Frequently Asked Questions

The AI RMM is a conceptual framework used to assess, measure, and improve the resilience of organisations using or planning to use (AI) Artificial Intelligence. The Model examines how organizations are setup with respect to their AI systems and is structured as a series of levels or stages that represent different degrees of resilience maturity.

The Model aligns with the National Institute of Standards and Technology (NIST) AI Risk Management Framework RMF's Core Functions namely, Govern, Map, Measure, and Manage. It is a tool used to evaluate and measure the effectiveness and sophistication of the risk management processes within such a framework..

Drawing inspiration from the CERT Resilience Maturity Model (CERT RMM), the Model includes maturity levels (Initial, Managed, Defined, Quantitatively Managed, and Optimizing) that organizations can progress through. These levels reflect the organization's capability to proactively manage and respond to AI-related disruptions, considering factors like governance, workforce diversity, accountability, and engagement with external stakeholders.

The primary goal is to guide an organization through a path of continuous improvement in its ability to anticipate, withstand, recover from, and adapt to adverse conditions, stresses, or attacks.

The secondary goal is to establish AI risk management practices within an organization and pinpoint areas of improvement. This enables organizations to make targeted enhancements to their risk management framework.

While a risk management framework lays out the foundational processes and guidelines for identifying and handling risks, a resilience model like the AI-RMM serves as a specialized tool for evaluating and advancing these processes, particularly in the context of AI-related risks.

The AI RMM specifically addresses the nuances and complexities inherent in AI systems, ensuring that an organization’s risk management strategy is comprehensive and adept at handling the evolving landscape of AI technologies.

By integrating the AI RMM with existing frameworks like the NIST AI RMF, organizations benefit from a more robust, AI-focused approach to risk management that not only complies with standard practices but also evolves and improves over time, keeping pace with advancements in AI.

An AI-specific Resilience Maturity Model is essential due to the unique complexities and challenges of AI systems, which aren't fully addressed by general models.

AI systems have intricate, evolving decision-making processes and rely heavily on data quality, making their risk profile distinct. This Model focuses on these unique aspects, ensuring resilience strategies are effectively tailored to manage AI's specific risks and opportunities.

While CERT RMM provides a broad foundation for organizational resilience, AI RMM zeroes in on the specific demands and peculiarities of AI systems, offering a more tailored framework for organizations to navigate the unique landscape of AI resilience.

AI RMM is designed explicitly to address the complexities and risks inherent in AI systems, such as algorithmic bias, data privacy concerns, and the unpredictability of AI decision-making processes. CERT RMM, while comprehensive, is more generalist in nature, focusing on broader organizational resilience and not specifically on AI.

AI RMM takes into account the rapidly evolving nature of AI technologies. It provides guidelines and best practices that are adaptable to the fast-paced advancements in AI, something that general frameworks like CERT RMM might not address as explicitly.

Given the critical role of data in AI systems, AI RMM places a significant emphasis on data management, quality, and integrity. This is in contrast to CERT RMM, which covers data aspects but not specifically from an AI perspective.

AI RMM integrates specific considerations related to AI ethics, transparency, and accountability. These are essential due to the unique impact AI decisions can have. While CERT RMM encompasses governance and ethics, it doesn’t focus as deeply on these aspects as they pertain to AI.

AI RMM offers resilience strategies and practices that are customized for AI deployments within organizations. In contrast, CERT RMM provides a broader framework applicable to various types of technologies and operational processes.

Organizations should consider implementing the AI Resilience Maturity Model (AI RMM) whenever AI technologies become a significant component of their operations or strategic vision. This could be at the outset of AI integration, during the expansion of existing AI capabilities, or as part of a broader initiative to strengthen organizational governance around AI.

AI RMM helps identify and manage risks specific to AI, ensuring these are incorporated into the organization's wider risk management strategy.

• It provides guidance on best practices tailored to AI, from data handling to ethical AI use, ensuring these practices align with the organization's resilience goals.
• The Model aids in adhering to regulatory requirements and ethical standards, crucial for maintaining trust and integrity in AI applications.
• AI RMM advocates for ongoing assessment and enhancement of AI strategies, keeping pace with technological advancements and emerging risks

By focusing on AI's dynamic nature, the Model ensures the organization remains adaptable and resilient in the face of AI-related challenges and opportunities.

The AI Resilience Maturity Model (AI RMM) is designed for a wide range of stakeholders within an organization, each with varying roles and interests in AI implementation and management. The key targeted groups include:

• Senior Management and Executives - They utilize AI RMM for strategic decision-making and resource allocation concerning AI initiatives.
• AI and Technology Teams - These professionals, including data scientists, AI developers, and IT staff, use the Model to guide the development, deployment, and management of AI systems.
• Risk Management and Compliance Teams - They apply AI RMM to ensure AI practices align with risk management strategies and comply with regulations.
• Operations Management – Operational leaders use the Model to integrate AI systems effectively into business processes.
• Human Resources and Training Departments - These stakeholders focus on aligning workforce capabilities and training with AI maturity requirements.
• Project Managers - Responsible for implementing AI projects in line with the organization's maturity level and strategic goals.
• Board of Directors - They leverage AI RMM for governance and oversight, ensuring AI initiatives align with broader organizational objectives.
• External Stakeholders - This includes regulators, customers, and partners who have an interest in the organization's AI maturity and practices.

In terms of accessibility, the AI RMM is designed to be approachable and understandable by these diverse stakeholder groups. It typically includes clear language, practical examples, and actionable guidelines tailored to each group's specific needs and levels of expertise. This ensures that regardless of their technical background or role within the organization, stakeholders can effectively engage with and apply the AI RMM to enhance AI resilience and maturity.

The AI Resilience Maturity Model (AI RMM) is the result of a collaborative effort initiated by two seasoned experts who recognized the critical need for a comprehensive framework to address the unique challenges posed by AI systems. Leveraging their expertise in security, risk management, and AI technologies, the initial version of the AI RMM was meticulously crafted to establish a solid foundation.

From its inception, the AI RMM was designed as a living framework, welcoming contributions from the academic and larger community. The intent with this Model has always been to foster a collaborative environment where diverse perspectives could enrich the Model, reflecting the evolving nature of AI risks and resilience strategies.

The timeline of AI RMM's development began with the initial conceptualization and design phase. Following this, a draft version will be released for public scrutiny, inviting feedback from the academic and professional communities.

The AI RMM is not a static document; rather, it is a dynamic resource that evolves with the advancements in AI technologies and the ever-changing threat landscape. Ongoing collaboration with the community ensures that the Model remains relevant and effective in addressing emerging challenges.

AI RMM helps organizations bridge the gaps between broad legislative requirements and practical implementation. It provides a structured approach that can be tailored to meet specific regulatory needs and standards in the AI domain.

More specifically, the AI RMM is particularly instrumental in the context of the EU AI Act. This legislation establishes a broad framework for regulating AI systems in the European Union, focusing on risk assessment and ethical standards. The AI RMM complements this act by offering organizations a detailed, actionable approach to achieving compliance. It guides them through the intricacies of aligning their AI systems with the Act’s requirements, particularly in areas like risk management, data governance, transparency, and accountability. By following the structured path laid out by the AI RMM, organizations can more effectively navigate the EU AI Act's regulations, ensuring their AI systems are not only compliant but also ethically and socially responsible.

In the United States, the AI Security Bill focuses on the security and integrity of AI systems, emphasizing the importance of safeguarding against malicious use and ensuring AI robustness. The AI RMM enhances an organization's ability to adhere to the principles of this bill by providing a comprehensive framework for assessing and strengthening the security aspects of AI. It addresses key components such as cybersecurity, data protection, and system resilience, which are crucial for complying with the bill. The AI RMM helps organizations to systematically evaluate and fortify their AI systems, ensuring that they meet the security standards and best practices outlined in the US AI Security Bill, thereby fostering a secure and trustworthy AI environment.

Regarding the ISO 42000 series, which sets international standards for AI systems, the AI RMM plays a critical role in guiding organizations to meet these global benchmarks. The ISO series covers various aspects of AI, including quality, safety, and ethical considerations. The AI RMM aligns with these standards by providing a structured approach to implementing best practices in AI development and deployment. It aids in ensuring that AI systems are not only efficient and effective but also adhere to the ethical and safety standards set by the ISO. By incorporating the guidelines of the AI RMM, organizations can ensure that their AI systems are internationally compliant, demonstrating a commitment to excellence and responsibility in the field of AI.

Many AI legislations set out general principles and requirements. The AI RMM offers a more detailed, step-by-step roadmap to achieve compliance, making it easier for organizations to understand and meet legal obligations. This provides a vital tool for organizations navigating the regulatory landscape, ensuring that their AI systems are compliant, secure, and ethically responsible, in line with e.g., the EU AI Act, the US AI Security Bill, and the ISO 42000 series standards.

Absolutely. Recognizing the importance of supporting organizations on their AI resilience journey, we are committed to providing comprehensive guidance for seamless implementation of the AI Resilience Maturity Model (AI RMM).

RiskFrame.ai is building a SaaS solution that empowers organizations to adapt swiftly and effectively to evolving AI resilience needs.

• Comprehensive Implementation Support - Our SaaS solution comes bundled with a rich set of resources, including detailed documentation, tutorials, and best practices to guide organizations through each stage of AI RMM implementation. This ensures that even those without extensive AI expertise can navigate the process confidently.
• Tailored Recommendations - Understanding that each organization is unique, our SaaS solution incorporates adaptive features that generate tailored recommendations based on the specific context, industry, and risk profile of the user.
• Continuous Updates and Insights - The landscape of AI risks is dynamic, and our commitment extends beyond the initial implementation phase. Our SaaS solution provides real-time updates, insights, and alerts to keep organizations informed about emerging threats and industry best practices.
• Community Collaboration - Building a community of users is integral to our approach. Organizations can connect with peers, share experiences, and seek advice within our platform. This collaborative environment fosters knowledge exchange and helps organizations learn from each other's successes and challenges.
• Feedback Mechanism - We value user feedback and actively seek input to enhance our SaaS solution continually. Organizations can contribute to the refinement of the AI RMM implementation process by sharing insights, challenges, and suggestions. This iterative feedback loop ensures that the solution evolves in alignment with user needs.

In essence, our SaaS solution is not just a tool; it is a comprehensive support system designed to accelerate the AI resilience journey for organizations. From initial setup to ongoing adaptation, we are dedicated to ensuring that organizations can leverage the AI RMM effectively, adapt rapidly to changing landscapes, and build resilient AI systems that inspire confidence.

We welcome and encourage active participation from organizations / individuals in the continuous development and improvement of the AI Resilience Maturity Model (AI RMM). By publishing the framework as an open-source repository on GitHub, we aim to create a collaborative ecosystem where contributions from diverse perspectives enhance the robustness of the Model.

Organizations / individuals can fork the AI RMM repository on GitHub, make modifications, and contribute enhancements. Whether it's identifying vulnerabilities, suggesting improvements, or adding new features, direct engagement from the community is invaluable.

We value input from organizations / individuals on features that would enhance the practical utility of the AI RMM. GitHub provides a platform for submitting feature requests and engaging in discussions about the rationale behind specific enhancements.

Organizations / individuals can contribute to the success of the AI RMM by promoting awareness within their networks. Sharing the repository, encouraging participation, and advocating for the use of the framework in the wider AI community all contribute to its impact.

The AI-RMM is licensed under the GNU General Public License (GPL), which is designed to ensure that all enhancements and modifications to the AI-RMM remain open and accessible to the community. For the purposes of AI-RMM, "source code" is defined as the comprehensive set of files (including text, markdown, Excel, etc.) that contain the detailed definitions of the practices and sub-practices organizations should follow to enhance their resilience in using AI systems. This includes all documentation, guides, templates, and materials necessary for understanding, implementing, and adapting the AI-RMM.

Internal Use

If an organization modifies any part of the AI-RMM (such as an Excel file, as defined above as the source code) but does not distribute the modified version outside of the organization, the GPL does not require these modifications to be shared. This is in accordance with the GPL's stance that the mere use of the work within an organization, without external distribution, does not trigger the obligation to disclose modifications.

Distribution

When the modified work is distributed, conveyed, or otherwise made available to entities outside the original organization, the GPL's copyleft provisions come into effect. This requires that the modified version be made available under the same GPL terms. This includes ensuring that any distributed copies are accompanied by the GPL license text, a clear statement of any changes made, and providing access to the modified "source code" in this case, the modified contents of the AI-RMM documentation and materials.

Derivative Works

The GPL permits the creation of derivative works based on the AI-RMM, but it mandates that such works, if distributed, must also be licensed under the GPL. This provision is intended to preserve the open-source nature of the AI-RMM, fostering a collaborative environment where improvements are shared, benefiting the broader community.

By adopting the GPL for AI-RMM, we aim to encourage innovation and collaboration within the community, ensuring that valuable insights and enhancements remain freely available and contribute to the collective knowledge and resilience in AI usage.

For any additional questions or clarifications about the AI RMM, organizations can reach out via email to framework@riskframe.ai.