Artificial Intelligence in banking isn’t new, but its speed of deployment and regulatory scrutiny are unprecedented. Banks face a “bandwagon effect,” rushing AI initiatives while balancing risk management, governance, and consumer expectations. Key challenges like explainability and hallucinations require embedding AI into existing model risk frameworks, with strong controls, transparency, and incident readiness to safeguard compliance and trust.

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 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.

Generative AI (GenAI) is moving from hype to practical adoption, transforming industries with tools like ChatGPT and Claude. But along with innovation come new risks, from data security and misinformation to compliance and third-party vulnerabilities. This article breaks down what GenAI is, outlines the unique challenges it creates, and explores frameworks like NIST’s AI RMF, ISO/IEC 42001, and OWASP’s LLM Top 10 that can help organizations innovate responsibly.

AI hasn’t created new cyber risks - it’s accelerating existing ones. Learn why strong fundamentals and a well-practiced incident response plan matter more than chasing “AI-proof” products, and how CipherNorth helps build real resilience.