AI Security Frameworks – Ensuring Trust in Machine Learning
As artificial intelligence transforms industries and enhances human capabilities, the need for strong AI security frameworks has become paramount.
Recent developments in AI security standards aim to mitigate risks associated with machine learning systems while fostering innovation and building public trust.
Organizations worldwide are now navigating a complex landscape of frameworks designed to ensure AI systems are secure, ethical, and trustworthy.
The Growing Ecosystem of AI Security Standards
The National Institute of Standards and Technology (NIST) has established itself as a leader in this space with its AI Risk Management Framework (AI RMF), released in January 2023.
The framework provides organizations with a systematic approach to identifying, assessing, and mitigating risks throughout an AI system’s lifecycle.
“At its core, the NIST AI RMF is built on four functions: Govern, Map, Measure, and Manage. These functions are not discrete steps but interconnected processes designed to be implemented iteratively throughout an AI system’s lifecycle,” explains Palo Alto Networks in its framework analysis.
Simultaneously, the International Organization for Standardization (ISO) has developed ISO/IEC 42001:2023, establishing a comprehensive framework for managing artificial intelligence systems within organizations.
The standard emphasizes “the importance of ethical, secure, and transparent AI development and deployment” and provides detailed guidance on AI management, risk assessment, and addressing data protection concerns.
Regulatory Landscape and Compliance Requirements
The European Union has taken a significant step with its Artificial Intelligence Act, which came into force on August 2, 2024, though most obligations will not apply until August 2026.
The Act establishes cybersecurity requirements for high-risk AI systems, with substantial financial penalties for non-compliance.
“The obligation to comply with these requirements falls on companies that develop AI systems and those that market or implement them,” notes an analysis of the Act.
For organizations looking to demonstrate compliance with these emerging regulations, Microsoft Purview now offers AI compliance assessment templates covering the EU AI Act, NIST AI RMF, and ISO/IEC 42001, helping organizations “assess and strengthen compliance with AI regulations and standards.”
Industry-Led Initiatives for Securing AI Systems
Beyond government and regulatory bodies, industry organizations are developing specialized frameworks.
The Cloud Security Alliance (CSA) will release its AI Controls Matrix (AICM) in June 2025. This matrix is designed to help organizations “securely develop, implement, and use AI technologies.”
The first revision will contain 242 controls across 18 security domains, covering everything from model security to governance and compliance.
The Open Web Application Security Project (OWASP) has created the Top 10 for LLM Applications, addressing critical vulnerabilities in large language models.
This list, developed by nearly 500 experts from AI companies, security firms, cloud providers, and academia, identifies key security risks including prompt injection, insecure output handling, training data poisoning, and model denial of service.
Implementing these frameworks requires organizations to establish robust governance structures and security controls.
IBM recommends a comprehensive approach to AI governance, including “oversight mechanisms that address risks such as bias, privacy infringement and misuse while fostering innovation and building trust.”
For practical security implementation, the Adversarial Robustness Toolbox (ART) provides tools that “enable developers and researchers to evaluate, defend, and verify Machine Learning models and applications against adversarial threats.”
The toolkit supports all popular machine learning frameworks and offers 39 attack and 29 defense modules.
Looking Forward: Evolving Standards for Evolving Technology
As AI technologies continue to advance, security frameworks must evolve accordingly.
The CSA acknowledges this challenge, noting that “keeping pace with the frequent changes in the AI industry is no easy feat” and that its AI Controls Matrix “will definitely have to undergo periodic revisions to stay up-to-date.”
The Cybersecurity and Infrastructure Security Agency (CISA) recently released guidelines aligned with the NIST AI RMF to combat AI-driven cyber threats.
These guidelines follow a “secure by design” philosophy and emphasize the need for organizations to “create a detailed plan for cybersecurity risk management, establish transparency in AI system use, and integrate AI threats, incidents, and failures into information-sharing mechanisms.”
As organizations navigate this complex landscape, one thing is clear: adequate AI security requires a multidisciplinary approach involving stakeholders from technology, law, ethics, and business.
As AI systems become more sophisticated and integrated into critical aspects of society, these frameworks will play a crucial role in shaping the future of machine learning, ensuring it remains both innovative and trustworthy.
