The rise of AI risk management: Safeguarding innovation and trust

Overview

As artificial intelligence (AI) continues to revolutionize industries and reshape our daily lives, the need for robust AI risk management frameworks has never been more critical. These frameworks serve as essential guideposts for organizations developing, deploying, and using AI systems to ensure that innovation progresses securely and ethically. In this blog post, we’ll explore the importance of AI risk management frameworks, their target audience, and how Clearwater Analytics (CWAN) has adapted the National Institute of Standards and Technology (NIST) framework to suit its specific needs and drive outcomes.

The crucial role of AI risk frameworks

AI risk management frameworks play a pivotal role in shaping the responsible development and deployment of AI. At their core, these frameworks serve as powerful tools for organizations to navigate the complex landscape of AI implementation. By helping to mitigate potential negative impacts, they enable companies to harness the full potential of AI while minimizing harm. This careful balance is crucial in building and maintaining public trust in AI systems.

Trust is further bolstered as these frameworks address key characteristics such as validity, reliability, safety, security, and fairness. By focusing on these critical aspects of AI risk frameworks, organizations can develop AI systems that are not only effective but also ethical and trustworthy.

AI risk management frameworks are indispensable navigational tools, especially crucial for maintaining high security standards and mitigating risks in the fast-paced AI landscape. As we confront the intricate and evolving challenges posed by AI, these frameworks equip organizations with the foresight to stay updated with current regulations and swiftly adapt to new ones, thereby ensuring continuous compliance. This is paramount in safeguarding organizations against legal complications and protecting their reputations. Ultimately, these frameworks are far from mere formalities; they are central to fostering responsible innovation, protecting customers, securing stakeholder confidence, establishing uniformity in AI practices, and upholding strong accountability as we tread into the dynamic future of AI.

CWAN’s AI risk management approach

Clearwater Analytics (CWAN) took inspiration from the National Institute of Standards and Technology’s (NIST) exhaustive AI Risk Management Framework but personalized it to suit our specific needs, resulting in the CWAN AI Risk Management Framework. While the NIST framework casts a wide net, designed for general application across various industries, CWAN focused on our unique operational requirements, and refined the NIST principles into a more precise and applicable guide for our specific use cases. The customized framework is designed for real-world application, presenting practical examples and best practices tailored to CWAN’s operations. This customization embeds AI risk management deeply within CWAN’s existing governance structures, making it a core component of our overall risk strategy.

In essence, while the NIST framework provides a robust foundation, CWAN’s adaptation demonstrates the value of customizing such frameworks to fit specific organizational needs. The CWAN AI Risk Management Framework addresses the unique risks and intricacies of AI within the financial analytics services CWAN provides. CWAN’s framework emphasizes precision and efficiency, integrating AI applications effectively with existing processes and fostering a culture of innovation balanced with rigorous risk oversight. It’s a living document, evolving with the fast-paced changes in AI technology, ensuring CWAN remains agile and maintains a pioneering spirit at the forefront of AI development and risk management. This approach ensures that AI risk management is not just a checkbox exercise, but a meaningful process that adds tangible value to Clearwater Analytics’ AI initiatives. It places our customers protection at the forefront, ensuring they benefit from the highest level of security across all our AI systems.

Bridging management to end-users

Zoom image will be displayedCredit: DALL-E AI Image Generator, Prompt: illustration showing how an AI security framework connects management to end-users

Management relies on the framework to effectively manage operational risks associated with AI, aiding strategic decision-making and facilitating the integration of AI into our operations.

AI developers benefit from the framework because it offers them robust guidelines and industry best practices to drive ethical innovation and safeguard technological advancement.

Internal policymakers can use the framework to create policies that not only amplify AI’s potential but also prioritize security and its effects on the organization and employee relations.

End-users gain from the safer AI environments the framework contributes to, even if indirectly. The framework also makes AI risks and mitigations easier for employees to understand, enhancing their ability to work effectively and securely with AI systems. Moreover, it considers the interests of employees, customers, and the wider community, ensuring that AI progresses in a manner that is secure and trustworthy.

Translating AI risk management framework into real-world solutions

CWAN AI Risk Management Framework covers comprehensive measures to nurture a culture inclined towards effective risk management, understanding and diagnosing potential risks, and enforce stronger governance and due diligence. The framework provides comprehensive strategies aimed at creating organizational risk policies, establishing metrics to assess AI risk and trustworthiness, conducting AI risk training, and managing inventory for AI systems and models. It covers key activities such as mapping risks, documenting risk tolerance, and consulting with independent experts on AI assessments. Additionally, it addresses the management of third-party AI risks, prioritizes risk treatment, and develops effective procedures for responding to emerging and unforeseen risks.

Perhaps most notably, the CWAN framework places a strong emphasis on practical application. It’s rich with specific examples and best practices that are directly relevant to CWAN’s operations. This real-world orientation helps bridge the gap between theoretical risk management principles and their actual implementation in the company’s unique environment.

The framework incorporates tags to categorize and outline specific actions for developers. These tags, created and maintained by senior and principal engineers, cover a range of practices from model fine-tuning, system assessment, data protocols, privacy handling, to risk prioritization. These categorizations guide developers in implementing security measures and aligning with ethical standards.

Example: Customers seek a generative AI solution for producing personalized financial reporting and analytics. However, concerns arise regarding data handling, privacy and compliance with financial regulations.

By utilizing CWAN ‘s custom tags such as ‘Privacy Information Handling’ and ‘Data Access Protocols’, the team can filter for guidelines when starting to design a solution to account for potential security risks. These guidelines, which align with NIST’s AI RMF Playbook, include:

• Ensure alignment with CWAN’s data governance policies, particularly concerning the handling of sensitive data and associated risks.
• Implement standard technology risk management practices — such as procurement assessments, security measures, and data privacy controls — across all third-party technologies acquired.
• Evaluate third-party materials, including data and models, for potential risks related to bias, data privacy, and security vulnerabilities.
• Integrate CWAN’s impact assessments with applicable regulatory and legal standards to ensure compliance.
• Clearly outline the connections that the AI system or data will have with external networks, financial markets, and critical infrastructure, being mindful of potential negative impacts. Document these risks as part of evaluating overall risk thresholds and making informed decisions about deployment and decommissioning.
• Develop clear policies that specify the essential attributes to be monitored for models or systems, including documentation, source code references, incident response plans, data dictionaries, and contact details for AI-related personnel.

The CWAN AI Risk Management Framework integrates a security-first approach into every layer of organizational operations, providing CWAN with the tools needed to advance AI responsibly. This comprehensive framework aligns management strategies, development practices, regulatory policies, and end-user interactions with industry best practices. It fosters a secure and ethical AI environment, strengthening CWAN’s risk management methodology. By balancing robust guidelines with practical examples, the framework ensures that AI innovation proceeds securely, benefiting not just the company, but also its employees, and customers.

Essential takeaway

CWAN’s AI Risk Management Framework is more than a set of procedures; it’s a testament to CWAN’s dedication to operational excellence, ethical responsibility, and our customer-driven approach. As we navigate the complex interplay between AI innovation and risk management, we remain steadfast in our goal: to deliver superior financial analytics, underpinned by a foundation of unwavering security and trustworthiness.

About the author

William Dickerson is a Senior Security Engineer with a robust background in system and network engineering. With a passion for exploring new and emerging technologies, he is dedicated to understanding how these innovations integrate into the security ecosystem. Always eager to learn, William strives to enhance security practices and contribute to a safer digital landscape.