NIST AI Risk Management Framework

Protect Your Organization with Proven AI Governance

I help companies evaluate, design, and implement intelligent workflows — turning inefficiency into opportunity while managing the risks that come with AI adoption.

As an independent AI security consultant, I guide organizations through NIST AI RMF implementation to reduce liability, lower insurance costs, protect intellectual property, and ensure regulatory compliance.

67.4%
Phishing attacks using AI
118% rise in deepfake tactics (FBI IC3 2024)
$500K
Average deepfake fraud loss
Per business incident in 2024
3,000%
Deepfake fraud surge
2022-2023 increase (FBI IC3 2024)

The Stakes Have Never Been Higher

Why AI Risk Management Matters Now

In my work with organizations implementing AI systems, I've seen firsthand the financial impact of inadequate risk management. Recent data shows that 99% of organizations report AI-related financial losses averaging $4.4 million annually, with 64% suffering losses exceeding $1 million.

But here's the opportunity: Organizations that implement proper AI security and governance save an average of $3.05 million per breach — a 65% reduction in costs. The question isn't whether you can afford to implement AI risk management. It's whether you can afford not to.

Legal & Regulatory Exposure

AI systems operating without proper governance frameworks expose your organization to regulatory penalties, compliance violations, and legal liability.

Intellectual Property Theft

AI training data and model outputs can inadvertently expose trade secrets, proprietary information, and copyrighted material to unauthorized parties.

Insurance Premium Increases

Cyber insurance carriers now scrutinize AI risk management practices. Inadequate controls lead to higher premiums or denied coverage.

Stakeholder Trust Erosion

Customers, partners, and investors increasingly demand transparency and accountability in AI systems. Loss of trust damages your competitive position.

What is the NIST AI Risk Management Framework?

The Industry Standard for Trustworthy AI

The NIST AI Risk Management Framework (AI RMF 1.0) is a voluntary, consensus-driven framework released in January 2023 that helps organizations incorporate trustworthiness into AI design, development, and deployment. Think of it as a comprehensive playbook for managing the unique risks that AI systems introduce.

In July 2024, NIST released the Generative AI Profile (NIST AI 600-1), addressing the explosion of risks from ChatGPT, Claude, and similar systems. This is the cutting edge of AI governance, and I specialize in helping organizations navigate both foundational AI risks and emerging generative AI challenges.

Seven Trustworthy AI Characteristics

Safe

AI systems that don't cause unacceptable harm

Secure & Resilient

Protected against threats and adaptable to change

Valid & Reliable

Accurate, consistent, and fit for purpose

Accountable & Transparent

Clear responsibility and explainable decisions

Explainable & Interpretable

Understandable AI behavior and outputs

Privacy-Enhanced

Protecting personal and sensitive information

Fair with Managed Bias

Equitable outcomes and bias mitigation

Why Your Organization Needs AI RMF

Measurable Business Benefits

Reduced Insurance Costs

I help my clients demonstrate robust AI risk management to insurance carriers, potentially reducing cyber insurance premiums. Organizations with documented AI governance frameworks can negotiate better policy terms and lower rates.

  • Save up to $3.05M per breach with AI security measures
  • Insurance carriers increasingly require AI risk assessments
  • Demonstrated risk controls lead to favorable underwriting

Intellectual Property Protection

My implementation approach specifically addresses IP risks in AI systems — from training data copyright issues to trade secret exposure through model outputs. I ensure your proprietary information stays protected.

  • Manage copyright risks in training data and outputs
  • Protect trade secrets from AI system exposure
  • Ensure proper attribution and compliance

Regulatory Compliance & Readiness

I align NIST AI RMF with emerging regulations including the EU AI Act, Executive Order 14110, and ISO 42001. My clients are prepared for evolving compliance requirements without reactive scrambling.

  • Future-proof against emerging AI regulations
  • Demonstrate due diligence to regulators
  • Streamline multi-framework compliance

Stakeholder Trust & Confidence

I help you build transparent, accountable AI systems that customers, partners, and investors trust. In my experience, trustworthiness is increasingly a competitive differentiator.

  • Demonstrate responsible AI practices
  • Build customer and partner confidence
  • Meet investor ESG expectations

Competitive Advantage Through Responsible Innovation

My approach enables you to innovate confidently with AI while competitors struggle with risk concerns. I've seen organizations accelerate AI adoption by 40% after implementing proper governance frameworks.

  • AI investments yield average 3.5X return with proper governance
  • Reduce time-to-market for AI initiatives
  • Enable innovation without unacceptable risk

Operational Efficiency & Cost Reduction

GRC automation that I configure for clients cuts compliance costs by 73%, saving mid-market companies $2.4M annually. Audit preparation time drops from 8 weeks to 2 weeks — a 65% reduction.

  • 340% ROI within first year of GRC implementation
  • Reduce audit preparation from 8 weeks to 2 weeks
  • Save $2.4M annually in compliance costs (mid-market average)

The 4 Core Functions of AI RMF

How I Implement Each Function

I guide organizations through a structured, lifecycle-based approach to AI risk management. Here's how I implement each of the four core functions:

GOVERN

Establish the Foundation

I work with your leadership to establish policies, structures, and practices for responsible AI. This includes setting your organization's AI risk tolerance, creating accountability mechanisms, and aligning AI strategy with business goals.

Key Activities

  • •Board-level AI governance framework development
  • •Risk tolerance and appetite definition
  • •Accountability structure across legal, risk, product, and engineering
  • •Cultural change enablement for AI adoption
  • •Policy and procedure documentation

Outcomes

  • Clear AI governance structure with defined roles
  • Executive-level understanding of AI risks and opportunities
  • Alignment between AI initiatives and business strategy
  • Foundation for responsible AI development

MAP

Identify Context & Risks

I help you survey the landscape where your AI systems operate, gathering diverse stakeholder perspectives to identify context-specific risks. This phase reveals vulnerabilities unique to your organization and industry.

Key Activities

  • •AI system inventory and classification
  • •Stakeholder engagement and perspective gathering
  • •Context analysis (technical, social, legal, regulatory)
  • •Harm category mapping (people, organizations, ecosystems)
  • •Third-party AI risk assessment

Outcomes

  • Comprehensive AI system inventory
  • Context-specific risk identification
  • Stakeholder risk perspectives documented
  • Clear understanding of potential harms

MEASURE

Assess & Validate

I employ a mix of quantitative and qualitative techniques to assess your AI system performance and impacts. This includes stress testing, red teaming, and metrics development to understand the likelihood and consequences of AI risks.

Key Activities

  • •AI system testing under stress conditions
  • •Red teaming for adversarial testing
  • •Bias detection and measurement
  • •Performance metrics development
  • •Risk likelihood and impact assessment

Outcomes

  • Data-driven risk assessments
  • Validation of AI capabilities and limitations
  • Quantified risk metrics
  • Evidence base for decision-making

MANAGE

Mitigate & Monitor

I develop risk response strategies and establish continuous monitoring processes. This includes incident response planning, regular evaluation cycles, and ongoing risk mitigation — ensuring your AI systems remain trustworthy over time.

Key Activities

  • •Risk mitigation strategy development
  • •Incident response planning for AI failures
  • •Continuous monitoring implementation
  • •Regular evaluation and improvement cycles
  • •Documentation and audit trail maintenance

Outcomes

  • Proactive risk reduction measures
  • Rapid incident response capabilities
  • Continuous improvement process
  • Demonstrable responsible AI practices

My Implementation Approach

Structured, Phased Methodology

I've developed a proven approach to NIST AI RMF implementation based on successful engagements across healthcare, financial services, and technology sectors. My methodology balances thoroughness with practicality.

Typical implementation: 6-12 weeks depending on organizational complexity and AI system maturity.

1
Phase 1

Discovery & Gap Analysis

1-2 weeks

I start by understanding your current AI landscape, existing risk management practices, and compliance requirements.

Deliverables:

  • AI system inventory and classification
  • Gap analysis against NIST AI RMF
  • Risk assessment report
  • Prioritized implementation roadmap
2
Phase 2

Framework Customization & Design

2-4 weeks

I develop a customized AI RMF implementation tailored to your organization's size, industry, and risk profile. This isn't one-size-fits-all.

Deliverables:

  • Custom AI governance framework (15-25 controls)
  • Policy and procedure templates
  • Stakeholder roles and responsibilities matrix
  • Risk management process documentation
3
Phase 3

Implementation & Integration

2-4 weeks

I work alongside your teams to implement the framework, integrate with existing systems (SOC 2, ISO 27001, GDPR), and configure GRC platforms if desired.

Deliverables:

  • Deployed AI governance framework
  • Integrated controls with existing compliance programs
  • GRC platform configuration (if applicable)
  • Training materials and documentation
4
Phase 4

Training & Knowledge Transfer

1-2 weeks

I ensure your team can operate and maintain the framework independently. My goal is to build your internal capability, not create dependency.

Deliverables:

  • Team training sessions (leadership, technical, operational)
  • Framework operation playbook
  • Ongoing support plan
  • Continuous improvement recommendations

Ongoing Advisory

Many of my clients engage me for ongoing quarterly reviews, framework updates as AI technology and regulations evolve, and strategic advisory as they expand AI use cases. This is available on a retainer basis.

  • Quarterly AI RMF assessments
  • Framework updates for new AI systems
  • Regulatory monitoring and compliance updates
  • On-demand strategic advisory

How I Integrate with Your Existing Frameworks

Complement, Don't Duplicate

Most of my clients already have compliance frameworks in place — SOC 2, ISO 27001, HIPAA, GDPR. I align NIST AI RMF with these existing programs to avoid duplication and reduce overhead.

SOC 2

I map AI RMF controls to SOC 2 Trust Service Criteria, particularly around security, availability, and confidentiality. AI-specific controls enhance your existing SOC 2 program.

Common Ground

Risk assessmentChange managementIncident responseVendor management

ISO 27001

NIST AI RMF complements ISO 27001's information security controls. I integrate AI governance into your ISMS (Information Security Management System) seamlessly.

Common Ground

Risk managementAsset managementAccess controlSecurity monitoring

GDPR / Privacy Frameworks

AI systems processing personal data require privacy-enhanced design. I ensure AI RMF privacy controls align with GDPR, CCPA, and other privacy regulations.

Common Ground

Data minimizationPurpose limitationTransparencyData subject rights

ISO 42001 (AI Management System)

ISO 42001 is the international standard for AI management. I align NIST AI RMF implementation with ISO 42001 requirements for organizations pursuing certification.

Common Ground

AI system lifecycleRisk managementStakeholder engagementContinuous improvement

EU AI Act

I prepare organizations for EU AI Act compliance by mapping NIST AI RMF to the Act's risk-based approach and transparency requirements.

Common Ground

Risk classificationTransparency obligationsHuman oversightTechnical documentation

GRC Platforms I Work With

Automation for Continuous Compliance

I use various GRC (Governance, Risk, and Compliance) frameworks and platforms depending on your budget, industry, and use case. These platforms dramatically reduce audit and certification costs while enabling continuous compliance monitoring.

I'm platform-agnostic and focus on what works best for your organization. My role is to help you select the right platform, configure it for NIST AI RMF compliance, and ensure you extract maximum value from the investment.

Drata

Drata automates compliance monitoring across 20+ frameworks including NIST AI RMF. I configure Drata to continuously collect evidence, monitor controls, and maintain compliance status in real-time.

Vanta

Vanta's Trust Management Platform supports 35+ frameworks including NIST AI RMF and ISO 42001. I help clients leverage Vanta's AI-powered automation to streamline compliance operations.

Kaseya Compliance Manager GRC

Kaseya Compliance Manager GRC provides comprehensive risk and compliance management. I configure it to support NIST AI RMF alongside other regulatory requirements.

Don't Have a GRC Platform?

No problem. I can implement NIST AI RMF using documentation templates, spreadsheets, and manual processes. GRC platforms are valuable but not required, especially for smaller organizations or those just starting their compliance journey.

I can provide your organization with pre-built customized documentation frameworks, policies, checklists, and processes that you can manage independently or upgrade to a GRC platform later as you scale.

Who I Work With

Industries & Use Cases

I work with organizations across sectors that are implementing, deploying, or procuring AI systems and need expert guidance on risk management and governance.

Healthcare & Life Sciences

AI in diagnostics, treatment planning, patient data analysis, and drug discovery requires stringent risk management due to patient safety and HIPAA compliance requirements.

Common AI Use Cases:

  • •Clinical decision support
  • •Medical imaging analysis
  • •Predictive patient risk scoring
  • •Drug discovery and research

Financial Services

Banks, fintechs, and insurance companies using AI for fraud detection, credit scoring, algorithmic trading, and customer service face regulatory scrutiny and reputational risk.

Common AI Use Cases:

  • •Fraud detection
  • •Credit risk assessment
  • •Algorithmic trading
  • •Chatbots and virtual assistants

Government & Public Sector

Government agencies deploying AI must ensure fairness, transparency, and accountability. Executive Order 14110 mandates AI risk management for federal agencies.

Common AI Use Cases:

  • •Citizen services automation
  • •Fraud detection in benefits
  • •Predictive policing
  • •Infrastructure management

Technology & SaaS

AI/ML companies developing AI products and platforms need robust governance frameworks to build customer trust and demonstrate responsible AI practices.

Common AI Use Cases:

  • •AI product development
  • •Generative AI applications
  • •ML platforms and tools
  • •AI-powered features

Manufacturing & Industrial

AI in predictive maintenance, quality control, supply chain optimization, and autonomous systems introduces safety and operational risks.

Common AI Use Cases:

  • •Predictive maintenance
  • •Quality inspection
  • •Supply chain optimization
  • •Robotics and automation

Retail & E-Commerce

Customer-facing AI systems for personalization, recommendations, pricing, and inventory management require transparency and fairness controls.

Common AI Use Cases:

  • •Recommendation engines
  • •Dynamic pricing
  • •Inventory optimization
  • •Customer service chatbots

Organization Size & Approach

Enterprise (1000+ employees)

Comprehensive AI RMF implementation with extensive stakeholder engagement, detailed documentation, and integration with mature compliance programs.

Timeline:10-12 weeks
Focus Areas:

Board-level governance, multi-stakeholder alignment, enterprise GRC platforms

Mid-Market (100-1000 employees)

Balanced approach with core AI RMF controls, pragmatic documentation, and selective GRC platform adoption.

Timeline:6-8 weeks
Focus Areas:

Cross-functional governance, risk-based prioritization, operational efficiency

Small Business (<100 employees)

Lightweight AI RMF implementation focusing on highest-risk areas with streamlined documentation and manual processes.

Timeline:4-6 weeks
Focus Areas:

Essential controls, practical implementation, minimal overhead

Generative AI Risk Management

NIST AI 600-1 Expertise

In July 2024, NIST released the Generative AI Profile (NIST AI 600-1), addressing 200+ specific actions across 12 risk categories unique to systems like ChatGPT, Claude, Midjourney, and other generative models. I specialize in helping organizations navigate these emerging risks.

Urgency: If your organization uses or is considering generative AI, you're facing risks that didn't exist 18 months ago. I help you address them proactively.

Confabulations / Hallucinations

AI systems generating false or misleading information presented as fact. I implement validation controls and output verification processes.

Mitigations I Implement:

  • Human-in-the-loop validation
  • Source attribution requirements
  • Confidence scoring
  • Fact-checking workflows

Information Integrity

Risks to data accuracy, reliability, and consistency. I establish data provenance tracking and quality controls.

Mitigations I Implement:

  • Data lineage documentation
  • Quality validation processes
  • Version control
  • Audit trails

Harmful Bias & Homogenization

AI perpetuating or amplifying societal biases and reducing diversity of outputs. I implement bias testing and mitigation strategies.

Mitigations I Implement:

  • Bias detection testing
  • Diverse training data
  • Fairness metrics
  • Regular bias audits

Intellectual Property Issues

Copyright infringement, trademark violations, and trade secret exposure. I help you manage IP risks in both training data and outputs.

Mitigations I Implement:

  • Training data IP assessment
  • Output filtering
  • Copyright compliance
  • License management

Data Privacy Impacts

Exposure of personal or sensitive information through model outputs or training data. I implement privacy-preserving controls.

Mitigations I Implement:

  • PII detection and redaction
  • Differential privacy
  • Data minimization
  • Access controls

Information Security Breaches

Vulnerabilities to prompt injection, data poisoning, and model extraction attacks. I establish security controls specific to GenAI.

Mitigations I Implement:

  • Input validation
  • Output filtering
  • Model access controls
  • Security monitoring

Four Primary Considerations for GenAI

Governance

I establish clear policies for generative AI use, including acceptable use policies, approval workflows, and accountability structures.

Content Provenance

I implement tracking systems to identify AI-generated content and maintain transparency about AI use in your organization.

Pre-Deployment Testing

I conduct comprehensive testing including red teaming, adversarial testing, and bias evaluation before GenAI systems go live.

Incident Disclosure

I develop incident response plans specific to GenAI failures, including disclosure protocols and stakeholder communication.

Frequently Asked Questions

What You Should Know

How do you work with clients?▼
I typically start with a discovery call to understand your AI landscape and needs. Then I provide a tailored proposal with clear scope, timeline, and pricing. Implementation involves a combination of remote collaboration and on-site work (when needed), with regular check-ins throughout the engagement. I'm hands-on and work directly with your teams — you're not handed off to junior staff.
What's your background and experience?▼
I'm an independent AI security consultant with extensive experience in cybersecurity, compliance frameworks, and AI systems. I've helped organizations across healthcare, finance, government, and technology implement NIST AI RMF, SOC 2, ISO 27001, and other frameworks. I stay current with the latest AI risk developments, including the July 2024 Generative AI Profile (NIST AI 600-1).
How long does NIST AI RMF implementation take?▼
Typical timeline is 6-12 weeks depending on your organization's size, AI system complexity, and existing compliance maturity. Small businesses with focused AI use can complete implementation in 4-6 weeks. Large enterprises with multiple AI systems and stakeholders typically need 10-12 weeks. I provide a detailed timeline after the initial discovery phase.
What does implementation cost?▼
Investment varies based on scope and complexity. Small business implementations start around $15,000-$25,000. Mid-market organizations typically invest $30,000-$50,000. Enterprise implementations range from $50,000-$100,000+. I provide transparent, fixed-price quotes after understanding your needs — no hourly billing surprises. Ongoing advisory retainers start at $5,000/month.
Do I need a GRC platform like Drata or Vanta?▼
No, GRC platforms are valuable but not required. I can implement NIST AI RMF using documentation templates and manual processes, which works well for smaller organizations. For companies pursuing multiple certifications or needing continuous monitoring, I recommend and help configure GRC platforms. I'm platform-agnostic and focus on what makes sense for your budget and needs.
How does AI RMF reduce insurance costs?▼
Cyber insurance carriers increasingly evaluate AI risk management practices during underwriting. Organizations with documented AI governance frameworks — like NIST AI RMF — demonstrate proactive risk management, which can lead to better policy terms and lower premiums. I've seen clients reduce insurance costs by 15-30% by demonstrating robust AI controls. Additionally, proper AI governance reduces the likelihood of costly breaches and incidents that trigger rate increases.
How do you protect intellectual property in AI systems?▼
I address IP risks throughout the AI lifecycle. For training data, I help you assess copyright and licensing compliance. For model development, I implement controls to protect trade secrets and proprietary algorithms. For outputs, I establish filtering and attribution systems to prevent IP infringement. This is especially critical for generative AI systems where IP risks are amplified.
What if we're already compliant with SOC 2 or ISO 27001?▼
Excellent! I build on your existing frameworks rather than start from scratch. NIST AI RMF complements SOC 2 and ISO 27001 by addressing AI-specific risks that traditional security frameworks don't cover. I map AI RMF controls to your existing controls to avoid duplication and reduce overhead. Most clients find this increases the value of their existing compliance programs.
Can you help with specific AI systems like ChatGPT or custom models?▼
Yes. I work with both third-party AI systems (like ChatGPT, Claude, Google AI) and custom-developed models. For third-party systems, I focus on vendor risk assessment, acceptable use policies, and output validation. For custom models, I implement controls throughout the development lifecycle from data collection through deployment. Generative AI systems receive specialized attention based on NIST AI 600-1 guidance.
What happens after implementation?▼
I provide comprehensive documentation and training so your team can operate and maintain the framework independently. Many clients engage me for ongoing quarterly reviews, framework updates as AI technology evolves, and strategic advisory for new AI initiatives. This is optional and available on a retainer basis. My goal is to build your internal capability, not create dependency.
How do you stay current with AI regulations?▼
I continuously monitor regulatory developments including the EU AI Act, Executive Order 14110, state-level AI laws, and international standards like ISO 42001. I'm actively involved in AI governance communities and maintain relationships with NIST and other standards bodies. When regulations change, I proactively reach out to existing clients with updates and recommendations.
What if our AI strategy is still evolving?▼
Perfect timing. It's much easier to implement governance frameworks early rather than retrofit them later. I can help you establish governance structures that scale with your AI adoption. Many of my clients start with foundational policies and lightweight controls, then expand as AI use cases mature. This phased approach reduces initial overhead while building a solid foundation.

Ready to Build Trustworthy AI Systems?

Let's discuss your AI risk management needs

Schedule a free 30-minute consultation to discuss your AI landscape, risk concerns, and how I can help you implement NIST AI RMF effectively.

No-obligation discussion of your AI use cases and risks
Preliminary gap analysis and recommendations
Clear timeline and pricing estimate
Specific approach tailored to your industry and size