Reflections on NIST Symposium in September 2024, Part 2

Written by Ken Huang, CEO of DistributedApps.ai and VP of Research at CSA GCR.

1. Introduction and Background

On September 24, 2024, I had the privilege of attending the NIST symposium "Unleashing AI Innovation, Enabling Trust." This event brought together leading experts, policymakers, and industry professionals to discuss the current state and future trajectory of artificial intelligence. I wrote Part 1 of my reflection on synthetic content issues.

This is Part 2 of my reflection, which focuses on the panel titled "Advancing Generative AI Risk Management: Understanding Risks, AI Actors, and Mitigations Across the AI Value Chain."

The panel was moderated by Chloe Autio, a NIST Associate known for her work in AI ethics and policy. The panel featured an impressive lineup of experts:

• Helen Toner from the Center for Security and Emerging Technology, bringing insights on the geopolitical implications of AI
• Taja Naidoo from Meta and Open Loop, offering perspectives from a major tech company actively involved in AI development
• Jessica Newman from the University of California, Berkeley, providing academic insights on AI ethics and governance
• Chris Meserole from the Foundation Model Forum, representing a coalition focused on the responsible development of large language models

This panel discussion was particularly inspiring, as it stimulated my thinking about the AI value chain, shared responsibilities, and risk management in ways I hadn't considered before. The diverse perspectives offered by the panelists, combined with the urgent need for a comprehensive framework to manage AI risks, motivated me to write this article. My goal is to expand on the concepts discussed during the panel and propose a more comprehensive framework for understanding and managing AI risks across different layers of the ecosystem.

2. The Three-Layer AI Value Chain: A Foundation for Understanding

During the panel, Jessica Newman proposed a three-layer model for understanding the generative AI value chain. This concept immediately struck me as both elegant and powerful in its ability to capture the complex ecosystem of AI development and deployment. The three layers she proposed are:

1. Provider Layer: This includes the foundational elements of AI systems, such as model providers (e.g., OpenAI, Google, Anthropic) and GPU cloud providers that offer the computational resources necessary for AI development and deployment.
2. Application Layer: This encompasses the tools and services that build upon the provider layer, including fine-tuning services, Retrieval-Augmented Generation (RAG) applications, AI agents, and various other AI applications that leverage foundation models to create more specialized or task-specific AI solutions.
3. User Layer: This represents the end-users of AI systems, whether they are individuals, businesses, or organizations that interact with and benefit from AI applications in various domains.

What makes this model particularly compelling is its ability to clearly delineate the different stages of AI development and deployment while also highlighting the interconnections between these layers. As I reflected on this model, I realized that it bears a striking resemblance to another well-established framework in the tech industry: the cloud computing service models advocated by NIST.

3. Mapping to Cloud Security: Enhancing Understanding and Applicability

The parallels between Newman's AI value chain model and the cloud computing service models (IaaS, PaaS, SaaS) advocated by NIST are not just coincidental. They reflect a fundamental similarity in how complex technological ecosystems are structured and how responsibilities are distributed among different actors. By mapping these concepts, we can leverage existing knowledge and best practices from the cloud computing domain to better understand and manage the AI ecosystem.

3.1 IaaS (Infrastructure as a Service) - Provider Layer

In the cloud computing model, IaaS providers offer the fundamental computing resources - processing power, storage, and networking - upon which other services are built. In the AI ecosystem, this layer encompasses:

• Model Providers: Organizations like OpenAI, Google, and Anthropic that develop and offer foundation models.
• Data Providers: Companies and organizations that curate and provide large datasets used for training AI models such as Scale AI.
• GPU and Compute Providers: Hardware manufacturers like NVIDIA and AMD that produce the specialized computing resources needed for AI workloads.
• Cloud Infrastructure Providers: Companies like AWS, Azure, and Google Cloud that offer the scalable computing infrastructure necessary for AI development and deployment.

3.2 PaaS (Platform as a Service) - Application Layer

PaaS in cloud computing offers a platform for developers to build, run, and manage applications without the complexity of maintaining the underlying infrastructure. In the AI context, this layer includes:

• Fine-tuning Tools and Frameworks: Services and platforms that allow developers to adapt foundation models for specific tasks or domains.
• Vector Databases: Specialized databases like Pinecone and Weaviate that are optimized for AI-related tasks such as semantic search and recommendation systems.
• RAG (Retrieval-Augmented Generation) Frameworks: Tools that enhance language models with the ability to access and leverage external knowledge bases.
• Search APIs and AI SDKs: Services that provide developers with easy-to-use interfaces for integrating AI capabilities into their applications.
• Orchestration Services: Platforms that help manage and coordinate complex AI workflows and pipelines such as Zapier.
• Agent Development Platforms: Tools and frameworks for creating AI agents capable of performing complex, multi-step tasks such as Crew.ai, AuthGens, and many other Agent frameworks

3.3 SaaS (Software as a Service) - User Layer

SaaS provides end-users with ready-to-use software applications. In the AI ecosystem, this layer represents:

• AI-Powered Productivity Tools: Applications like AI writing assistants, code completion tools, or design aids that leverage AI to enhance user productivity.
• Industry-Specific AI Solutions: Specialized AI applications tailored for specific sectors such as healthcare diagnostics, financial fraud detection, or autonomous vehicles.
• Consumer-Facing AI Applications: AI-driven services that interact directly with end-users, such as virtual assistants, recommendation systems, or personalized learning platforms.

3.4 Users/Actors as a Vertical Layer

An important addition to this model is the concept of users or actors as a vertical layer that cuts across all three horizontal layers. This reflects the reality that entities can play multiple roles within the AI ecosystem:

• At the IaaS level, a model provider like OpenAI is a key actor, but it's also a user of cloud infrastructure provided by Azure.
• In the PaaS layer, a company like Pinecone is a provider of vector database services, but it's also a user of AWS's cloud infrastructure.
• At the SaaS level, a product like Salesforce's Einstein GPT is an AI application provider, but it's also a user of underlying cloud and AI services from providers like AWS and Azure.

This vertical layer highlights the interconnected nature of the AI ecosystem and underscores the importance of considering multiple perspectives when addressing risks and responsibilities.

4. Shared Responsibility Model in AI

By framing the AI value chain in this layered manner, we can develop a more nuanced shared responsibility model that accounts for the interconnected nature of AI systems and services. This model helps clarify who is responsible for what aspects of AI development, deployment, and use, ensuring that all stakeholders understand their roles in maintaining a safe and ethical AI ecosystem.

4.1 IaaS (Provider) Layer Responsibilities

Actors in the Provider Layer bear significant responsibilities due to their foundational role in the AI ecosystem:

• Model Integrity and Security: Ensuring the security and integrity of foundation models, including protection against adversarial attacks or unauthorized modifications.
• Ethical AI Development: Adhering to ethical AI development practices, including considerations of bias, fairness, and potential societal impacts.
• Transparency: Maintaining transparency about model capabilities, limitations, and potential risks associated with their use.
• Infrastructure Security: Providing secure and scalable infrastructure for AI workloads, including robust data protection measures and compliance with relevant security standards.
• Resource Efficiency: Developing more energy-efficient hardware and optimizing infrastructure to reduce the environmental impact of AI computations.
• Responsible Scaling: Managing the responsible scaling of AI capabilities, considering potential societal and economic impacts.

4.2 PaaS (Application) Layer Responsibilities

The Application Layer acts as a bridge between foundational technologies and end-user applications, carrying its own set of responsibilities:

• Secure Development Tools: Developing and maintaining secure tools and frameworks for AI application development, including built-in safeguards against common vulnerabilities.
• Access Control and Authentication: Implementing robust access controls and authentication mechanisms to prevent unauthorized use of AI development tools and resources.
• Data Privacy: Ensuring data privacy in vector databases, search APIs, and other data-handling components, complying with relevant data protection regulations.
• Documentation and Guidelines: Providing clear documentation and guidelines for the responsible use of AI development tools and platforms.
• Misuse Prevention: Monitoring and mitigating potential misuse of AI development tools, including implementing safeguards against the creation of harmful AI applications.
• Interoperability and Standards: Promoting interoperability and adherence to industry standards to ensure consistency and reliability across different AI applications.

4.3 SaaS (User) Layer Responsibilities

The User Layer, while often seen as the "end" of the value chain, carries crucial responsibilities in ensuring the responsible use and impact of AI systems:

• Ethical Use: Ensuring responsible and ethical use of AI applications, including consideration of potential biases and fairness issues.
• Governance and Oversight: Implementing proper governance structures and oversight mechanisms for AI systems within organizations.
• User Transparency: Providing clear information to end-users about AI involvement in services, including potential limitations and risks.
• Performance Monitoring: Continuously monitoring AI system outputs and performance to detect and address any issues or unexpected behaviors.
• Feedback Mechanisms: Establishing channels for users to provide feedback or report concerns about AI system behavior.
• Education and Training: Providing adequate training and education to users to ensure they can effectively and responsibly interact with AI systems.

4.4 Vertical (User/Actor) Layer Responsibilities

The vertical layer, representing entities that operate across multiple layers, has unique responsibilities:

• Cross-Layer Understanding: Developing a comprehensive understanding of responsibilities and risks across all layers in which the entity operates.
• Coordinated Risk Management: Implementing coordinated risk management strategies that account for interdependencies between different layers.
• Transparent Communication: Maintaining clear communication channels between different operational levels to ensure consistent risk management and ethical practices.
• Compliance Across Layers: Ensuring compliance with relevant regulations and standards across all layers of operation.
• Collaborative Innovation: Fostering innovation that leverages insights and capabilities from different layers of the AI ecosystem.

5. Risk Management Framework

Building upon the shared responsibility model, we can develop a risk management framework for AI systems that considers the unique challenges and opportunities at each layer of the value chain.

5.1 Risk Identification

Effective risk management begins with thorough risk identification:

• Layer-Specific Risk Assessments: Conduct regular risk assessments tailored to each layer (IaaS, PaaS, SaaS) of the AI value chain.
• Cross-Layer Risk Analysis: Identify potential risks that may arise from interactions between different layers.
• Emerging Risk Monitoring: Establish processes to monitor and identify new and emerging risks as AI technologies evolve.
• Stakeholder Involvement: Involve stakeholders from all layers in risk identification processes to ensure comprehensive coverage.
• Scenario Planning: Use scenario planning techniques to anticipate potential future risks and challenges.

5.2 Risk Analysis

Once risks are identified, they must be thoroughly analyzed:

• Impact Assessment: Evaluate the potential impact of identified risks across all relevant layers of the AI value chain.
• Probability Estimation: Assess the likelihood of risks materializing at each layer and across layer boundaries.
• Interdependency Mapping: Analyze how risks at one layer may affect or amplify risks in other layers.
• Quantitative and Qualitative Analysis: Employ both quantitative metrics and qualitative assessments to fully understand risk profiles.
• Risk Prioritization: Prioritize risks based on their potential impact and likelihood, considering both immediate and long-term consequences.

5.3 Risk Mitigation

Developing effective risk mitigation strategies is crucial:

• Layer-Specific Mitigation Strategies: Develop targeted mitigation strategies that address the unique challenges at each layer of the AI value chain.
• Cross-Layer Mitigation Measures: Implement mitigation measures that address risks spanning multiple layers.
• Adaptive Mitigation: Design flexible mitigation strategies that can adapt to the rapidly evolving AI landscape.
• Ethical Considerations: Ensure that risk mitigation strategies align with ethical AI principles and societal values.
• Residual Risk Management: Develop plans to manage residual risks that cannot be fully mitigated.

5.4 Monitoring and Review

Continuous monitoring and regular review are essential in the dynamic AI ecosystem:

• Real-Time Monitoring: Implement systems for real-time monitoring of AI performance and potential risk indicators across all layers.
• Regular Risk Reassessment: Conduct periodic reassessments of the risk landscape to identify new risks and evaluate the effectiveness of existing mitigation strategies.
• Incident Response Planning: Develop and regularly update incident response plans for potential AI-related crises or failures.
• Feedback Loops: Establish feedback mechanisms to continuously improve risk management processes based on operational experiences and outcomes.
• External Audits: Engage independent third parties to conduct regular audits of AI systems and risk management practices.

5.5 Governance and Compliance

Effective governance and compliance frameworks are crucial for managing AI risks:

• AI Governance Structures: Establish clear governance structures that span all layers of the AI value chain, defining roles, responsibilities, and decision-making processes.
• Regulatory Compliance: Ensure compliance with relevant AI regulations and standards at each layer, staying abreast of evolving regulatory landscapes.
• Ethics Committees: Form AI ethics committees to provide guidance on ethical issues and dilemmas across the AI value chain.
• Transparency and Reporting: Implement transparent reporting mechanisms on AI risks, incidents, and mitigation efforts.
• Industry Collaboration: Participate in industry-wide initiatives to develop standards and best practices for AI risk management.

6. Challenges and Future Directions

While the proposed framework provides a structured approach to understanding and managing risks in the AI ecosystem, several challenges and areas for future development remain:

6.1 Rapid Technological Evolution

The fast-paced nature of AI development poses a significant challenge to risk management efforts. Frameworks and strategies must be flexible enough to adapt to new technologies and emerging risks.

6.2 Regulatory Landscape

The regulatory environment for AI is still evolving, with different approaches being taken in various jurisdictions. Navigating this complex and changing landscape requires ongoing attention and adaptability.

6.3 Balancing Innovation and Risk Management

There's a constant need to balance the drive for innovation with the imperative of responsible development. Overly restrictive risk management practices could stifle innovation, while inadequate safeguards could lead to harmful outcomes.

6.4 Global Coordination

AI development and deployment often occur on a global scale, requiring coordination between different countries and regulatory regimes. Developing harmonized approaches to AI risk management is an ongoing challenge.

6.5 Ethical Considerations

As AI systems become more advanced and autonomous, new ethical challenges emerge. Incorporating ethical considerations into risk management frameworks is an area that requires ongoing research and discussion.

6.6 Quantifying AI Risks

Developing reliable methods for quantifying AI risks, especially for advanced AI systems with potentially far-reaching impacts, remains a significant challenge and area for future research.

7. Conclusion

By adopting this multi-layered approach to the AI value chain, we can create a more robust and comprehensive framework for shared responsibility and risk management in AI systems. This model acknowledges the interconnected nature of AI development and deployment, encouraging collaboration and communication across all layers of the ecosystem.

As the AI landscape continues to evolve at a rapid pace, it's crucial for stakeholders at all levels to work together in addressing the challenges and opportunities presented by this transformative technology. By clearly defining responsibilities and implementing a thorough risk management framework, we can foster innovation while building trust in AI systems across the entire value chain.

The future of AI holds immense promise, but realizing that promise safely and ethically requires a concerted effort from all stakeholders in the AI ecosystem. By embracing shared responsibility and implementing robust risk management practices, we can work towards a future where AI technology enhances human capabilities while upholding our values and ensuring the well-being of society as a whole.