The government’s cybersecurity experts are about to hand you a roadmap for AI security — but only if you know how to read it
Picture this: You’re a security professional at a 500-person company, watching your CEO demo the latest AI-powered sales tool while your risk radar screams warnings about data exposure, regulatory compliance, and attack vectors you haven’t even cataloged yet. Sound familiar? You’re not alone.
NIST is developing the Cyber AI Profile based on its landmark Cybersecurity Framework, with a planned release sometime within the next six months, according to Kat Megas, the agency’s cybersecurity, privacy and AI program manager. For mid-market companies caught between enterprise-level AI ambitions and startup-level security budgets, this upcoming framework represents both opportunity and challenge.
The AI Security Reality Check
Before diving into what NIST has planned, let’s acknowledge the current state of AI security in mid-market organizations. A recent BigID study reveals that nearly two-thirds (64%) of organizations lack full visibility into their AI risks, with almost 40% admitting they lack tools to protect AI-accessible data. Even more concerning? Only 6% of organizations have an advanced AI security strategy or a defined AI TRiSM (Trust, Risk, and Security Management) framework.
This isn’t a failure of understanding — it’s a failure of resources and guidance. Mid-market companies face a confounding challenge: they’re now dealing with enterprise-level threats and requirements, but still working with the same resources to address them. The new NIST framework aims to bridge exactly this gap.
What’s Actually Coming: NIST’s Cyber AI Profile
The Cyber AI Profile, currently in development, could help firms better prepare for hackers that use AI tools to enhance their cyberattacks. But unlike the typical NIST publication that reads like a government manual, this framework is being designed with practical implementation in mind.
Here’s what we know so far:
Framework Foundation
A potential Cyber AI Profile would tie AI-specific risks and considerations to existing cybersecurity goals, helping organizations strengthen defenses, counter AI-driven threats and map efforts to relevant laws and standards. This isn’t reinventing the wheel — it’s adding AI-specific guidance to proven security practices.
Control Overlays Approach
NIST intends to fully leverage existing cybersecurity Frameworks and technical guidelines (specifically the Security and Privacy Controls) to develop a series of use case-focused, threat-informed cybersecurity control overlays. Think of these as targeted security configurations rather than entirely new requirements.
Practical Implementation Focus
NIST intends to concurrently provide practical implementation guidelines to help organizations achieve the outcomes in the Cyber AI profile. This represents a significant shift toward actionable guidance rather than purely theoretical frameworks.
Why Mid-Market Companies Should Care Now
1. You’re Already a Target
Cybersecurity executives are struggling to navigate cyber threats fueled by AI, according to a top National Institute of Standards and Technology official. According to IBM’s 2024 Cost of a Data Breach Report, mid-sized companies now face average breach costs of $3.5 million. AI-enhanced attacks are making these numbers worse, not better.
2. Regulatory Pressure Is Building
Cybersecurity regulations are no longer reserved for enterprises. Mid-market companies handling sensitive customer data, financial transactions, or supply chain operations are under growing scrutiny. Having a NIST-aligned AI security strategy won’t just protect you — it’ll demonstrate due diligence to auditors and customers.
3. Competitive Advantage Through Security
Using a well-established framework also helps when it comes time to make the business case for investing in your security needs. Emphasize the benefits of a scalable strategy, best-in-class research, and wide compatibility with cybersecurity compliance standards you might already be on the hook for.
The Three-Phase Implementation Strategy
Based on NIST’s development timeline and mid-market realities, here’s how to prepare:
Phase 1: Foundation Assessment (Next 3 months)
Audit Your Current AI Landscape
- Document every AI tool, service, and integration currently in use
- Identify “Shadow AI” — unauthorized tools employees are using
- Map data flows to and from AI systems
- Assess current security controls covering AI systems
Practical Action Items:
- Create an AI inventory spreadsheet with business owner, data access level, and security controls
- Review vendor contracts for AI services to understand data handling
- Test your incident response plan with an AI-related scenario
Phase 2: Framework Alignment (Months 4–9)
Map to Existing NIST Controls In many aspects, the cybersecurity and privacy controls needed to manage risk to AI systems and components is no different than those required for any type of software; there is no need to rehash or reiterate these controls.
Start with what you have:
- Access Control (AC): Apply principle of least privilege to AI system access
- System and Information Integrity (SI): Monitor AI system outputs for anomalies
- Risk Assessment (RA): Include AI-specific threats in your risk register
Budget-Friendly Implementation:
- Leverage existing logging infrastructure for AI system monitoring
- Use configuration management tools to standardize AI deployments
- Implement automated scanning for AI model vulnerabilities
Phase 3: Advanced AI Security (Months 10+)
Implement AI-Specific Controls The focus of these overlays should be based solely on the controls that require unique implementation considerations and address AI-specific risks.
Focus areas for advanced implementation:
- Adversarial ML Protection: Input validation and output verification
- Model Governance: Version control, change management, and rollback procedures
- Data Poisoning Prevention: Training data integrity and supply chain security
Practical Tools and Techniques
Log Analysis for AI Security
Your existing log analysis infrastructure can be your first line of defense:
- API Call Monitoring: Track unusual patterns in AI service requests
- Data Access Logging: Monitor what data AI systems are accessing
- Output Analysis: Flag AI-generated content that deviates from normal patterns
- Performance Metrics: Detect model drift or degradation through response time analysis
Cost-Effective AI Security Measures
Immediate (0–30 days, <$5K):
- Implement API rate limiting for AI services
- Add AI usage policies to your security awareness training
- Configure alerts for unusual AI service usage patterns
Short-term (3–6 months, $5–25K):
- Deploy AI-specific monitoring dashboards
- Implement automated AI model vulnerability scanning
- Create AI incident response procedures
Long-term (6+ months, $25K+):
- Advanced threat detection for AI-specific attacks
- Automated AI governance and compliance reporting
- AI red team exercises and penetration testing
The Resource Reality for Mid-Market
Most IT leaders in this space are being asked to do more with less while attackers are getting faster, smarter, and more opportunistic. Here’s how to make NIST AI compliance achievable:
Leverage External Expertise
Currently, 53% of companies rely on partners for their cybersecurity needs, a figure projected to rise to 79% within two years. Consider:
- Managed Security Service Providers (MSSPs) with AI expertise
- Virtual CISO services for strategic guidance
- AI security consulting for framework implementation
Start Small, Scale Smart
Not every organization will be developing AI; many will just be users. In these cases, scoping the needs for the intended user will ensure lightweight and modular solutions that organizations can pick from to use alone or in different combinations to meet their unique needs.
Focus on your highest-risk AI implementations first:
- Customer-facing AI systems
- AI with access to sensitive data
- AI systems affecting business-critical processes
Common Implementation Pitfalls (And How to Avoid Them)
Pitfall 1: Framework Paralysis
Don’t wait for the perfect framework. Take from it what you need, review and prioritize incremental improvements, and give your business the freedom to build on it every year.
Pitfall 2: Over-Engineering
Organizations often create entirely new security frameworks for AI systems, unnecessarily duplicating controls. In most cases, existing security controls apply to AI systems — with only incremental adjustments needed for data protection and AI-specific concerns.
Pitfall 3: Ignoring Business Context
Security controls should enable business objectives, not hinder them. Work with AI system owners to understand business requirements before implementing restrictions.
Preparing for the Framework Release
Monitor Development Progress
NIST already released a concept paper and held an initial public workshop, where practitioners were surveyed about the AI-cyber landscape. The scientific standards agency is now developing a public draft based on workshop input and additional research.
Join the Conversation
Once complete, that draft will be released for public comment, with the possibility of a second workshop depending on feedback. Mid-market voices are crucial in shaping practical implementation guidance.
Build Internal Readiness
- Train your security team on AI fundamentals
- Establish relationships with AI system owners
- Create governance structures for AI security decisions
The Bottom Line: Act Now, Refine Later
“We are at a watershed moment where everybody’s talking about how artificial intelligence is helping both the defenders as well as the attackers,” Megas said. The NIST Cyber AI Profile represents the government’s acknowledgment that AI security needs structured, practical guidance.
For mid-market companies, this framework offers something unprecedented: enterprise-level AI security guidance designed for real-world implementation constraints. The security challenges facing mid-market companies will only grow more complex as we move further into 2025. Applying a NIST 2.0 foundation and keeping pace with the updates is one of the smartest things mid-level companies can do.
The framework isn’t available yet, but the preparation can — and should — start now. Begin with an AI inventory, map your current controls, and start building the governance structures you’ll need. When NIST releases the Cyber AI Profile in the coming months, you’ll be ready to implement rather than scramble.
Because in AI security, as in most cybersecurity challenges, the companies that thrive are those that start preparing before they’re required to act.
Ready to get ahead of the AI security curve? Start with a comprehensive AI risk assessment using your existing log analysis tools — you might be surprised what you discover about your organization’s AI attack surface.
