NIST's move toward use-case specific control overlays for AI systems highlights a common issue: organizations often struggle not due to a lack of cybersecurity expertise, but because they misapply it. The April 3, 2025 workshop at NIST's National Cybersecurity Center of Excellence revealed a recurring theme: teams repeatedly make the same integration mistakes, rooted in misunderstandings about how AI security differs from traditional software protection.
Why These Mistakes Keep Happening
Your team is familiar with NIST SP 800-53. You've implemented controls for databases, APIs, and enterprise software. The instinct when securing AI systems is to apply the same strategies. However, AI introduces unique risks that don't align neatly with traditional control frameworks. A model's training data can create vulnerabilities absent in conventional software. Adversarial inputs can alter outputs without affecting infrastructure. Your existing controls are effective, but only if you understand which ones need AI-specific adjustments.
The issue is compounded because organizations typically fall into one of two categories: AI developers who underestimate cybersecurity complexity, or security teams who treat AI as just another application layer. Neither view captures the full picture.
Mistake 1: Treating All AI Systems Identically
Why it happens: When NIST announces control overlays, teams often think they need one comprehensive overlay for "AI security," similar to approaches for cloud security or mobile device management.
Real consequence: This results in bloated control requirements that frustrate AI users and overlook critical risks in development environments. For example, a simple classification model for document routing doesn't require the same controls as a General-Purpose AI Model for customer interactions. The former needs basic input validation and monitoring, while the latter requires adversarial simulation, model provenance tracking, and continuous behavioral analysis. Applying identical controls wastes resources and creates a facade of compliance.
The fix: Categorize your AI inventory by use-case before selecting controls. NIST's approach in SP 800-218A illustrates this: controls for AI developers differ from those for AI users. Start with three categories: (1) models you develop internally, (2) foundation models you fine-tune or prompt, and (3) AI systems you consume as services. Each requires different overlay emphasis. Internal development needs secure software development lifecycle controls. Foundation model use requires vendor due diligence and rate limiting. Consumed services focus on AI interaction disclosure and usage instructions.
Mistake 2: Reinventing Controls That Already Work
Why it happens: AI feels new, so teams assume they need new controls. When securing a machine learning pipeline, the tendency is to create AI-specific policies from scratch instead of adapting existing SP 800-53 controls.
Real consequence: This leads to parallel control structures that auditors can't reconcile with your existing cybersecurity framework. Your team ends up maintaining two sets of access control policies, two change management processes, and two incident response procedures. This fragmentation becomes evident during audits when you can't show consistent control application across AI and non-AI systems.
The fix: Use SP 800-53 as your starting point. Most AI security needs align with existing controls, with modified implementation guidance. Access control (AC family) applies to model artifacts as it does to source code. Configuration management (CM family) covers model versioning and feature store integrity. The control objective remains the same; only the asset type and implementation details change. Document which controls need AI-specific implementation notes instead of creating separate AI controls. For instance, AC-2 (Account Management) doesn't change, but your implementation guide should specify that model training environments need separate access tiers based on data sensitivity and model risk.
Mistake 3: Skipping Threat-Informed Control Selection
Why it happens: Traditional risk assessments focus on infrastructure vulnerabilities and data breaches. Teams apply the same threat model to AI systems without considering adversarial machine learning attacks cataloged in NIST AI-100-2e2025.
Real consequence: Your controls protect against network intrusions but miss model inversion attacks that extract training data through crafted queries. You've secured your infrastructure but left your model's decision boundary vulnerable to adversarial inputs that manipulate classifications without triggering alerts.
The fix: Use NIST AI-100-2e2025's taxonomy to guide overlay selection. This isn't about implementing every mitigation; it's about understanding which attack vectors apply to your specific AI use case. If deploying a computer vision model for quality control, prioritize poisoning attacks during training and evasion attacks during inference. Select SP 800-53 controls that address these vectors: SI-7 (Software, Firmware, and Information Integrity) for training data integrity checks, and SI-4 (System Monitoring) with behavioral anomaly detection for inference patterns. Document the threat-to-control mapping to show deliberate, risk-based selection.
Mistake 4: Ignoring the Developer vs. User Control Split
Why it happens: Your organization uses AI in both modes, developing some models while consuming others as services, but your control framework doesn't distinguish between these roles. You apply development-focused controls to teams who only prompt foundation models, or skip supply chain controls because you're "not really developing AI."
Real consequence: Development teams get bogged down with procurement controls irrelevant to their build process. User teams lack guidance on vendor due diligence for foundation model providers, leading to undocumented dependencies on external AI services that introduce unmanaged vendor model risk.
The fix: Implement role-specific overlay modules. NIST's SP 800-218A provides the blueprint: if you develop models, emphasize secure model provisioning, reproducibility controls, and validation evidence requirements. If you consume AI services, focus on vendor assessment criteria, service-level agreements for model performance, and responsible disclosure procedures for model failures. Most organizations need both modules but applied to different teams. Your central AI governance function should maintain a matrix showing which teams operate under which overlay, with clear criteria for when a use case shifts from "user" to "developer" status.
Mistake 5: Building Overlays Without Community Input
Why it happens: Control development often feels like an internal security function. You assign it to your cybersecurity team or a compliance group, finalize the overlay, and roll it out to AI practitioners who had no input in its design.
Real consequence: Your overlay includes controls that sound rigorous but prove unworkable in practice. Data scientists ignore requirements they don't understand. Model risk managers can't validate compliance because the controls don't align with actual model workflows. Within six months, your overlay exists in policy documents but not in practice.
The fix: Establish a standing community of interest that includes AI developers, data scientists, model validators, security engineers, and compliance specialists before drafting control language. NIST's workshop model demonstrates this: they seek feedback at the concept stage, not after publication. Run your overlay draft through this group with specific questions: Which controls create implementation friction? Where do you need more guidance? What AI-specific risks aren't covered? Iterate based on feedback. Your overlay should emerge from this community, not be imposed on it. Schedule quarterly reviews where the community assesses whether controls remain relevant as your AI use cases evolve.
Prevention Checklist
Before finalizing your AI control overlay:
- Segment your AI inventory into developer, fine-tuner, and consumer categories
- Map each category to existing SP 800-53 controls before writing new ones
- Review NIST AI-100-2e2025 and document which attack types apply to your use cases
- Identify which controls need AI-specific implementation guidance vs. standard application
- Assign role-based overlay modules to teams based on how they interact with AI
- Establish a cross-functional community of interest with veto power over impractical controls
- Document the threat model that justifies each selected control
- Create implementation examples showing how controls apply to actual models in your environment
- Define criteria for when a use case moves between overlay categories
- Schedule a 90-day post-implementation review with practitioners to identify gaps
Your control overlay isn't a one-time deliverable. It's a living document that evolves as your AI use cases mature and as NIST releases additional guidance. The teams that succeed treat overlay development as an ongoing conversation between security requirements and operational reality, not a compliance checkbox.
