In 2026, assurance extends far beyond the financial statements.
Auditors are no longer just verifying numbers. They’re also validating the systems, data, and models behind them. AI oversight, cybersecurity, and continuous control testing are redefining the boundaries of audit and how firms deliver trust – not just test it.
Here are five recent trends in auditing to watch in 2026, each showing how trust, technology, and transparency will converge in the years ahead.
1. Continuous assurance and real-time auditing
The traditional year-end audit report is losing relevance. As organizations operate in real time across digital supply chains, cloud-based ERPs, and automated workflows, a single annual snapshot no longer keeps pace.
Audit trends show teams moving toward continuous assurance: ingesting live data, automating control testing, and surfacing insights in real time. Instead of explaining what went wrong months ago, auditors can now identify control failures as they happen.
Why it matters:
- 24/7 operations mean stakeholders expect immediate visibility and rolling assurance.
- Continuous auditing reduces surprises and enables faster, risk-based intervention.
- The auditor role evolves from compliance reporter to strategic advisor.
2. The rise of AI agents and intelligent automation
As continuous assurance scales, intelligent automation becomes the connective layer that makes it sustainable: bringing data, logic, and evidence together in real time.
AI in audit is shifting from assisted to autonomous. AI agents are self-directed systems capable of executing multi-step tasks, and they’re now handling document reviews, control testing, and even drafting preliminary conclusions.
Deloitte has identified agentic AI as a defining trend for 2026, with adoption rapidly scaling. These systems can reason through audit workflows while producing traceable evidence, version histories, and confidence scores for every action.
Why it matters:
- Productivity gains are exponential. Tasks that once took days and weeks can now be completed in hours.
- Auditors can focus on judgment and interpretation, not transaction checks.
- Governance becomes essential. To preserve trust, AI systems must produce outputs that are traceable, explainable, and auditable.
3. Cybersecurity as a core audit pillar
Cyber risk has evolved from an IT problem to a business continuity issue.
The Institute of Internal Auditors’ Risk in Focus 2026 report ranks cybersecurity as the top global concern, while regulators like the PCAOB and ISO embed digital-resilience criteria into audit guidance. According to AuditBoard’s 2025 Risk intelligence report, 40% of firms plan to expand their cybersecurity headcount.
A single breach can distort financial data, disrupt operations, and erode investor confidence. Many audit functions now test cloud configurations, access controls, and third-party exposures alongside financial controls.
Teams need confidence that the systems they use protect data with stringent controls. This principle underpins Trullion’s platform design: AI operates securely within a controlled environment, never training on client data and maintaining SOC II-level safeguards.
Why it matters:
- Data accuracy depends on security and resilience.
- AI introduces new risks that must be governed, not avoided.
4. Governance and auditing AI
As organizations increasingly adopt AI, auditors face a new question: who audits the AI algorithms?
Nearly 40% of AI leaders surveyed by Deloitte this year cited risk and compliance as top adoption challenges. AuditBoard’s 2025 Risk intelligence report found that fewer than 30% of leaders feel ready to meet upcoming AI governance standards.
In 2026, the audit boundary extends beyond financial statements into the systems that generate them. AI models must be governed by transparent, explainable oversight frameworks – verifying not just outputs, but the logic behind them.
Leading solutions in this space, like Trullion, are designed with built-in oversight: every AI output is reviewed and approved by practitioners, and users can clearly see how AI arrives at its conclusions. This keeps professional judgment at the center while aligning with enterprise governance standards like the AICPA Trust Services Criteria.
Why it matters:
- AI model decisions directly influence outputs, and fall squarely within the audit scope.
- Transparent, reviewable AI outputs preserve trust and compliance.
- Auditors need to develop new competencies in AI governance and model validation.
5. The audit talent shift
Behind every digital transformation is a human one.
Audit excellence still requires technical accounting, but fluency in data analytics, AI, and cybersecurity is now equally essential. Meanwhile, the pipeline is shrinking: the US accounting workforce has declined by more than 300,000 since 2020.
Leading firms are hiring “audit technologists,” embedding data science teams, and building continuous learning into their talent strategies. According to AuditBoard’s 2025 Risk intelligence report, 39% of firms plan to expand AI/ML skills.
Why it matters:
- The best auditors bridge data, judgment, and strategy – translating data anomalies into risk narratives, or interpreting AI logic.
- More than ever before, continuous learning is a core professional trait.
Looking ahead at audit transformation: The era of intelligent assurance
2026 audit trends will be defined by real-time insight and intelligent assurance, not after-the-fact verification. Firms that invest early in AI oversight, transparency, and digital fluency will set the new standard for trust and performance.
The next phase of audit transformation will depend on how effectively human expertise and intelligent systems work together. At Trullion, we’re focused on helping firms build this balance: enhancing accuracy, efficiency, and trust across every engagement. Book a personalized demo to see how AI-driven assurance can strengthen your audit workflows for the year ahead.