Generative AI governance is complex, with multiple frameworks available to address security, risk, ethics, and compliance. Compare OWASP LLM Top 10, NIST AI RMF & 600-1, ISO/IEC 42001:2023, and CipherNorth’s Foundational Framework to find the right approach for your organization’s maturity and goals.

Not every organization is ready to implement a full AI governance program, but waiting to set guardrails can expose you to real risks like data leakage, misuse, and compliance gaps. At CipherNorth, we recommend a foundational framework, a streamlined set of policies, safeguards, and processes drawn from NIST, ISO, and other trusted sources, that gives organizations a secure starting point for using generative AI responsibly.

ISO/IEC 42001:2023 is an international standard for Artificial Intelligence Management Systems (AIMS), guiding organizations of all sizes to implement responsible AI governance, risk management, transparency, and continuous improvement. Certification demonstrates credible AI oversight, ethical practices, and regulatory alignment.

The NIST AI Risk Management Framework (AI RMF 1.0) offers organizations a structured approach to managing AI risk through four functions: Govern, Map, Measure, and Manage. NIST AI 600-1, released in 2024, extends this framework to the unique challenges of generative AI, addressing issues like hallucinations, copyright, bias, and misuse. Together, they provide a practical foundation for integrating AI governance into existing risk and security programs.

The Department of War’s new Cybersecurity Risk Management Construct (CSRMC) isn’t a revolution, it’s a reframing of existing ideas like continuous monitoring, automation, DevSecOps, and resilience. While the strategic direction is sound, CSRMC lacks the practical guidance such as control sets, telemetry standards, KPIs, and enforcement that operators and contractors need to act. Aligning CSRMC with well-established frameworks like NIST CSF, NIST SP 800-53, CMMC, and CIS Controls would turn vision into practice.

The OWASP Top 10 for Large Language Model (LLM) Applications highlights the most critical security risks in generative AI systems, from prompt injection to data leakage and misinformation. Updated in 2025, it provides organizations with a practical framework to identify vulnerabilities, strengthen application security, and build trust in LLM-powered tools.

At first glance, NIST frameworks, like SP 800-61, might seem designed exclusively for large enterprises with big security teams and budgets. But they're not, and this is how they can add value to small businesses.