Top Risk Areas for Internal Audit Across Industries in 2026

As organizations prepare for 2026, internal audit must adapt to a more complex and connected risk landscape. Advances in technology, workforce changes, digital transformation, and new regulations are reshaping how risks arise and interact. Internal audit’s role as an independent, forward-looking adviser is more vital than ever.

Five areas of risk that require increased focus from internal audit include: 

• Cybersecurity: Threat actors are using AI and adaptive tactics to target vulnerabilities. Internal audit should elevate its role from control testing to strategic oversight to review effectiveness and alignment with resilience goals.
• AI governance and ethics: With new state regulations on the horizon, weak AI governance poses compliance and reputational risks. Internal audit teams should assess whether controls and oversight are properly built into AI development and deployment.
• Talent and workforce capability: Skill gaps in critical areas can disrupt execution and innovation. Internal audit should evaluate workforce planning, third-party reliance, and alignment with strategic needs.
• Third-party and concentration risk: Growing reliance on vendors brings systemic exposure. Internal audit should assess the strength of third-party risk frameworks and visibility into vendor ecosystems.
• Data integrity: As digital reliance grows, trustworthy data becomes even more essential. Internal audit should examine governance of data quality, traceability, and accountability.

Internal audit plans that address these areas of risk can strengthen resilience, safeguard stakeholder value, and support long-term strategy.

Cybersecurity

Cybersecurity has ranked high on organizational risk registers for more than a decade. December 2025 will mark 12 years since the Target breach, one of the first major, publicly reported cyber incidents. Since then, information security has become a critical element of enterprise risk management that demands sustained investment. Internal audit has followed suit.

Today’s cyberthreats look vastly different from those in 2013. Now they are: 

• Adaptive and tailored to each organization
• Powered by advanced tools, including AI
• Focused on weak links like employees and third parties 

The COVID-19 pandemic further reshaped risk exposure. Remote work, mobile access, outsourcing, and cloud adoption have expanded the attack surface, which requires ongoing evolution in security programs.

As threats grow more sophisticated, internal audit will encounter greater complexity. Teams must assess whether security efforts are effective and responsive to rapid change.

Heading into 2026, internal audit teams should consider a top-down approach to cybersecurity. Historically, internal audit focused primarily on control-level testing, patches, terminations, and pen tests. But in today’s environment, strategic alignment and cybersecurity vision might require equal scrutiny.

Next steps for internal auditors 

• Strategy alignment: Evaluate how cybersecurity strategy supports enterprise growth and resilience.
• Emerging threats: Test preparedness for AI-driven and quantum-enabled cyberattacks.
• Identity and access: Review identity governance, remote access, and third-party integration controls.
• Cloud and SaaS security: Assess controls over cloud platforms and software as a service (SaaS) configurations, data protection, and vendor reliance.
• Incident response: Evaluate the speed and effectiveness of detection, escalation, and recovery, especially for ransomware.
• Governance and reporting: Review how cyber metrics and threat intelligence are communicated to leadership and the board.
• Third-party risk: Test cybersecurity oversight for key vendors, including high-risk suppliers. 

AI governance and ethics

AI continues to expand rapidly across sectors, from finance to healthcare and logistics. While enabling innovation and efficiency, this growth also increases systemic risks. Most organizations still lack strong, enforceable AI governance, which leaves them vulnerable to regulatory, reputational, and operational risks – areas in which internal audit can add critical value.

AI adoption often outpaces ethical safeguards such as explainability, data quality, bias mitigation, and accountability. Without proper oversight, AI systems can produce harmful outcomes, including discrimination or operational failures, without clarity on accountability.

By 2026, regulations such as the European Union’s AI Act and many U.S. state laws are expected to be enforceable, and the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF), and the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) 42001:2023 standards will continue to be used as benchmarks. These frameworks highlight impact assessments, audits, incident reporting, and governance by design, and they create new compliance requirements and opportunities for internal audit to assess readiness.

Multidimensional risk

Gaps in AI governance represent multidimensional risk for organizations, including:

• Compliance risk: New AI laws require documentation, audit trails, and risk classification. Noncompliance might result in fines or enforcement.
• Operational risk: Use of closed-box models or unvetted application programming interfaces (APIs) in critical processes increases failure potential.
• Reputational risk: Biased or flawed outputs can damage trust and credibility.
• Strategic risk: Suboptimal governance limits competitiveness, especially in sectors sensitive to regulations or environmental, social, and governance concerns. 

Why internal audit must step in

Internal audit can serve as an independent reviewer of AI governance by assessing: 

• Integration of AI risks into enterprise risk management (ERM)
• Documentation of impact assessments
• Oversight of third-party AI solutions
• Controls for fairness, explainability, and robustness 

Embedding governance across the AI life cycle, from design to monitoring, can help organizations move from reactive responses to proactive risk management.

Next steps for internal auditors 

• Governance framework: Assess design and implementation against NIST AI RMF, ISO 42001, and ISACA standards.
• Impact assessments: Review fairness, explainability, privacy, and bias controls in high-risk AI use cases.
• Third-party AI risk: Evaluate model validation, data lineage, and security protocols of external AI providers.
• ERM integration: Embed AI risks in enterprise risk and compliance frameworks with clear accountability.
• Model life cycle controls: Examine governance from model design through deployment, monitoring, and retirement.
• Ethics and oversight: Evaluate alignment with ethical standards, transparency, and human oversight.
• Regulatory readiness: Assess preparedness for EU AI Act, state-level laws, and global regulatory trends.
• Bias and incident monitoring: Review mechanisms for detecting, reporting, and remediating AI-driven errors or bias. 

Talent and workforce capability

Heading into 2026, talent scarcity and workforce capability gaps remain significant risks. Advances in AI, cybersecurity, data science, and sustainability continue to outpace the availability of skilled professionals. This trend is not a short-term labor issue; it’s a structural shift driven by demographics, evolving expectations, and increasing role complexity.

Skill shortages affect resilience, delay initiatives, strain operations, and heighten reliance on technology or third-party providers. Workforce readiness is essential to long-term viability. This risk extends beyond internal teams. As organizations outsource cloud and digital services, talent gaps among vendors pose additional exposure. For example, a provider’s lack of cybersecurity expertise can threaten data protection and business continuity. Oversight of internal and third-party workforce capacity is increasingly vital.

Internal audit can assess whether workforce strategies align with business goals and if succession planning, talent analytics, and vendor governance address capability risks. Key areas to evaluate include:

• Workforce planning for critical skill gaps
• Third-party frameworks that address provider talent risk
• Governance for managing skill gaps in technology adoption 

As generative AI and automation expand, internal expertise becomes more critical. Without it, implementation might outpace governance and controls. Internal audit can evaluate whether readiness is keeping pace with digital change.

Demographic shifts and geopolitical trends continue to affect labor supply. Organizations should embed workforce resilience into ERM through risk assessments, strategic skills mapping, and long-term talent partnerships.

Internal audit plays a key role in highlighting workforce risks and fostering board-level dialogue. Treating talent as a strategic asset and a risk is essential to future resilience.

Next steps for internal auditors 

• Workforce resilience: Assess strategies for closing skill gaps in areas such as AI, cybersecurity, and data analytics.
• Third-party labor risk: Review vendor workforce capacity and risks tied to labor shortages.
• Succession planning: Evaluate leadership pipelines and backup plans for key roles.
• Technology readiness: Confirm training and readiness for automation, AI, and digital tools.
• Talent forecasting: Assess use of workforce analytics for predicting needs and guiding planning.
• DEI readiness: Evaluate diversity, equity, and inclusion (DEI) initiatives as drivers of resilience and innovation.
• Training and reskilling: Review learning programs and knowledge transfer aligned with future needs.
• Workforce risk integration: Check that talent-related risks are embedded in ERM and operational frameworks.
• Geopolitical and demographic risk: Assess exposure to regional labor trends and political disruptions.
• Hiring practices: Evaluate recruiting and onboarding processes for fairness, efficiency, and alignment with strategic workforce goals. 

Third-party and concentration risk

As organizations rely more heavily on external providers, internal audit can play a key role in identifying and managing the systemic risks tied to third-party and concentration dependencies.

Many critical functions, data hosting, payment processing, logistics, and customer platforms are now outsourced. While outsourcing boosts efficiency and access to expertise, it also introduces risk. A single vendor failure, security event, or geopolitical disruption can ripple across the enterprise. The question is no longer whether to use third parties but how well those relationships are governed. As reliance deepens, the margin for error narrows.

Concentration risk is often overlooked until disruption occurs. One vendor might support multiple systems or rely on the same subcontractors, such as Amazon Web Services^™ or Microsoft Azure^™. Such overlaps can amplify the impact of a single failure.

Traditional risk assessments can miss these interdependencies. Without a consolidated view of third-, fourth-, and nth-party exposure, organizations might underestimate how quickly external events can escalate.

Though onboarding diligence is typically strong, ongoing monitoring is often fragmented. Risk data might be siloed, which makes it difficult to spot vendor distress or compliance lapses, limits visibility, and slows response. Effective third-party risk management (TPRM) requires ongoing, integrated oversight – something many organizations still lack.

Internal audit can bring independent insight regarding this challenge. By evaluating TPRM design and execution, internal audit teams can help organizations shift from reactive risk management to a more strategic, enterprise-level approach.

Next steps for internal auditors  

• TPRM governance: Assess oversight processes for managing third-party relationships.
• Concentration risk: Identify dependencies on critical vendors, locations, or shared infrastructure, such as cloud services.
• Monitoring and reporting: Review the quality of vendor risk data, dashboards, and escalation protocols.
• TPRM maturity: Benchmark practices against industry standards to highlight improvement areas.
• Fourth-party dependencies: Evaluate visibility into subcontractors and extended vendor chains.
• Continuity planning: Assess how third-party risks are incorporated into business continuity plans.
• Contracts and service-level agreements: Review contracts for service levels, data security, and compliance terms.
• Exit and transition readiness: Test preparedness for transitioning away from critical vendors.
• Cyber and data protection: Evaluate vendor adherence to cybersecurity and privacy requirements, including cross-border data handling. 

Data integrity

Data integrity is not just an IT issue; it’s a core enterprise risk. As organizations digitize operations, adopt AI, and join complex data ecosystems, data volume and velocity have surged. These advancements support faster decisions but also increase exposure if data is not actively governed. AI and digital transformation are reshaping how data is used, which makes integrity essential to strategy and resilience.

An expanding threat landscape

Data integrity is threatened by an expanding threat landscape, including: 

• Intentional manipulation, such as cyberattacks and insider threats
• Human error, including data entry mistakes, misconfigurations, inconsistent procedures
• System failures, such as outages and API breakdowns
• Low-quality inputs, including flawed data in AI systems that can lead to biased or unreliable outcomes 

These risks are worsened by fragmented ownership, inconsistent governance, and poor visibility into data flows. Without effective governance and audit trails, problems might go undetected until damage is done.

Traditional governance can’t keep pace. Organizations need technology-enabled frameworks that support transparency, accountability, and control at scale.

Strategic implications

By 2026, poor data integrity could erode trust, impair decisions, and threaten long-term viability. As reliance on predictive tools and real-time data grows, high-quality data becomes essential for compliance, performance, and competitive edge. Regulatory pressure is also increasing, especially in data-intensive industries such as finance, healthcare, and infrastructure.

Next steps for internal auditors 

• Data governance and life cycle: Evaluate policies, ownership, and controls that support data integrity throughout its life cycle.
• Critical data flows: Assess accuracy and completeness of inputs, transformations, and outputs in core systems.
• AI data integrity: Review training data for AI and machine learning models and focus on bias, quality, and traceability.
• Regulatory compliance: Check adherence to data governance, privacy, and transparency regulations.
• Data quality and validation: Test reliability of sources, validation routines, and reconciliation processes.
• Stewardship and accountability: Evaluate clarity of roles for maintaining data accuracy and ownership.
• System interfaces and integration: Review integrity of data transfers across platforms, APIs, and pipelines.
• Incident response and recovery: Assess capabilities to detect, respond to, and recover from data loss or corruption.
• MDM frameworks: Examine master data management (MDM) frameworks for consistency and control across systems. 

Looking ahead

As 2026 approaches, internal audit functions that embrace innovation, strengthen collaboration across the business, and cultivate talent can better navigate uncertainty and help organizations be ready for what’s next. The ability to see risk as something to be mitigated as well as an opportunity to shape smarter decisions and sustainable growth is critical. By staying agile and curious, internal audit can help organizations move confidently into the future.

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