Best Practices and Tools for Navigating AI and IT Audit Risks

AI and IT Audit Resources Available for Download:

Digital Strategy Oversight Model

Strengthen governance and accelerate decision making with a strategic approach to digital strategy that balances innovation, risk and growth.

IT Performance Risk Key Performance Indicators (KPIs)

Identify, measure and mitigate IT risk with actionable KPIs to ensure your IT infrastructure meets performance, scalability and business continuity requirements.

New Technology Audit Risks Research: Cybersecurity and AI Represent Key Concerns

Address emerging technology risks for successful ESG implementation and discover how Protiviti can help your organization thrive.

Technology Change Management Audit Report

Transform your approach to technology change management with this sample audit report, providing guidance on identifying strengths and uncovering improvement opportunities.

The Next Phase: AI and Human Collaboration Powering Internal Audit Transformation

Look to AI technology enablement as a catalyst to empower CAEs and their teams to deliver stronger strategic oversight and value-added input for the business. 

Rethinking Technology Risk in the Age of AI

Artificial intelligence is reshaping how technology risk is managed, and internal audit functions are feeling the shift firsthand. Cybersecurity concerns, data governance questions and the rapid rollout of AI tools across the industry are converging in ways that test long-standing approaches to AI and IT audit.

Recent research shows that most technology audit leaders view AI as a growing risk over the next two to three years, even as many of these same leaders have already started using AI within their own departments. The organizations conducting technology audits more frequently tend to report better preparedness, while those auditing less often are more likely to face blind spots as AI adoption accelerates.

Companies need a clear way to evaluate AI-driven technology alongside traditional IT controls without treating either in isolation. Building that clarity starts with revisiting current best practices. From there, the right resources put those practices into action.

AI is both a risk and a resource and that dual reality is why modernized AI and IT audit standards matter more now than at any point in the past.

Best Practices

Strong AI and IT audit practices rest on a few consistent habits that include:

• Clear standards
• Disciplined change management
• Ongoing risk monitoring

Rather than treating AI oversight as a separate discipline, most effective audit functions fold it into their existing technology audit approach. This means updating the same procedures, checklists and reporting formats that already work well for traditional IT audits, so AI-related risks get evaluated with the same rigor as everything else.

Teams that neglect integrating AI into their internal controls often end up managing AI risk as an afterthought, layered on top of existing programs rather than built into them from the start.

Aligning Governance With Emerging Technology Risk

Governance frameworks that once focused narrowly on IT change management now need to stretch further, covering generative AI, machine learning and agentic AI tools operating across the business. That shift is prompting boards and audit committees to ask sharper questions about digital investments, including AI, and how those investments get evaluated and approved.

A clear digital strategy oversight process helps here. It gives directors and audit teams a shared vocabulary for weighing AI opportunities against the risks they introduce. Data privacy gaps and unclear accountability for automated decisions are common examples.

Internal audit functions that build this alignment early tend to catch control gaps sooner. They also avoid a common mistake: deploying AI tools before governance structures are ready to support them.

Updating Procedures for Continuous Risk Assessment

Traditional audit cycles were built around periodic reviews, but AI systems change faster than most annual audit plans can track. Updated AI and IT audit procedures build in checkpoints specifically for AI-related controls, rather than folding them into a single broad IT audit.

New Technology Audit Risks Research: Cybersecurity and AI Represent Key Concerns shows why this matters. Audit groups running six or more technology audits a year report fewer blind spots across most risk categories, while less frequent audits leave more risk unseen.

Meeting this bar means training audit teams to ask new kinds of questions, such as how an AI model was validated or who is accountable when an automated recommendation turns out to be wrong. Building these checks into existing procedures, instead of creating a parallel process, keeps audit work efficient and consistent.

Building an AI-Ready Audit Workforce

The Next Phase: AI and Human Collaboration Powering Internal Audit Transformation covers the people side of AI adoption. It calls for strong human oversight, with AI supporting auditor judgment rather than replacing it. Two practical steps stand out:

1. Run a skill inventory
2. Pilot AI use cases before scaling

Auditors should keep their focus on judgment and relationship work AI cannot do.

Toolbox

A handful of practical AI and IT audit tools can help internal audit and risk teams put these best practices into action. They cover a range of needs, from assessing change management maturity to strengthening board oversight of digital strategy.

Building these tools from scratch takes time most audit functions don't have to spare. Starting from an existing framework instead of a blank page lets teams focus their effort on adapting the details to their own environment, rather than reinventing the structure itself.

Assessing Technology Change Management Maturity

The Technology Change Management Audit Report offers a structured way to evaluate how well an organization manages changes to its IT systems. Built around a capability maturity model, this tool helps audit teams identify gaps in change approval, testing and documentation.

The report includes two samples:

• An enterprisewide review of technology change management practices
• A focused assessment of SAP change management, covering configuration controls and segregation of duties

Because it doubles as one of the more detailed AI and IT audit templates available, it also saves setup time for teams building their first change management audit program.

Measuring IT Performance Risk

The IT Performance Risk Key Performance Indicators (KPIs) breaks down how organizations can evaluate whether infrastructure is keeping pace with business demands. This includes the added computing and scalability requirements introduced by AI workloads.

The tool defines IT performance risk clearly, then walks through the business risks that can follow when systems fail to scale, from missed service-level agreements to costly emergency upgrades.

For teams incorporating AI into their technology stack, these KPIs offer a practical way to track whether new systems are performing as expected before problems reach end users. Benchmarking performance early also gives audit teams a baseline to reference later, making it easier to spot when an AI-driven system starts to drift from expected results.

Strengthening Board Oversight of Digital Strategy

The Digital Strategy Oversight Model gives boards and audit committees a framework for evaluating digital investment decisions, including the speed and scale of AI adoption.

It poses direct questions for directors to consider, such as whether AI, machine learning and other emerging technologies are being used to strengthen finance, IT and risk management functions. Because digitally mature organizations with coherent strategies tend to outperform their peers, this model helps boards treat digital and AI investment as a driver of value rather than simply a cost to manage.

Wrap Up

AI is no longer a future consideration for technology audit. It is already shaping how organizations approach risk today. Keeping pace means revisiting standards, tightening procedures and choosing the right resources to support both traditional IT audits and newer AI-specific reviews. It also means watching how AI and IT audit regulations evolve, since governments and industry bodies are still refining their expectations for AI oversight.

Getting started doesn't require an overhaul. Most audit functions can begin by mapping which existing procedures already touch AI-adjacent systems and building targeted checkpoints from there rather than launching a separate AI audit track.

Organizations that combine strong governance, frequent risk assessment and regulatory awareness are better positioned to manage AI confidently rather than reactively. This kind of preparation also gives internal audit a stronger seat at the table when leadership decides where and how quickly to expand AI use.