Why Audit is Entering its Biggest Transformation in 30 Years

When I began my career in IT Risk at Citibank, audits reflected the pace of the businesses they were evaluating. Technology environments changed more slowly, infrastructure was largely on-premises, and evidence was collected through binders, spreadsheets, email and shared folders. Auditors spent days interviewing control owners, selecting samples, tracking requests manually, and reviewing environments that often looked very similar at the end of an audit as they did at the beginning.

The principles behind the work have not changed. Independent assurance, professional skepticism, sound methodology, and accountability remain the foundation of a quality audit.

Learn more: Everyone Is Talking About AI in Audit. I Think We're Asking the Wrong Question.

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The organizations we audit, however, have changed dramatically. Companies today operate in cloud environments that evolve continuously. They rely on hundreds of software providers, distributed workforces, complex data flows, third-party dependencies, and increasingly, artificial intelligence. In an era when infrastructure can be deployed in hours and business processes can change overnight, an organization's risk profile can shift significantly within a single audit period.

The pace of business has accelerated, but the way organizations approach assurance has not always evolved at the same speed – and this is why I believe the audit profession is entering its biggest transformation in more than 30 years.

Most conversations about this transformation focus on AI, and while it’s certainly an important catalyst, I believe the larger movement is about how organizations manage risk, and how assurance is delivered in a world where change is continuous.

Learn more: The Future of Audit is AI-Powered – But Must be Human-Led

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Assurance was long viewed as a largely periodic activity. Organizations prepared for an audit, gathered evidence, completed testing, received an independent opinion, and repeated the process the following year. That model reflected the way businesses operated at the time.

Today’s organizations need a different model, for a variety of reasons: 

• Security teams are managing environments that change constantly
• Enterprise customers expect greater visibility into vendor risk
• Boards want a clearer understanding of cybersecurity posture
• Regulators are expanding expectations around governance, operational resilience, third-party oversight, and AI risk management

In short, as risk becomes more dynamic, assurance must evolve alongside it.

I see the industry moving toward a model of continuous validation – not in the sense that an independent audit opinion is issued every day, but in the sense that organizations have greater visibility into the effectiveness of their control environment throughout the year. The annual audit will remain an essential component of independent assurance. However, it will increasingly become the culmination of ongoing risk management activities rather than a once-a-year exercise to prepare for an audit.

AI is accelerating this evolution from both directions, because while most discussions focus on how AI will change the audit process, AI is also changing how organizations maintain and manage their control environments. Security and compliance teams are beginning to use AI to automate evidence collection, monitor changes, identify gaps, improve documentation, and answer questions about policies and controls. These capabilities can reduce administrative burden and help organizations maintain stronger governance throughout the year rather than relying on manual preparation before an audit begins.

Not every organization will adopt AI at the same pace, but the objective isn’t technology adoption for its own sake. It’s about maintaining a stronger, more effective control environment.

The same evolution is happening across the broader assurance landscape. Penetration testing, security assessments, and other validation services are also being transformed by AI. This enables security professionals to analyze larger attack surfaces, prioritize findings more effectively, reduce repetitive work, and spend more time understanding business risk and helping organizations remediate issues.

However, the value of these technologies depends on the expertise and methodology behind them. AI can identify patterns and accelerate analysis. It cannot independently determine whether a risk is material, whether a finding requires deeper investigation, or what actions will have the greatest impact on improving security posture. Those decisions require experienced professionals applying judgment and technical expertise.

This is why I believe the next generation of assurance firms will be AI-native. It’s not about adding AI to isolated steps of an engagement or introducing another productivity feature, but embedding intelligence throughout the entire assurance lifecycle – from scoping and evidence collection to technical validation, testing, review, collaboration, and reporting.

An AI-native assurance firm can reduce administrative friction, improve consistency, connect information across engagements, and allow experienced professionals to spend more time focused on the areas where judgment matters most.

Historically, many assurance activities have operated independently. Audits, penetration tests, risk assessments, and compliance reviews were often performed as separate engagements, with valuable insights from one activity rarely informing another.

AI creates an opportunity to connect these activities. For example, a penetration test may identify a technical weakness. That insight can inform risk assessments, influence control testing, guide remediation efforts, and provide better context for future assurance activities. Rather than viewing each engagement as a standalone event, organizations can develop a more complete understanding of their security posture over time.

This does not diminish the independence of each engagement. It strengthens it by providing better context, more relevant information, and more informed professional judgment. Experienced auditors and security professionals still determine the appropriate scope of an engagement. They evaluate whether evidence is sufficient. They assess whether controls are designed effectively and operating as intended. They challenge assumptions, interpret exceptions, and understand the broader business context behind the data.

The best assurance providers create value because they help organizations understand risk, strengthen security programs, and make better decisions. A high-quality audit or security assessment should leave an organization stronger than when it began. It should provide clarity around risk, improve governance, strengthen operational discipline, and give leadership greater confidence in the effectiveness of their control environment.

The firms that define the next decade won’t just use AI to deliver today's services more efficiently. They will combine AI-native technology, rigorous methodology, independent judgment, and deep technical expertise to deliver a new model of assurance, which is more connected, more continuous, and more valuable to the organizations that rely on it.

Every generation of business has required the assurance profession to evolve. The move from paper records to digital systems changed how evidence was collected. Cloud computing changed what organizations needed to protect. AI is changing both how companies manage risk and how assurance providers evaluate it. The purpose of assurance, however, remains the same.

Organizations need independent validation they can rely on, boards need confidence that risks are being managed appropriately, and most importantly, customers need evidence that the companies they depend on are operating responsibly. 

The future of assurance will belong to the firms that can combine technology with expertise by using AI to enhance what professionals do best: applying judgment, understanding risk, and helping organizations build stronger security programs.

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