Artificial intelligence (AI) has quickly become an indispensable tool for organizations looking to optimize operations, identify hidden risks and gain actionable insights from vast amounts of data. For IT auditors, and for auditors in general, AI (and related technologies such as Large Language Models, Natural Language Processing and AI Agents) offers tremendous value. This includes areas such as automation of audit processes, risk identification, controls testing, continuous auditing, and more.
As the technology matures, it has become progressively easier for auditors to leverage AI responsibly and effectively. ISACA recently put forward the ISACA Advanced in AI Audit (AAIA) credential to allow auditors to layer AI expertise into their auditing skill sets. Both ISACA and The Institute of Internal Auditors (IIA) emphasize the importance of innovation and continuous improvement in auditing. ISACA’s frameworks (such as COBIT 2019) and the IIA’s International Professional Practices Framework (IPPF) remind auditors to stay current with emerging technologies.
Below are five practical ways in which AI can fit into the IT audit landscape, supported by real-world examples and aligned to established audit standards.
1. Risk Analysis and Predictive Analytics
Traditional risk assessments often involve manual review of business processes and controls. With AI-based predictive analytics and process mining, auditors can systematically analyze vast quantities of operational and financial data to identify latent risks and trends. By creating models that recognize past patterns of fraud, system failures, or control breakdowns, auditors can better forecast potential vulnerabilities. Many large institutions, such as those in financial, insurance and telecom industries, already employ AI-driven tools to predict system outages, potential credit defaults or fraud, allowing internal audit teams to focus on these high-risk areas ahead of the audit cycle.
By leveraging AI, the scope and depth of risk analysis can increase exponentially. IT auditors can integrate such analytics into their planning phase, enabling them to prioritize the areas of highest risk and allocate resources effectively.
2. Automated Evidence Collection
Collecting audit evidence is a fundamental audit procedure but can be time-consuming and still not free from human errors. AI tools can streamline this by intelligently extracting relevant data and documentation from multiple sources (e.g., system logs, transactional records, emails, and policy documents).
For example, a multinational manufacturing company implemented a natural language processing (NLP) solution to categorize procurement-related emails, invoices and contract documents, which drastically reduced the time internal audit spent on gathering and organizing information.
When well-designed and thoroughly tested, AI-driven evidence collection can help auditors ensure quality and timeliness of information. AI-powered solutions not only expedite the preliminary phase of an audit but also reduce errors that could undermine audit quality.
3. Intelligent Controls Testing
Controls testing has traditionally involved selecting samples and manually verifying whether processes and safeguards are working as intended. AI enables a more comprehensive approach. By analyzing entire data sets in near real-time, AI can flag unusual transactions or configurations that may indicate control failures.
AI-powered controls testing can provide deeper assurance than sampling alone. With AI, audits can extend beyond the manual checks to continuous monitoring and testing (further discussed below). This approach not only boosts the thoroughness of an audit but also empowers auditors to deliver insights on control effectiveness more proactively.
AI-powered control testing can go further by analyzing entire workflows of process, time consumed at each stage/step, and the diversions or exceptions, known as process mining. This can help auditors identify patterns and trends in different transactions and the related workflows and efficiency of the process and internal controls, such as for procurement processes.
4. Continuous Assurance (Continuous Auditing and Control Monitoring)
Continuous Assurance (also known as Continuous Auditing) has been on the internal audit roadmap for years, but AI accelerates and refines this concept. By integrating AI models into existing IT systems, data warehouses and big data platforms, auditors can receive live or near-real-time updates on anomalies or deviations from established controls. These alerts can trigger deeper investigations or immediate remediation efforts. A global logistics provider, for example, leveraged AI-enabled dashboards that continuously monitored shipping transactions against policy thresholds, reducing the time to detect potential compliance breaches.
By embedding AI-driven rules into continuous auditing frameworks, IT auditors can offer more timely insights, minimize risk exposure, and enhance the control environment.
5. Automation
AI and related technologies such as Large Language Models (LLM) and Natural Language Processing (NLP) capabilities can be used to automate internal audit process and achieve service excellence for stakeholders. Some of the practical examples are:
- NLP based chatbots/communication channels: Auditees and stakeholders can interact with these channels 24/7. These capabilities can be used to provide awareness on internal audit polices and process. By integrating adequate levels of authentication and authorization, these channels can also be used to disseminate information such as recommendation status, follow-ups, KPIs related to the auditee addressing recommendations, etc.
- Avatars can be used to automate the traditional opening meeting, freeing up auditors’ time and enhancing auditee experiences.
Striking the Right Balance of Technology and Governance
As AI continues to evolve, it will undoubtedly play an even greater role in helping not just IT auditors, but auditors across all domains, to perform more robust, timely and insightful reviews. Auditors adopting AI should remember that success depends on balancing cutting-edge technology with robust governance, clear objectives and a thorough understanding of audit standards, such as those from ISACA framework COBIT 2019 and the IIA’s IPPF.
From predictive risk analysis to continuous controls assurance, AI can serve as a capability multiplier—enabling audit functions to offer deeper insights, reduce manual processes, optimize operational and resource efficiency, and respond at the speed of risks, quickly and effectively.
