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Why a risk management framework is critical for AI initiatives
Risk management frameworks are an essential tool to optimize the planning, deployment and use of AI. Without one, AI initiatives might be vulnerable to several threats.
Growth in AI use has been significant in the past year alone. But despite its known benefits, AI comes with business risks organizations might not be ready for.
Identifying and mitigating those risks presents major challenges. Fortunately, standards and frameworks are available to provide risk guidance during AI deployment.
The principal reasons for using an AI risk management framework are to identify, assess, and mitigate potential risks when deploying AI systems. Such frameworks help ensure the responsible development and deployment of AI technologies.
Properly used, AI risk management frameworks deliver important benefits to senior management, technology professionals, business unit leaders and employees.
There are several frameworks available to organizations, but this article primarily references the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework (AI RMF) to examine how frameworks align AI initiatives with ethical standards, regulatory requirements and organizational goals.
Key elements of an AI risk management framework
An AI RMF identifies, monitors and manages risks in an AI-based system. It does this by addressing governance, ethics, operations, compliance and regulatory issues.
As demonstrated by leading AI risk frameworks, such as the NIST AI RMF, several key activities must be included.
Key activities in an AI RMF include the following:
- Identifying and categorizing risk. When using an RMF, organizations must first identify potential AI-based risks, threats and vulnerabilities. These can include threats to security, system operations, data governance, compliance and ethics.
- Establishing governance. Frameworks should establish governance activities, including the use of policies, AI lifecycle management, rules for accountability, and how AI supports business objectives and regulatory mandates.
- Risk management. Risk frameworks help identify risks, their likelihood of occurring and their effect on the enterprise. Key activities include performing risk assessments and establishing risk registers.
- Targeting top risks for mitigation. Once risks are identified, organizations must make sure they are working to mitigate potential risks to the enterprise. Methods for addressing risk events might include access controls, data validation, bias detection, and analysis of data models and algorithms.
- Continuous performance monitoring. Throughout the AI lifecycle, organizations must continuously monitor system performance to detect any anomalies and potential compliance violations.
- Compliance with standards and regulations. The number of standards and regulations governing AI activity continues to grow, and with that growth is the increased need to demonstrate compliance. Risk frameworks can help ensure that compliance is a primary outcome.
When is it important to use an AI RMF?
Following are examples of situations where using an AI RMF can benefit the enterprise.
When ensuring regulatory compliance
AI RMFs include guidance on complying with specific standards and regulations. AI technology streamlines the collection and processing of data that can demonstrate compliance, reducing the likelihood of violations that might result in fines or litigation.
When protecting the brand
Frameworks can specify how AI protects the brand from reputational damage, increasing consumer confidence and trust.
When reducing financial losses
Use of frameworks ensures that AI systems are properly configured and deployed, can identify potential disruptions, and have controls that highlight fraud and incorrect decisions, which can have financial implications.
When improving security and operational resilience
RMF guidance can help improve security measures, such as strong access controls, threat modeling and incident response. Using an AI RMF can improve resilience by identifying ways to effectively recover from and adapt to future risk events.
When reducing vendor and third-party risks
Assuming an enterprise decides to buy an AI product from a vendor or other third party rather than build one, a risk management framework can help lower risks by providing evidence supporting (and challenging) vendor claims, assessing potential risks from a vendor product, and evaluating case studies of other enterprises.
When establishing a risk-focused AI culture
By setting expectations for AI teams and supporting research and innovation, a RMF encourages the development of a risk-based AI culture.
When strengthening senior leadership support
By providing guidance on reporting, AI performance metrics, use of dashboards, defining accountability, and other benefits, a RMF helps deliver better governance and executive oversight.
Paul Kirvan, FBCI, CISA, is an independent consultant and technical writer with more than 35 years of experience in business continuity, disaster recovery, resilience, cybersecurity, GRC, telecom and technical writing.
