Fast-Growing Potential and Possibilities with AI Use Cases

Teams who embrace AI’s early potential and begin integrating it into their work will be better positioned to make the most of AI’s promise as it develops. 

Reducing the Regulatory Compliance Burden

The pace of regulatory and legislative change continues to accelerate across cybersecurity, data privacy, and environmental, social, and governance (ESG). New compliance requirements also emerge from entry into new markets, spinning off new business units, and other business changes. Audit, risk, and compliance teams have vital roles to play in helping their organizations understand the new requirements, their likely impact, and how to implement them. AI can help reduce this burden by:

• Expediting adoption of new compliance frameworks. Organizations implementing new frameworks need to create evidence requests mapped to the new framework requirements. AI can quickly locate existing requests that may be relevant to the new requirement. If appropriate, existing requests can be connected to new requirements, or leveraged to accelerate the process of authoring new evidence requests. 
• Reducing noncompliance risk. AI can help monitor current regulatory compliance obligations, identifying gaps and issues to reduce the risk of noncompliance or fines.
• Horizon scanning and monitoring. AI can be leveraged to monitor regulatory and legislative activity to help detect and provide early warning of emerging compliance risks. 

Proactively Surfacing Data-Driven Insights

Simple visibility into your data is not enough. As your organization grows, your data grows — and without the tools to surface what may be applicable in specific contexts, that data often goes unnoticed and unused. In  today’s highly volatile risk landscape, it is increasingly important for your organization to connect the dots between the data being created and draw on those connections to surface insights and accelerate outcomes. Whether you are looking to obtain maximal coverage in your compliance program, identify similar risks and issues that exist across your business units, or reduce duplication in your enterprise risk management (ERM) program, AI-driven insight can be a powerful enabler in helping you connect the dots.

Understanding and Solving the Data Problem

The data your organization creates across its audit, risk, and compliance programs — from controls, issues, and policies to risks or evidence requests — is largely unstructured. While organizations do try to label, tag, and segment their data, the majority of the data created quickly recedes into a database. As a result, data can quickly become hard to find, or its existence may be unknown when the data is needed. 

AI solutions have the power to collect, categorize, search, and analyze your data in new ways, working behind the scenes to process millions of data points to surface insights that may otherwise be inaccessible. These solutions are designed to help you effectively mine and extract value from your organization’s data and the work generated by your teams, providing you with insights and just-in-time connections that save you time and help you build a more risk-aware and resilient organization. AI solutions can also offer other key benefits, including helping to drive competitive advantage and protect the organization from risk (e.g., non-compliance, reputational damage, financial losses). Example use cases include:

• Continuously identifying risks, trends, potential blind spots, and predictive insights, helping teams make more informed decisions about where to focus efforts and get a true picture of threats and opportunities across the organization.
• Increasing coverage across larger datasets (e.g., more full population testing).
• Creating data visualizations that help to convey key risks and insights. 
• Detecting fraud, anomalies, and suspicious patterns.
• Detecting patterns of over- or under-testing of controls. 
• Detecting potential cybersecurity threats, breaches, and impacts.
• Detecting and reducing errors and irregularities.
• Uncovering opportunities for improving processes and workflows.

Different AI technologies execute this work in different ways. As with all generative AI outputs, however, review and verification of AI-driven insights and recommendations are key in leveraging this technology securely. Human insight, judgment, and experience will always be critical in building on AI’s outputs to develop relevant, actionable recommendations and make informed decisions about strategies and next steps.