How AI can make your enterprise
IT audit-ready

Organizations conduct IT asset audits to achieve critical objectives, spanning the domains of finance, security, and regulatory compliance. However, growing IT complexity and evolving regulations make enterprise-wide audits increasingly difficult. Challenges like incomplete records, ghost assets, shadow IT, outdated systems, and fragmented data—combined with manual, time-intensive processes—undermine efficiency.

According to a YouGov survey, 47% of organizations exceed their planned time and budget by at least 10% during audits. Additionally, 62% of organizations indicated they will turn to automation to accelerate audit workflows. For many organizations, the time and effort needed to be audit-ready is in tension with budgetary and workforce constraints. Leveraging AI can significantly enhance audit readiness by improving accuracy, streamlining processes, and delivering deeper insights. Here’s how.

Pre-audit phase: Using AI to ensure IT asset register integrity

From reviewing asset records to reconciling data mismatches, IT teams often find themselves manually navigating multiple systems—IT asset management (ITAM) tools, unified endpoint management and security (UEMS) solutions, purchase records, enterprise resource planning (ERP) software, and financial systems—presenting challenges such as high manual overhead, errors, and time-consuming efforts. As a result, audit readiness is delayed, with increased risks of non-compliance and hidden security threats. By leveraging AI, IT teams can ensure data integrity in real time and proactively identify anomalies—saving time, reducing risk, and boosting audit confidence.

To begin with, predictive AI can identify issues like duplicate, outdated, or untracked assets by analyzing historical usage patterns and configuration data. This enables IT teams to resolve discrepancies proactively and ensure clean, audit-ready records.

AI agents, acting as an organization’s digital employees, can access asset information across IT solutions and help IT teams facilitate contextual actions to address discrepancies. Here, the "unreported asset identifier" agent can analyze data—such as location history and last scan timestamps—to flag assets that remain unreported. IT teams can further examine these details to mark them as stolen or misplaced. When stolen assets are identified, AI agents can initiate device wipe measures to safeguard enterprise data and mitigate potential security threats. This way, AI agents can act as IT teams' sidekicks, augmenting audit-readiness while saving significant time and effort.

Additionally, generative AI (GenAI) can support data consistency by normalizing variations in entries across diverse systems, such as consolidating entries with "Microsoft Office," "MS Office," and "Office Suite" tags to only use the "Microsoft Office" tag. Doing so will ensure clean, standardized records that improve reporting accuracy.

A "deep research" AI agent can analyze data across enterprise systems—such as ERP, finance, and ITAM tools—to detect inconsistencies in asset records. By correlating purchase orders, invoices, delivery notes, and inventory data, it can identify mismatches like assets marked as missing in ITAM systems that have actually been delivered. It can then flag these discrepancies for review or update records automatically, keeping the asset database accurate and
audit-ready.

Going beyond rule-based detection, predictive AI can correlate software behavior with usage patterns, while GenAI can consider the software's name and version to identify restricted software. Further, ITAM teams can review and remove such software across devices. This approach uncovers critical blind spots, ensuring better adherence to organizational policies while strengthening overall security.

Mid-audit phase: Leveraging AI agents to drive audits aligned with purpose, policy, and infrastructure

To streamline audit execution, AI agents can take the role of audit assistants, enabling organizations to navigate audits with ease. By interpreting regulatory mandates (such as HIPAA and ISO 27001) and referencing organizational policies, AI can accurately gauge audit expectations. It can also analyze IT infrastructure to identify potential compliance gaps and offer actionable recommendations. While undertaking internal audits, IT teams can capitalize on these recommendations and implement corrective actions, maximizing audit-readiness and compliance when faced with external audits.

For example, AI can detect laptops accessing ePHI across locations by correlating data from the configuration management database (CMDB). It flags unencrypted devices and, in compliance with HIPAA clause 164.312(a)(2)(iv), then recommends isolating and encrypting such devices.

To meet clause 164.308(a)(1)(ii)(D), AI can evaluate software usage patterns, identifying underutilized licensed software. It can suggest reallocation, cancellation, or cost-effective alternatives—backed by projected savings and a compliance rationale.

AI agents can further simplify the execution of audits by automating repetitive tasks such as sending follow-up reminders to users, parsing their email responses to verify asset possession, and updating the audit status accordingly. They can also mark known assets as audited by cross-verifying usage logs or CMDB entries, reducing manual effort while ensuring timely progress of the audit.

Hence, by uncovering compliance gaps and simplifying audit execution, AI empowers IT teams to fix issues, facilitating a stronger compliance posture before external audits happen.

Post-audit phase: Learning from key takeaways

In the post-audit phase, the focus shifts from detailed compliance checks to strategic reflection to drive lasting improvements. However, with audit data scattered across systems, manually compiling reports becomes time-consuming and error-prone. GenAI can simplify this process by generating tailored, user-friendly audit summaries—giving a clear snapshot of the overall audit outcome. Here, a post-audit report can highlight the key areas of non-compliance and the strong compliance zones, while indicating a composite compliance score. This enables IT teams to understand the overall severity and business impact of their current compliance posture.

To prevent recurring incidents, the audit report can highlight the causes of failures, identify areas for improvement, and enumerate an actionable roadmap to meet future compliance requirements.

For instance, after a HIPAA audit is completed, GenAI can summarize the areas of non-compliance—including lapses in hardware tracking and device encryption—while scoring this non-compliance as "high risk" based on the business criticality. GenAI can trace the failure to outdated device management policies and provide recommendations, from automating policy updates to streamlining the onboarding of unmanaged devices. To streamline implementation, it can prioritize these recommendations based on the risk score, enabling IT teams to fast-track crucial requirements. It can also produce visualizations to help stakeholders quickly grasp patterns and gaps.

Beyond reporting, IT leaders can engage with the system through natural language to explore deeper insights. For example, querying “Which asset categories are likely to face increased scrutiny under emerging compliance trends?” will generate a response in natural language rather than with data alone. This transforms audits from a reactive ordeal to a proactive and strategic exercise, helping organizations improve governance, close recurring gaps, and strengthen future audit preparedness.

Final thoughts

By embedding AI across every phase of the audit life cycle—pre, mid, and post—forward-thinking organizations can shift IT audits from reactive scrambles to strategic levers for continuous improvement. As regulatory demands and IT environments grow more complex, AI empowers enterprises to conduct smarter audits, reduce surprises, and stay aligned with evolving standards like HIPAA and ISO 27001.