OWASP Top 10 for LLM Applications 2025 Mapping to Security Frameworks

Mapping the OWASP Top 10 for Large Language Models (LLMs) Applications 2025 to well-established security frameworks is a crucial step in developing secure AI applications.

As LLMs become increasingly integrated into sensitive and high-stakes environments, their complex structures introduce unique risks that can be challenging to address with traditional cybersecurity practices alone.

We must understand there is not one size fits all and each industry and particular organization requires a customized approach.

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Security Frameworks Mapping

1. Foundational Cybersecurity Standards

1. NIST Cybersecurity Framework CFS2.0
   • Provides comprehensive guidelines for managing cybersecurity risk.
   • Recognized globally as a foundational cybersecurity framework.

2. ISO/IEC Standards

2. ISO/IEC 27001 (Information Security Management)
   • Establishes security controls and ensures global business compliance.
3. ISO/IEC 20547-4:2020 (Big Data Security and Privacy)
   • Focuses on securing big data environments.
4. ISO/IEC 5338
   • A newer standard for AI and ML security.
   • Crucial for compliance and information security.
5. ISO/IEC 38507
   • Governs digital governance of AI.
   • Provides guidelines on the governance of artificial intelligence in organizations.
6. ISO/IEC 23894
   • Focuses on privacy in AI systems.
   • Provides requirements for privacy in AI, addressing risks and compliance with data protection laws.
7. ISO/IEC 24028
   • Covers bias in AI systems.
   • Guidelines to identify, manage, and mitigate biases in artificial intelligence systems.
8. ISO/IEC 23053
   • AI framework for transparency and explainability.
   • Ensures AI systems' outputs are understandable and transparent.
9. ISO/IEC 22989
   • Standard on AI trustworthiness.
   • Focuses on building trust in AI through reliability, accuracy, and ethical principles.

3. Risk and Threat Modeling

10. MITRE ATT&CK
    • A knowledge base for understanding and defending against cyberattacks.
    • Valuable for threat modelling and analyzing adversarial tactics and techniques.
11. STRIDE
    • A threat modelling methodology to identify potential security threats.
    • Commonly used in early software development stages for proactive security.
12. FAIR (Factor Analysis of Information Risk)
    • Focuses on quantifying and managing cybersecurity risk.
    • Helps organizations evaluate cyber risks in financial terms.

4. Application and Data Security

13. CIS Controls
    • Developed by the Center for Internet Security, offering practical, actionable security controls.
    • Highly regarded for its effectiveness in strengthening defences.
14. ASVS (Application Security Verification Standard)
    • Framework for testing and assessing web application security controls.
    • Widely used for securing web applications.
15. SOC 2 (Service Organization Control 2)
    • Primarily for SaaS and service providers, relevant for secure handling of customer data.
    • Complements application security, especially in LLM contexts.
16. PCI DSS
    • Ensures data security for applications dealing with payment information.
    • Crucial for compliance in payment and financial services.

5. OT-Specific Frameworks

17. ISA/IEC 62443
    • Designed specifically for OT environments, focusing on ICS security.
    • Widely adopted in OT for its standardized approach to securing industrial systems.

6. Software Development and Maturity Models

18. SAMM (Software Assurance Maturity Model)
    • Supports integrating security into software development.
    • Helps organizations benchmark and improve their software security practices.
19. BSIMM (Building Security In Maturity Model)
    • Measures and enhances software security initiatives.
    • Ideal for tracking and improving organizational software security practices.
20. OPENCRE
    • Facilitates alignment of cybersecurity controls across various standards.
    • Acts as a cross-framework bridge, enhancing interoperability.

7. Specialized and Emerging Standards

21. MITRE ATLAS
    • Focused on adversarial behaviour modelling for threat analysis.
    • Specific and detailed, but not all-encompassing for cybersecurity management.
22. ENISA
    • The European Union Agency for Network and Information Security, providing guidance on cybersecurity best practices.
    • Especially relevant for European compliance.
23. CycloneDX Machine Learning SBOM
    • Provides a standard for advanced supply chain security capabilities.
    • Enables detailed tracking of software, hardware, and services in the software supply chain.
24. COBIT (Control Objectives for Information and Related Technologies)
    • A governance framework aligning IT operations with enterprise objectives.
    • Useful for integrating governance into LLM applications.
25. TOGAF (The Open Group Architecture Framework)
    • Enterprise architecture framework with security applications.
    • Valuable for complex systems and secure architecture mapping.
26. NIST AI RMF 1.0/AI 100-1 (AI Risk Management Framework)
    • Provides guidelines for managing risks related to AI systems.
    • Emphasizes reliability, robustness, and trustworthiness of AI implementations.

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In an era where AI technologies like LLMs play critical roles in decision-making, customer interactions, and data processing, securing these systems is essential for maintaining trust and resilience. Mapping the OWASP Top 10 for LLM applications 2025 to multiple frameworks not only provides comprehensive protection but also supports regulatory compliance, risk management, and ethical AI deployment. This integrated approach empowers organizations to adopt LLMs confidently, knowing they are safeguarded by industry-recognized security standards tailored to address the distinct challenges AI applications face.

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