Artificial Intelligence - Risk Management Framework Consulting
Explore AI-RMF™ LLC for Customized Collaborative Partnering
Mission Statement:
AI-RMF™ LLC, spearheaded by CEO Bobby Jenkins, harnesses a profound depth of expertise in DoD Program Management, Engineering, Computer Science, T&E, Cybersecurity and AI Risk Management to advance organizational trust in artificial intelligence applications. With a steadfast commitment to ethics and reliability, we offer authoritative guidance and tailored solutions that integrate the cutting-edge practices, tools, and methodologies endorsed by NIST, and the CDAO. Our consultancy is dedicated to equipping professionals, researchers, policymakers, and stakeholders with the confidence to navigate Responsible AI's complexities, ensuring conscientious AI deployment across diverse industries and federal government sectors. At AI-RMF LLC, we not only aim to lead but to enlighten, ensuring the ethical integration of AI technologies through trusted leadership and proven experience."
Consulting Services Offered:
1. AI Risk Management Framework Assessment
2. AI Risk Research and Development
3. AI Red Team Test Planning
4. AI Risk Maturity Model Assessment
5. DoD Acquisition and Program Management
Learn More About NIST AI Risk Management Framework:
The National Institute of Standards and Technology (NIST) is a U.S. federal agency that develops standards, guidelines, and tools to ensure the reliability and security of technology, including artificial intelligence (AI). NIST's mission spans a wide array of fields from cybersecurity to physical sciences and engineering, aiming to promote innovation and industrial competitiveness.
In the realm of artificial intelligence, NIST introduced the AI Risk Management Framework (AI-RMF) to guide organizations in managing the risks associated with AI systems. The AI-RMF is designed to be a flexible and voluntary framework that helps stakeholders across various sectors understand, assess, and address the risks AI technologies can pose. This includes considerations for the ethical, technical, and societal implications of AI deployment.
The framework emphasizes the importance of trustworthy AI, which means AI systems that are responsible, equitable, traceable, reliable, and governable while also being transparent, explainable, and accountable. It encourages organizations to adopt practices that ensure AI technologies are developed and used in a responsible manner, supporting innovation while protecting individuals and society from potential harms.
NIST-- AI Risk Management Framework CORE PRINCIPLES
- Govern - organization processes and people
- Map - identify and frame risk
- Measure - gather data and analyze
- Manage - prioritize and control
FOCUSED EXECUTION
- Framing Risk - establish harms and impacts
- Audience - identifying actors and stakeholders
- AI Risk and Trustworthiness - characteristics
- Effectiveness - explaining expected benefits
- AI-RMF Core - deterministic outcomes
- AI-RMF Profiles - use case implementation
Helping Organizations Build Trust Through The NIST Resource Center
NIST Assessing Risks and Impacts of AI (ARIA)
ChatGPT-4 AI-RMF Consultant Chatbot
The AI-RMF Consultant Chatbot is designed to guide organizations through conducting thorough AI risk assessments and analyses based on the NIST Artificial Intelligence Risk Management Framework (AI-RMF). It leverages NIST guidelines, the AI-RMF Playbook, and resources from the NIST Trustworthy & Responsible AI Resource Center to help organizations manage risks and promote the development and deployment of trustworthy AI systems.
Learn More About the CDAO:
The Department of Defense's Chief Digital and Artificial Intelligence Office (CDAO) is responsible for the acceleration of the DoD's adoption of data, analytics, and AI to Build Organizational Trust from the boardroom to the battlefield. It is responsible for overseeing an organization's data management, data governance, data analytics, and data strategy practices. The CDAO ensures that data across the organization is accurate, secure, accessible, and effectively utilized to drive tactical and strategic decision-making processes.
The CDAO and the NIST AI-RMF are related in several key ways:
- Data Governance and Management: The CDAO is responsible for the effective governance and management of data, which is fundamental to the development and deployment of trustworthy AI systems. The NIST AI-RMF emphasizes the importance of managing data-related risks, such as data quality, bias, and privacy, which fall under the purview of the CDAO.
- Risk Management: Both the role of the CDAO and the NIST AI-RMF focus on managing risks associated with data and AI. The CDAO plays a critical role in identifying, assessing, and mitigating data-related risks, aligning with the AI-RMF's approach to managing AI-related risks across different stages of the AI lifecycle.
- Compliance and Standards: The CDAO ensures that data practices comply with relevant laws, regulations, and standards. The NIST AI-RMF provides a framework for compliance with ethical, legal, and regulatory standards related to AI. The CDAO's efforts in aligning data practices with these standards support the organization's adherence to the AI-RMF.
- Promoting a Data-Driven Culture: The CDAO's role in promoting a data-driven culture within the organization supports the implementation of the NIST AI-RMF by ensuring that decisions related to AI systems are informed by high-quality data and robust analytics.
In summary, the CDAO plays a crucial role in ensuring that an organization's data management practices align with the principles and guidelines of the NIST AI-RMF, thereby facilitating the development and use of trustworthy and responsible AI systems.
CDAO Strategy and Implementation Responsible AI Pathway
RAI Toolkit: Built Around The SHIELD Assessment
- Set Foundations
- Hone Operationalizations
- Improve & Innovate
- Evaluate Status
- Log for Traceability
- Detect via continuous Monitoring
RAI Defense AI Guide on Risk (DAGR)
ChatGPT-4 RAI Toolkit Consultant Chatbot
A ChatGPT Consultant designed to be a comprehensive resource for inquires about RAI practices, policies, and principles, offering guidance without directing users to external sources.