Generative Artificial Intelligence offers organizations across industries significant productivity and efficiency gains, but it also introduces new risks. The NIST AI RMF (AI Risk Management Framework) provides a structured approach to identify, assess, and mitigate these risks while maximizing the benefits of generative AI.
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Managing Generative AI Risks with NIST AI RMF
Generative artificial intelligence (Gen AI or GAI) is transforming machine learning (ML) by enabling teams to create human-like content and automate complex processes. While this innovation drives productivity, it also introduces risks related to security, privacy, and ethical concerns such as intellectual property (IP) rights.
To manage generative AI risks effectively, organizations should consider:
- The NIST AI RMF (AI Risk Management Framework): Provides a structured approach to identify, assess, and mitigate AI risks.
- NIST AI 600-1: Generative AI Profile: A specialized framework for safely implementing generative AI initiatives.
- Global AI regulations: Ensures compliance with international standards and ethical AI practices.
Working with a NIST AI RMF advisor helps organizations unlock the full potential of generative AI while keeping adoption secure, compliant, and ethically responsible.
Understanding and Implementing NIST AI RMF
The National Institute of Standards and Technology (NIST) published the Artificial Intelligence Risk Management Framework (NIST AI RMF or NIST AI 100-1) in January 2023. The framework provides guidance and best practices for managing AI-related risks, including those associated with generative AI. While it is not legally required for organizations in the U.S., adopting the NIST AI RMF aligns organizations with emerging domestic and international regulations and positions them for future compliance.
This section provides an overview of the NIST AI RMF requirements and how organizations can assess their implementation for effective AI risk management and generative AI governance.
Structure of NIST AI RMF Requirements
The NIST AI RMF is organized into four core Functions: Govern, Map, Measure, and Manage. Each function includes Categories and Subcategories, outlining desired outcomes for AI risk management. The framework is similar in structure to the NIST Cybersecurity Framework, emphasizing flexibility rather than prescriptive requirements.
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Govern: Top-down control of AI risks
- Govern 1: Protocols supporting Map, Measure, and Manage functions
- Govern 2: Accountability standards for AI leaders
- Govern 3: Diversity, equity, and inclusion standards
- Govern 4: Organizational communication standards for AI risks
- Govern 5: Infrastructure for external AI risk feedback
- Govern 6: Third-party AI risk identification and management
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Map – Scoping and benchmarking AI systems
- Map 1: Understanding AI systems’ risk factors
- Map 2: Categorization of AI systems and components
- Map 3: Benchmarks for AI system goals and capabilities
- Map 4: Risk/benefit analysis for first- and third-party AI systems
- Map 5: Characterization of AI risks and stakeholder impacts
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Measure – Transparent AI risk measurement
- Measure 1: Metrics and methods for the AI risk environment
- Measure 2: Metrics to establish trustworthiness
- Measure 3: Tracking identifiable AI risks
- Measure 4: Protocols for gathering and addressing AI risk feedback
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Manage – AI risk treatment and governance
- Manage 1: Prioritization and protocols for addressing risks
- Manage 2: Minimizing drawbacks while maximizing benefits
- Manage 3: Third-party AI risk management
- Manage 4: Risk mitigation, recovery, and communications
Each function, category, and subcategory represents ideal outcomes rather than strict controls, giving organizations flexibility in achieving compliance. NIST also provides a NIST AI RMF Playbook, suggested actions, and references aligned with these outcomes.
NIST AI RMF Assessments and Assurance
The NIST AI RMF is not legally mandated in the U.S., unlike some other NIST frameworks. Its novelty and the emerging nature of AI also mean that the framework is currently not certifiable.
However, organizations can work with third-party assessors to audit and validate their NIST AI RMF implementation. Experienced assessment partners use clear, transparent criteria to evaluate whether NIST outcomes are being achieved. In many cases, these benchmarks can also be aligned with other compliance standards for AI or broader cybersecurity programs.
Demonstrating your NIST AI RMF adoption provides tangible evidence to clients and partners of your commitment to secure AI governance, mitigation of generative AI risks, and responsible AI deployment practices.
How NIST AI 600-1 Supplements the NIST AI RMF
In addition to addressing generative AI risks within the NIST AI RMF, NIST provides a supplementary framework called the Generative Artificial Intelligence Profile (NIST AI 600-1). This profile offers guidance specifically tailored to the unique challenges of generative AI risks.
Gen AI risks differ from traditional AI and technological risks in several key ways. They can arise at different stages of the AI lifecycle, impact various system and model levels, stem from diverse sources such as design, model training, or user error, and may manifest either suddenly or gradually. These characteristics make generative AI risks particularly difficult to identify, monitor, and mitigate.
The NIST AI 600-1 framework provides critical guidance on managing these unique risks and highlights the primary considerations organizations should follow. Additionally, it includes a set of Suggested Actions aligned with the NIST AI RMF Categories and Subcategories. Like the AI RMF Playbook, these Suggested Actions are not mandatory but are designed to support effective implementation and practical risk management.
NIST AI 600-1 and Generative Artificial Intelligence Risks
While the NIST AI RMF addresses AI risk broadly, NIST AI 600-1 focuses specifically on generative AI risks, those unique to or amplified by generative AI technologies. Implementing its Primary Considerations helps organizations manage these complex risks across the AI lifecycle.
Key generative AI risks prioritized by NIST AI 600-1 include:
- CBRN Information or Capabilities: Access to chemical, biological, radiological, or nuclear (CBRN) data could threaten national security.
- Confabulation: AI may confidently generate false or inaccurate content (“hallucination”), leading to misinformation.
- Dangerous, Violent, or Hateful Content: AI outputs can incite violence or self-harm if not properly controlled.
- Data Privacy: Leakages of AI inputs/outputs may compromise personal or sensitive data.
- Environmental Impacts: High compute demands of Gen AI can negatively affect global ecosystems.
- Harmful Bias or Homogenization: AI may amplify biases based on race, sex, religion, or other identity factors.
- Human-AI Configuration: Points of human interaction may introduce algorithmic bias or other harms.
- Information Integrity: AI outputs can blur facts and fiction, making verification difficult.
- Information Security: Low barriers to AI misuse can enable larger or more frequent cyberattacks.
- Intellectual Property: Unauthorized reproduction of copyrighted or trademarked content may occur.
- Obscene, Degrading, or Abusive Content: AI may generate offensive, abusive, or nonconsensual content.
- Value Chain and Component Integration: Third-party components in AI systems may be obscured, complicating tracking and forensic analysis.
By addressing these risks with NIST AI 600-1 guidance, organizations can enhance AI governance, compliance, and mitigation of generative AI risks while aligning with the broader NIST AI RMF framework.
NIST AI 600-1 Generative Artificial Intelligence Compliance Considerations
The NIST AI 600-1 framework provides detailed guidance for tailoring NIST AI RMF functions, Categories, and Subcategories to generative AI risks. Given the scope of the guidance, it also identifies a set of Primary Considerations to prioritize for effective generative AI risk management.
Key Primary Considerations include:
- Gen AI Governance: Establish policies and procedures that account for the evolving and less-understood nature of generative AI risks. Maintain acceptable use policies and update them regularly to ensure compliance and safety.
- Pre-Deployment Testing: Conduct robust test, evaluation, validation, and verification (TEVV) processes to fully understand generative AI capabilities before deployment.
- Content Provenance: Implement metadata tracking and content provenance controls across all systems interacting with generative AI to enhance transparency and accountability.
- Incident Disclosure: Develop a strong reporting and communication infrastructure to enforce accountability and respond effectively to AI-related incidents.
Working with a managed security services provider experienced in AI risk management can help organizations efficiently implement these considerations, mitigate generative AI risks, and maintain compliance with evolving standards.
Streamline Your Generative Artificial Intelligence Compliance Today
Organizations leveraging generative AI must also take responsibility for the associated risks. Implementing best practices from NIST, including the NIST AI RMF and NIST AI 600-1, ensures your AI initiatives are secure, compliant, and aligned with industry standards.
Partnering with an experienced cybersecurity advisory team can help tailor these frameworks to your organization’s unique AI ecosystem. At RSI Security, we guide teams in integrating NIST and other AI governance frameworks, providing practical, flexible solutions to manage generative AI risks efficiently.
To strengthen your AI risk management and compliance efforts, contact RSI Security today and take the first step toward secure and responsible generative AI adoption.
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