Incorporating AI in Your Risk Management Strategy

The use of artificial intelligence technology in risk management is simultaneously changing both discipline and practice. The biggest shift involves moving both static and reactive, backward-looking controls to predictive and autonomous systems that reside within an organization’s governance, risk and compliance (GRC) functions. Additionally, AI technologies are being increasingly used to automate tasks, speed up incident response, and predict potential risks and impacts.

The Role of AI in Risk Management

The role of AI in risk management is to apply technologies like machine learning, data analytics and automation to detect, assess and mitigate risks across domains. With its capability to analyze large data sets, identify trends within these data sets, and forecast potential threats, AI enhances both decision making and risk mitigation. AI serves several functions within risk management:

• Real-time monitoring—The capability to continuously monitor risk factors and provide updates.
• Data analysis, detection, and recognition—AI can analyze data with speed and accuracy. As data is being analyzed, the technology can detect anomalies and recognize patterns.
• Decision support and prediction—AI can provide data-driven recommendations, offering decision makers an analysis of risk scenarios.
• Predictive capabilities—By leveraging models, AI can predict potential risks and their impact, allowing organizations to implement preemptive measures.

Emerging Risk Management AI Technology Trends

Below are several of the most significant emerging risk management artificial intelligence technology trends.

Predictive Risk Modeling and Analytics

Predictive risk modeling uses historical incidents as well as operational and external data in statistical models to estimate how likely specific risks are to occur, as well as any potential impact. Rather than using a point-in-time heat map, these models provide dynamic risk scores that act as early warning signals.

In practice, these models help risk management professionals and senior leadership to prioritize resources for mitigation projects. For example, predictive models can rank vendors on the probability of disruption or business units on potential compliance failures.

Additionally, global banks use predictive AI to help process transactions, preventing fraud. These same banks leverage predictive AI to help process credit and loan applications using historical data to determine creditworthiness.

Generative and Agentic AI Assistants

AI assistants are acting more like valued team members, helping teams to interpret regulations, assess risk, monitor controls, and even mitigating risks under human supervision.

Generative AI uses large language models to read, summarize and generate risk management-related content from policies, regulations and audit-related information. Agentic AI can go further, linking tasks together and interacting with systems to perform actions. For example, agentic AI can (1) assess a risk, (2) draft a response, and (3) notify team members working with those systems that perform these tasks.

The trend of the use of agentic AI in risk management is to act as an agent that is always calculating exposure and watching for both internal and external changes and events that can prompt action, rather than waiting for a committee review. This will move risk management closer to navigating risks, as opposed to relying on a point-in-time risk register.

Real-Time Anomaly and Fraud Detection

Quickly becoming a core component within the risk management practice, this AI capability can scan large amounts of live data and immediately act on threats. This is particularly helpful in financial services, technology services, and to assist those individuals who monitor operations or are in cybersecurity roles.

Within these AI tools, AI models train on normalized or baseline data, which they can then compare to live data, such as transactions, account login events, and the flow of different data types to detect anomalies and fraud.

This real-time capability is helping to shift periodic, sample-based reviews into continuous monitoring. Moreover, AI models continuously learn from new data, improving their ability to detect over time, bringing adaptive learning into the risk management practice.

AI Risk Management Regulation

AI risk management is governed by both regional laws and by widely adopted frameworks. Both laws and frameworks aim to ensure that all AI systems are developed and used safely, ethically and legally.

European Union Artificial Intelligence Act

The world’s first regulatory framework for AI, the EU AI Act, places binding rules on higher-risk AI systems. It applies broadly to any provider or user of AI systems within the EU, and even to those outside of the EU if the output of the AI system is used within the EU.

The EU AI regulation categorizes AI systems into four risk categories:

• Minimal risk—Most AI applications currently fall under this category, with examples being email and spam filters, as well as AI-enabled video games. These types of applications face no obligations.
• Limited risk—AI applications that fall in this category can pose transparency risks, and they must inform users that they are interacting with AI. Examples of these applications include chatbots, where users must be made aware they are talking with an AI application, and AI-generated or altered videos, pictures or audio must also be labelled as AI.
• High risk—AI applications and systems that could negatively impact health, safety and human rights fall within this category. These include applications and systems used in critical infrastructure, education and training, law enforcement, and medical devices.
• Unacceptable risk—AI systems that pose a threat to human rights are banned. Examples of this AI technology include AI applications that manipulate human behavior, real-time biometric identification in public spaces performed by law enforcement (except with judicial approval), and AI that recognizes emotion used in the workplace or educational settings.

NIST AI Risk Management Framework (RMF)

Developed by the U.S. National Institute of Standards and Technology (NIST), the NIST AI RMF is a voluntary framework that is considered a global set of best practices. The framework contains four core functions that can be applied at any point in the design and usage of any AI system:

• Govern—This function, designed to provide leadership oversight, involves defining roles and responsibilities, as well as establishing policies, procedures and accountability.
• Map—This function involves understanding the context in which the AI system will be used. Meant to be determined prior to any implementation, it includes identifying the purpose, capabilities, benefits and impacts to all relevant stakeholders.
• Measure—Measuring the AI system means testing the system to identify risks as well as evaluate performance, fairness indicators and security vulnerabilities. Additionally, during this phase, monitoring thresholds are established.
• Manage—In the manage function, practitioners prioritize all identified risks and implement strategies and actions to mitigate. This includes deployment and operating decisions based on the balancing of risks vs. benefits.

AI Risk Management Templates

There are several helpful, reusable templates to manage risks associated with AI systems and applications. While static, templates can offer teams the ability to have a valuable discussion regarding risks and to create a baseline. Below are several to consider incorporating into your program.

AI risk assessment template—This template can be in spreadsheet form or any type of table. Start by listing each AI application, and add the following descriptor columns:

• Business use case
• Data sensitivity
• Identified risks
• Likelihood
• Impact
• Mitigation strategy

This simple type of template is a very good starting point in any AI risk assessment.

While not a specific template, the NIST AI RFM Playbook is the primary companion guide to the NIST AI RFM framework. It includes a breakdown of the four core functions and offers documentation examples that can serve as templates.

AI Risk Management Best Practices

Whether you are already using artificial intelligence technology in your risk management practice or are just exploring its use, below are several best practices to consider.

Integrating Explainable AI (XAI) Into Your Organization

Explainable AI applies methods and techniques to AI models, turning them from “black boxes” into transparent tools that allow risk management and governance teams to understand and audit AI-driven decisions.

Several ways in which XAI is being used include:

• Using XAI outputs to document why a score, alert, or recommendation was produced, creating a rationale.
• For regulated and higher-risk use cases, restricting model use to only those that can be explained, such as linear models, or by applying SHAP techniques.

Incorporate a Responsible AI Strategy

Responsible AI describes how organizations deploy AI tools and applications. Organizations that use AI responsibly are focused on fairness and mitigating any bias. To help build responsibility, consider creating a governance board or council to provide guidance and oversight. Empower the council with the following abilities:

• Develop and monitor the use of guidelines for AI development and usage.
• Establish a consistent decision-making process for ethical questions.
• Designate named owners who are responsible for each element of an AI tool.

Learn more about artificial intelligence in the risk management practice by exploring these related resources on KnowledgeLeader:

• Topic Guide: Artificial Intelligence
• Best Practices of Internal Audit Innovators – Technology Enablement
• AI Pulse Survey, Vol. 3: From Automation to Autonomy