AI Risk Assessment: A Roadmap for Financial Institutions

AI has the potential to revolutionize how financial institutions operate, but like any new technology, it also introduces new risks. These range from biased algorithms affecting decision-making to potential data breaches involving sensitive customer information which is why it's important to conduct a comprehensive risk assessment that allows organizations to proactively manage and heed risks and safeguard operations.

In this guide, we’ll walk through the high-level steps financial institutions can utilize to identify, assess, and mitigate AI-related risks that include data privacy and information security as mentioned in NIST AI 600-1 (AI RMF).

Steps In an AI Risk Assessment

1. Preparation

Before embarking on an AI risk assessment, thorough preparation is essential. This involves setting a clear objective and scope for the assessment and selecting the appropriate risk model.

When conducting any risk assessment, a key step is to set clear objectives and define the scope. This helps determine the areas of focus and the depth of the evaluation. In the case of AI systems, it's essential to ask yourself which system will be assessed—whether it's an in-house solution or one sourced from a vendor—and what specific risks will be examined. For this blog, we’re focusing on data privacy and information security. Other risks, as outlined in the NIST AI 600-1, include issues like confabulation, harmful bias, homogenization, intellectual property concerns, and more. Finally, consider how these risks impact and support critical business processes, as this helps prioritize which systems are most important to address.

The second part of this step is deciding whether to use a quantitative or qualitative risk model. This might depend on the complexity of the AI system and available data, however, given that we primarily focus on data privacy and information security, controls can be directly associated with channels of exposure and risk, making it an easier quantitative model which is easier to comprehend, internalize and report. 

2. Conducting an AI Risk Assessment

Once the objectives are established, the AI risk assessment process can begin. This involves several critical steps to ensure all risks are properly identified, evaluated, and addressed.
First, catalog the AI systems in use, whether they are customer-facing chatbots, automated trading platforms, or other systems. Once you have a comprehensive list, the next step is to review the information assets these AI systems handle, such as customer financial data, proprietary algorithms, or other sensitive information that powers these systems.

With the data identified, assess the potential impact of non-compliance or security breaches. This includes evaluating the cost of exposure if sensitive data is compromised. You’ll need to determine both the likelihood of these risks occurring and their potential impact on your organization, including financial, reputational, and regulatory consequences.

The final step is to examine the existing security controls in place, such as encryption, access management, and monitoring tools. Review their effectiveness in mitigating the risks identified. Are they sufficient to protect the data and systems? Or do additional controls need to be implemented? This comprehensive evaluation will help ensure that all identified risks are addressed appropriately and that the AI system remains secure.

3. Communicate Risk Assessment and Approach

Begin by compiling a concise report that outlines the findings of the risk assessment. Detail the identified risks, their potential impacts, and the current controls in place. For each risk, assess its severity, likelihood, and consequences. This report forms the foundation for discussions on risk management and ensures stakeholders have a clear understanding of the risk landscape.

Next, present the results to relevant stakeholders, including leadership, compliance, IT, and other impacted departments. It’s key that the presentation clearly communicates risks and provides actionable insights - we find this step optimal if put in quantifiable terms

Based on the assessment, propose specific risk treatment strategies, such as updating security protocols or revising data governance practices. Each recommendation should include a rationale, expected outcome, and the level of risk reduction it will achieve.

Risk treatment strategies may include:

• Risk mitigation: Reduce risks through new controls like stronger encryption or stricter access management
• Risk transfer: Shift financial risk through insurance
• Risk avoidance: Eliminate risky AI applications or data practices
• Risk acceptance: Choose to accept certain manageable risks

Finally, create an action plan for implementing these strategies, including timelines, resource allocation, and mechanisms to monitor their effectiveness.

 4. Maintain Risk Assessment

Rather than viewing risk assessments as static reports, organizations must ensure they are consistently revisited and revised to stay aligned with the latest developments in technology and security.

Regular updates are key to keeping the risk assessment relevant. As AI systems grow more complex and new functionalities, data sources, or algorithms are introduced, these changes can introduce new vulnerabilities. To stay ahead of these potential risks, it’s important to routinely evaluate the systems and data being used, ensuring that the risk assessment reflects these updates.

Periodic reviews also play a critical role in the ongoing effectiveness of the risk management process. These reviews—whether conducted quarterly, semi-annually, or annually—allow organizations to reassess their AI systems, examine whether previously identified risks remain relevant, and identify new risks that may have emerged. 

Finally, it’s essential to remain adaptive as new AI technologies and threats emerge. As AI capabilities expand—whether through machine learning, natural language processing, or other advancements—new vulnerabilities and risks will inevitably arise. The risk assessment process must be flexible enough to incorporate these innovations and their associatded risks. 

Rival’s AI Risk Assessment

Does conducting an AI risk assessment feel overwhelming? Leverage Rival’s platform and team of experts to ensure you get it right. Our comprehensive methodology ensures a thorough risk assessment that not only addresses your current needs but will provide a foundation for future assessments.

With Rivial Security, you're not just getting software to guide you through the process. We deliver precise, quantifiable risk measurements that give you clear insights into your AI systems. Plus, we help you prioritize actions based on your organization’s goals and highest ROI, ensuring you get maximum value from your risk management strategy.

Ready to take the next step? Schedule a call below to get started.

Are you starting out your AI compliance journey? Check out our AI information security policy, this free resource offers clear, actionable guidelines, designed with the latest and best practices to ensure your institution remains secure and compliant.