Developing an AI Risk Management Framework

Artificial intelligence is here—and it’s here to stay. 

AI is expected to have an estimated 21% net increase in the US GDP by 2030, so it’s no wonder that nearly 80% of companies are either actively using AI or are in the process of incorporating AI into their everyday operations.  

However, just like every groundbreaking technology before it, AI must have guardrails. We’ve already seen example after example—in a relatively short amount of time—of AI use leading to serious ethical, legal, and security issues, not to mention poor AI use leading to layoffs. 

This is part one of a two-part blog series on the importance of an AI Risk Management Framework and AI governance, along with tips to help you develop, implement, and enforce them.  

Understanding AI Risks 

AI is rapidly transforming our world and how we work in it, bringing advancements in many industries. However, this transformative power comes hand-in-hand with risks. Like any tool, AI must be deployed responsibly, ensuring its benefits outweigh potential downsides.  

What are some of those concerns surrounding AI? Let's explore these areas, and in doing so, we can develop effective mitigation strategies and pave the way for responsible use of AI. 

• Operational Risks: Imagine an AI-powered stock trading system making a bad call, leading to massive financial losses. Or a self-driving car encountering an unexpected situation and causing an accident. These are operational risks, where AI systems malfunction, produce inaccurate outputs or behave in unforeseen ways. Mitigating these risks involves rigorous testing, clear limitations on AI decision-making, and robust fallback plans.
  

• Security Risks: AI systems are goldmines of data and complex algorithms. Hackers could target them to steal sensitive information, manipulate AI outputs for malicious purposes, or even disrupt critical infrastructure controlled by AI. Strong cybersecurity measures, secure data storage, and vulnerability assessments are crucial to minimize these risks.
  

• Ethical Risks: AI algorithms are only as good as the data they're trained on. Biased data can lead to biased AI, resulting in unfair or discriminatory outcomes. For example, an AI-powered resume screener might inadvertently favor certain qualifications, disadvantaging otherwise qualified candidates. To mitigate ethical risks, developers should ensure diverse training datasets, implement fairness checks, and prioritize transparency in how AI arrives at its decisions.
  

• Regulatory Risks: The legal landscape around AI is constantly evolving. Regulations might address data privacy, liability in AI-related accidents, or even the use of autonomous weapons. Staying up-to-date on regulations and ensuring AI development complies with them is essential to avoid legal roadblocks and ensure responsible AI use.
  

The Importance of an AI Risk Management Framework

AI offers a powerful tool, but like any powerful tool, it needs careful handling. An AI Risk Management Framework (AI RMF) acts as your roadmap, helping you navigate potential pitfalls and maximize the benefits of AI. How can an AI RMF empower your organization?

• Protects what’s most important: AI systems often handle sensitive data and make critical decisions. An AI RMF helps identify and mitigate potential security vulnerabilities, safeguard data assets, and help prevent costly breaches.
  

• Compliance compass: The legal and ethical landscape surrounding AI is constantly evolving. An AI RMF ensures your AI development and deployment comply with current regulations and best practices—but only if you stay in the loop on laws and industry regulations. This adds legal protection for you and your organization and fosters responsible AI use.
  

• Building trust—the cornerstone of success: Customers and stakeholders are increasingly scrutinizing how organizations handle AI. An AI RMF demonstrates your commitment to transparent, ethical AI development. 
  

• From reactive to proactive: Without a framework, you might only react to AI risks after they cause problems. An AI RMF promotes a proactive approach. By systematically identifying and addressing potential issues early on, you can make informed decisions and achieve your strategic goals more confidently.
  

An AI RMF isn't only about mitigating risks. It's about unlocking AI's full potential, while ensuring its responsible development and use. 

NIST AI Risk Management Framework  

The National Institute of Standards and Technology (NIST) has developed an AI RMF to help guide organizations in managing AI risks. Released in January 2023, the NIST AI RMF “...was developed through a consensus-driven, open, transparent, and collaborative process…intended to build on, align with, and support AI risk management efforts by others.” 

In addition to its AI RMF Framework, NIST provides a companion playbook, roadmap, crosswalk, and various perspectives. In March of last year, it launched the Trustworthy and Responsible AI Resource Center, which facilitates implementing and aligning with the AI RMF.  

Long story short: AI Risk Management is a big deal, and NIST—the federal agency tasked with promoting technology-use standards across the United States—has spent an incredible amount of time and resources to help organizations develop a framework for implementing AI safely and intelligently. 

We encourage you to spend some time reading the above materials from NIST, but here are the key elements in the AI RMF: 

• Governance: Establishing an AI governance structure.
• Mapping: Identifying and categorizing AI systems and associated risks.
• Measuring: Evaluating and measuring AI risks.
• Managing: Implementing strategies to mitigate identified risks.

Your organization is unique, and although the NIST AI RMF provides a comprehensive approach to AI risk management when developing your AI governance, it is up to you to ensure your AI governance ensures trust, safety, and ethical use of artificial intelligence.  

Tips for Developing an AI Risk Management Framework for Your Business 

An AI RMF is the foundation of responsible AI development and deployment in your business. It empowers organizations to harness the power of AI while minimizing potential downsides. See below for some tips to guide you in developing a robust and effective AI RMF: 

• Assemble a Cross-Functional Team: AI is complex, and a successful RMF requires diverse perspectives. Put together a team with representatives from IT, legal, compliance, and business units. This collaborative approach should help build a well-rounded framework that addresses all aspects of AI risk.
  

• Establish Clear Policies and Procedures: A strong AI risk management framework doesn't exist in a vacuum. Develop clear policies and procedures that define roles, responsibilities, and processes for managing AI risks. This includes outlining the AI development lifecycle, risk assessment methodologies, and incident response protocols. Clear documentation ensures everyone involved in AI projects understands their part and how to navigate potential issues.
  

• Conduct Regular Audits and Assessments: AI—both what it does, and how it is used—is constantly changing, and so should your AI RMF. Schedule regular audits and assessments of your AI systems. This proactive approach helps identify new risks that may emerge over time and will give you the opportunity to adapt your framework and mitigation strategies accordingly.
  

• Implement Strong Data Governance: Data is the lifeblood of AI. Good data entering an AI system generally means good results, and the reverse is also true. Ensure strong data governance practices are in place for better data quality, security, and privacy. This involves implementing data collection and storage protocols, access controls, and bias detection techniques. High-quality data is essential for building trustworthy AI systems, and strong data governance helps mitigate risks associated with biased or compromised data.
  

• Invest in Training and Awareness: Empower your employees to actively participate in responsible AI development. Invest in training programs that educate them on AI risks, best practices, and ethical considerations. A well-informed workforce can flag potential issues early and contribute to a culture of responsible AI use within your organization.
  

It takes time and effort, but you can—and should—develop an AI RMF that fosters trust, mitigates risks, and unlocks AI's full potential for your organization. 

Establishing an AI Risk Management Framework is the First Step in AI Implementation 

AI is here—and it's here to stay. Before leaping feet first into leveraging AI in your business, you must develop an AI Risk Management Framework. This is essential for safeguarding your AI initiatives and your data.  

By understanding the various risks and implementing a structured framework, you can harness the power of AI responsibly and effectively. In part two of this blog, we will explore AI governance in detail, providing you with some ideas needed to govern AI in your organization.

At HBS, we want your organization to reach its full potential with AI. Our team offers expert guidance and support in AI risk management, AI development and implementation, and AI security. 

We specialize in helping organizations in developing technology frameworks, strategies, and governance. AI is no different. 

Reach out now to learn how HBS can assist you with all your AI-related needs.