Recent insights from multiple industry sources, including ISACA and AuditBoard discussions, highlight the rapid emergence of "Shadow AI" - the use of artificial intelligence tools by employees without formal governance, approval, or oversight.
This trend is accelerating in 2026, driven by the accessibility of generative AI tools and the pressure on organisations to improve productivity and innovation. However, it introduces a new category of technology risk that sits outside traditional control frameworks.
For IT audit and risk professionals, Shadow AI represents a significant shift.
Why Shadow AI Is Different
Unlike traditional shadow IT, which typically involved unsanctioned systems or applications, Shadow AI operates at a more subtle level.
Employees may:
- Upload sensitive data into external AI tools
- Generate business content without validation controls
- Use AI-generated outputs in decision-making processes
- Integrate AI tools into workflows without IT awareness
Because these activities often occur outside formal systems, they can bypass existing ITGC structures, particularly in areas such as access management, data governance, and change control.
Implications for ITGC and SOX Environments
Traditional IT General Controls are designed around known systems and defined processes. Shadow AI disrupts this model by introducing:
- Uncontrolled data flows outside governed environments
- Lack of audit trails and evidence for AI-driven decisions
- Potential breaches of data privacy and confidentiality requirements
- Increased risk of inaccurate or biased outputs influencing financial processes
From a SOX perspective, this raises important questions around reliance on system-generated information. If AI-generated outputs influence financial reporting or operational decisions, organisations must consider how these outputs are validated and controlled.
A Governance Gap Emerging
Recent commentary suggests that many organisations are still early in their response to Shadow AI, with governance frameworks lagging behind adoption.
Common gaps include:
- No clear policy on acceptable AI usage
- Limited visibility of AI tool adoption across business units
- Lack of defined ownership for AI risk
- Minimal integration of AI into existing risk and audit frameworks
For regulated organisations, this creates a growing exposure that is not yet fully addressed by traditional IT risk approaches.
What Should Senior Stakeholders Do?
From a practitioner perspective, the response should focus on extending existing governance frameworks, rather than creating entirely new ones.
Key priorities include:
- Expanding ITGC scope to consider AI usage and data flows
- Introducing AI governance policies and usage guidelines
- Enhancing monitoring and visibility over tool adoption
- Integrating AI risk into internal audit and risk assessments
Importantly, this is not about restricting innovation—but ensuring it operates within a controlled and auditable environment.
Closing Perspective
Shadow AI is a clear example of how technology adoption is outpacing governance frameworks.
For IT audit and risk leaders, the challenge is not just identifying the risk, but adapting existing control environments to address it effectively.
Organisations that move early to incorporate AI into their governance models will be better positioned to manage risk, support innovation, and maintain regulatory confidence in an increasingly AI-driven landscape.
