize your organization’s security posture with comprehensive dashboards. The tool automatically calculates maturity levels and scores based on assessment responses, providing clear visibility into strengths and areas for improvement. The goal should be to have measurable and trackable progress of the organizations maturity as initiatives targetting AI Security capability are completed.
AI-Powered Analysis
One of the innovative features is the integration of AI agents for assessment analysis. Using LangChain and local LLM inference through Ollama, the tool can:
- Generate executive summaries across all three pillars
- Identify key milestones and achievements
- Highlight areas for improvement with gap analysis
- Provide prioritized recommendations (critical/high/medium/low)
- Offer cross-pillar insights and strategic guidance
Standards Alignment
Every assessment question and domain is mapped to relevant industry standards, helping organizations demonstrate compliance and identify gaps against regulatory requirements. This can also be customized using the YAML file to help incllude and prioritize frameworks to specific industries.
Getting Started with AISMM
The AISMM project is available on GitHub at github.com/laughingman42/AISMM. The repository includes:
- Canonical YAML Definition: The complete AISMM model definition in a structured, version-controlled format
- Web Application: A React + TypeScript frontend with an Express.js backend
- AI Agent Framework: LangChain-powered agents for automated analysis
- Database: SQLite for lightweight, embedded data storage
Prerequisites
To run the AISMM tool locally, you’ll need:
- Node.js 18+ and npm
- Ollama (optional, for AI agent analysis)
Quick Installation
# Clone the repository
git clone https://github.com/laughingman42/AISMM.git
cd AISMM/webapp
# Install dependencies
npm install
# Copy environment configuration
cp .env.example .env
# Start the application
npm run dev:allThe application will be available at http://localhost:5173 with the API running on http://localhost:3001.
Connecting to the Maturity Framework
The AISMM tool operationalizes the maturity levels we discussed in the earlier articles. Each assessment domain within the three pillars maps to the five-level progression:
| Level | Name | AISMM Assessment Focus |
|---|---|---|
| 1 | Initial | Basic awareness and ad-hoc practices |
| 2 | Developing | Documented policies and defined processes |
| 3 | Established | Enterprise-wide governance and risk-based approach |
| 4 | Managed | Quantitative metrics and continuous improvement |
| 5 | Optimized | Adaptive measures and industry-leading practices |
By completing assessments across all pillars and domains, organizations receive a comprehensive view of their maturity level and a clear roadmap for advancement.
Contributing to AISMM
The AISMM project is open source under the MIT License. Contributions are welcome, whether it’s:
- Enhancing the YAML model definition with additional domains or questions
- Improving the web application user experience
- Adding new AI agent capabilities
- Translating the framework to additional languages
- Sharing feedback from real-world implementations
See the CONTRIBUTING.md file for detailed contribution guidelines.
Conclusion
The AI Security Maturity Model represents the evolution from theoretical framework to practical tool. By providing an open-source assessment platform, organizations of all sizes can now systematically evaluate their AI security posture and chart a course toward maturity.
As AI adoption continues to accelerate, the importance of robust AI security practices cannot be overstated. The AISMM provides a structured, standards-aligned approach that helps organizations move beyond ad-hoc security measures toward a comprehensive, measured, and continuously improving AI security program.
Explore the AISMM repository, try the assessment tool, and contribute to its ongoing development.
