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.
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.
AI systems operating without proper governance frameworks expose your organization to regulatory penalties, compliance violations, and legal liability.
AI training data and model outputs can inadvertently expose trade secrets, proprietary information, and copyrighted material to unauthorized parties.
Cyber insurance carriers now scrutinize AI risk management practices. Inadequate controls lead to higher premiums or denied coverage.
Customers, partners, and investors increasingly demand transparency and accountability in AI systems. Loss of trust damages your competitive position.
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.
AI systems that don't cause unacceptable harm
Protected against threats and adaptable to change
Accurate, consistent, and fit for purpose
Clear responsibility and explainable decisions
Understandable AI behavior and outputs
Protecting personal and sensitive information
Equitable outcomes and bias mitigation
Measurable Business Benefits
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.
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.
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.
I help you build transparent, accountable AI systems that customers, partners, and investors trust. Trustworthiness is a competitive differentiator.
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.
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.
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:
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.
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.
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.
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.
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 to 12 weeks depending on organizational complexity and AI system maturity.
I start by understanding your current AI landscape, existing risk management practices, and compliance requirements.
I develop a customized AI RMF implementation tailored to your organization's size, industry, and risk profile. This isn't one size fits all.
I work alongside your teams to implement the framework, integrate with existing systems like SOC 2, ISO 27001, and GDPR, and configure GRC platforms if desired.
I ensure your team can operate and maintain the framework independently. My goal is to build your internal capability, not create dependency.
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.
Complement, Don't Duplicate
Most of my clients already have compliance frameworks in place like SOC 2, ISO 27001, HIPAA, and GDPR. I align NIST AI RMF with these existing programs to avoid duplication and reduce overhead.
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.
NIST AI RMF complements ISO 27001's information security controls. I integrate AI governance into your ISMS (Information Security Management System) seamlessly.
AI systems processing personal data require privacy-enhanced design. I ensure AI RMF privacy controls align with GDPR, CCPA, and other privacy regulations.
ISO 42001 is the international standard for AI management. I align NIST AI RMF implementation with ISO 42001 requirements for organizations pursuing certification.
I prepare organizations for EU AI Act compliance by mapping NIST AI RMF to the Act's risk-based approach and transparency requirements.
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 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'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 provides comprehensive risk and compliance management. I configure it to support NIST AI RMF alongside other regulatory requirements.
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.
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.
AI in diagnostics, treatment planning, patient data analysis, and drug discovery requires stringent risk management due to patient safety and HIPAA compliance requirements.
Banks, fintechs, and insurance companies using AI for fraud detection, credit scoring, algorithmic trading, and customer service face regulatory scrutiny and reputational risk.
Government agencies deploying AI must ensure fairness, transparency, and accountability. Executive Order 14110 mandates AI risk management for federal agencies.
AI/ML companies developing AI products and platforms need robust governance frameworks to build customer trust and demonstrate responsible AI practices.
AI in predictive maintenance, quality control, supply chain optimization, and autonomous systems introduces safety and operational risks.
Customer-facing AI systems for personalization, recommendations, pricing, and inventory management require transparency and fairness controls.
Comprehensive AI RMF implementation with extensive stakeholder engagement, detailed documentation, and integration with mature compliance programs.
Board-level governance, multi-stakeholder alignment, enterprise GRC platforms
Balanced approach with core AI RMF controls, pragmatic documentation, and selective GRC platform adoption.
Cross-functional governance, risk-based prioritization, operational efficiency
Lightweight AI RMF implementation focusing on highest-risk areas with streamlined documentation and manual processes.
Essential controls, practical implementation, minimal overhead
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.
AI systems generating false or misleading information presented as fact. I implement validation controls and output verification processes.
Risks to data accuracy, reliability, and consistency. I establish data provenance tracking and quality controls.
AI perpetuating or amplifying societal biases and reducing diversity of outputs. I implement bias testing and mitigation strategies.
Copyright infringement, trademark violations, and trade secret exposure. I help you manage IP risks in both training data and outputs.
Exposure of personal or sensitive information through model outputs or training data. I implement privacy-preserving controls.
Vulnerabilities to prompt injection, data poisoning, and model extraction attacks. I establish security controls specific to GenAI.
I establish clear policies for generative AI use, including acceptable use policies, approval workflows, and accountability structures.
I implement tracking systems to identify AI-generated content and maintain transparency about AI use in your organization.
I conduct comprehensive testing including red teaming, adversarial testing, and bias evaluation before GenAI systems go live.
I develop incident response plans specific to GenAI failures, including disclosure protocols and stakeholder communication.
What You Should Know
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.