The National Institute of Standards and Technology (NIST) has expanded its AI security guidance with a new cybersecurity framework profile specifically designed for AI systems. As reported by Utility Dive, this profile provides organizations with a structured approach to addressing AI-specific risks while leveraging the familiar NIST Cybersecurity Framework (CSF) structure.
For enterprises already aligned with NIST CSF, this new profile offers a clear path to extending existing security programs to cover AI workloads. For those just starting their AI governance journey, it provides a proven framework to build upon.
Why This Matters
NIST frameworks often become de facto standards for enterprise security. This AI-specific profile will likely influence regulations, insurance requirements, and audit expectations for years to come. NIST has also launched new centers for AI in manufacturing and critical infrastructure as part of broader AI leadership initiatives.
The Five Functions Applied to AI
The NIST CSF organizes security activities into five core functions. The new AI profile maps AI-specific considerations onto each:
IDENTIFY
Understand AI assets, data flows, model dependencies, and AI-specific risks across your organization.
PROTECT
Implement safeguards for AI systems including access controls, data protection, and AI guardrails.
DETECT
Monitor AI behavior for anomalies, adversarial inputs, model drift, and potential compromises.
RESPOND
Develop response plans for AI incidents including model failures, bias discovery, and security breaches.
RECOVER
Plan for AI system recovery including model rollback, retraining procedures, and communication strategies.
Key AI-Specific Considerations
The new profile highlights several AI-specific risk areas that traditional cybersecurity frameworks don't adequately address:
Model Security
AI models themselves are valuable assets that can be stolen, poisoned, or manipulated. The profile emphasizes protecting model integrity throughout the lifecycle—from training data to deployed inference. The MITRE ATLAS framework provides additional guidance on AI-specific attack patterns.
Data Pipeline Security
AI systems depend on data pipelines that introduce unique vulnerabilities. The profile addresses training data integrity, feature engineering risks, and continuous learning exposure.
Inference Security
Once deployed, AI models face attacks through their inputs—prompt injection, adversarial examples, and extraction attacks. Runtime guardrails are essential protection. The OWASP Top 10 for LLM Applications catalogs these risks.
Supply Chain Considerations
Many organizations use third-party models, APIs, and training data. The profile addresses AI supply chain risks including model provenance and dependency management.
Implementation Guidance
The NIST AI CSF profile provides actionable guidance for implementation across maturity levels:
Tier 1: Partial
- Basic inventory of AI systems
- Ad hoc risk assessments
- Reactive incident response
Tier 2: Risk Informed
- Documented AI asset management
- Regular risk assessments integrated with enterprise risk management
- Defined AI incident response procedures
Tier 3: Repeatable
- Comprehensive AI governance framework
- Automated monitoring and detection
- Standardized AI development lifecycle controls
Tier 4: Adaptive
- Continuous improvement based on lessons learned
- Predictive risk management for AI systems
- Industry leadership in AI security practices
Practical Application
Start by assessing your current NIST CSF maturity level, then use the AI profile to identify gaps specific to your AI deployments. Focus on the highest-risk areas first—typically runtime protection and data pipeline security.
How AI Guardrails Map to NIST CSF
AI guardrails platforms directly support multiple NIST CSF functions:
Protect Function
Guardrails provide runtime protection through input validation, output filtering, and policy enforcement. They implement the "protective technologies" subcategory with AI-specific controls.
Detect Function
Continuous monitoring capabilities detect anomalous AI behavior, potential adversarial inputs, and policy violations. This supports "anomalies and events" detection requirements.
Respond Function
Automated guardrail responses—blocking harmful outputs, escalating to human review, or gracefully degrading—implement "response planning" and "mitigation" subcategories.
Aligning with Other Frameworks
The NIST AI CSF profile complements other AI governance frameworks:
- NIST AI RMF: The profile provides security-specific implementation guidance for AI RMF's "Govern" and "Manage" functions
- EU AI Act: Organizations can use the profile to demonstrate technical compliance with EU requirements
- ISO 42001: The profile's structure aligns with ISO AI management system requirements
- SOC 2: AI-specific controls can be integrated into SOC 2 Type II assessments
Getting Started
Organizations should take these steps to leverage the new NIST AI CSF profile:
- Assess Current State: Evaluate your existing NIST CSF implementation and AI security posture
- Identify Gaps: Use the AI profile to identify AI-specific gaps in each function
- Prioritize Risks: Focus on the highest-risk AI systems and most critical gaps
- Implement Controls: Deploy AI-specific controls including guardrails, monitoring, and governance processes
- Document and Audit: Maintain documentation for compliance and continuous improvement
Conclusion
The NIST AI Cybersecurity Framework profile provides much-needed guidance for securing AI systems within established security frameworks. For organizations already invested in NIST CSF, this profile offers a natural extension. For those starting fresh, it provides a proven foundation for AI governance.
The key insight is that AI security isn't separate from cybersecurity—it's an extension of it. Organizations that integrate AI considerations into their existing security programs will be better positioned than those treating AI as a separate domain.