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. This philosophy is also driving AuditBoard’s integration of AI, purpose-built to help teams start using AI to derive more value from their data. Below, we explore potential use cases in more detail.
Reducing the Learning Curve
AI has the capacity to serve as a contextual assistant for professionals of any experience level in organizations of any size or industry. By reducing the learning curve for new domains and tasks requiring localized knowledge, AI can help both experienced and inexperienced professionals provide more strategic value to their organizations. Enhanced performance offers vital relief for short-staffed or resource-strapped teams.
Saving Time and Effort on Writing and Research
Writing is a challenging but critical function in the day-to-day work of audit, risk, and compliance teams. While practitioners spend an inordinate amount of time drafting risk descriptions and security policies, they would prefer to have more time for the more strategic parts of their jobs. What if you could reduce the time you spend on writing and research by 25% or even 50%? Generative AI holds the potential to transform the way you work, saving significant time and effort by accelerating:
- Research. AI can provide users with ready access to enormous bodies of knowledge, enabling you to more effectively leverage the work your team has already done (e.g., developing policies, writing control language) in developing new documents, strategies, testing activities, risks, policies, controls, and so on.
- Initial drafting. Generative AI tools can give practitioners a head start in crafting detailed short- and long-form documents. For example, AuditBoard AI embeds levers to help users source and tailor text in useful ways (e.g., integrating risk tolerance considerations, regional or linguistic preferences, and contexts.)
- Issue authoring. If users are documenting new risk or control issues, AI can search for similar issues and provide for users’ review, offering context-relevant starting points they can use to jumpstart their drafts.
Streamlining Operations and Improving Efficacy
AI tools can help audit, risk, and compliance teams to more efficiently and effectively achieve desired outcomes in countless ways. For example, AuditBoard AI helps to:
- Support effective evidence reuse. Audit, risk, and compliance teams are often unaware of evidence collected by partner teams, leading to redundant evidence collection efforts. AI can enable the identification and reuse of evidence already collected.
- Find, connect, and document risk or control issues. When users are documenting new risk or control issues, AI can quickly surface similar issues. Existing issues can be connected with new control tests or risk issues if appropriate. Alternatively, if issues are similar but distinct, AI can help users leverage existing issues as starting points.
- Improve cross-organizational alignment and collaboration. Instead of disparate stakeholders managing multiple action plans across duplicate issues, AI can enable identification and connection of efforts across the organization. This enables audit, risk, and compliance teams to collaborate to define a single action plan and manage it through to resolution.
- Enable a more risk-aware culture across the organization. AI tools make it easier for front-line leaders to participate in day-to-day risk management (e.g., reporting and documenting new risks, maintaining ongoing visibility on risks directly impacting their roles). As engagement increases, awareness rises.
Reducing the Regulatory Compliance Burden
The pace of regulatory and legislative change continues to accelerate across areas such as 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 be able to connect the dots between the data being created, as well as 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.
Download AI’s Bright Future in Audit, Risk and Compliance: Exploring AI’s Transformative Impact, Use Cases, and Risks to connect data across functions, uncover trends as they happen, and proactively surface actionable issues, risks, and insights.
