When enterprise data slips into shadow AI tools

The disconnect in perceptions regarding AI usage policies

Executives often believe they’ve built strong, clear policies around how artificial intelligence is used inside their companies. Yet most employees don’t agree. Over half of professionals surveyed in Okta’s latest report said their organization’s AI policies were unclear, hard to find, or didn’t exist at all. That means people inside the same company are operating with entirely different understandings of the rules.

This gap is about communication. AI governance can’t work if only top leadership understands it. Policies need to be accessible, visible, and easy for everyone to follow. When employees don’t know what’s permitted, they make their own choices. In many cases, those decisions expose the company to unnecessary risk.

Business leaders need to think beyond compliance checklists. AI clarity should be treated as a core element of trust and culture. Executives must align messaging, tools, and training so that every employee, regardless of technical background, knows how to work with AI safely. The real task is not writing more rules but ensuring everyone understands and embraces them. That clarity builds accountability and strengthens overall security posture.

U.S.-based employees are increasingly turning to unsanctioned AI tools to boost productivity

Two-thirds of U.S. employees are now using unapproved AI tools at work, and nearly a quarter rely on them regularly, according to Okta’s research. Employees want faster ways to solve problems, meet deadlines, and extend their creative reach. When company-provided AI systems can’t keep up, they look elsewhere.

This growing use of “shadow AI” reflects both enthusiasm and frustration. Most employees see generative AI as a tool that accelerates progress. The issue is that without official oversight, data security and compliance take a back seat. Files shared with external AI platforms can easily end up outside company control, creating risks that leadership may not even be aware of.

For executives, this is a signal. When staff rely on unapproved tools, they’re telling you that the official tools aren’t meeting their needs. Instead of cracking down, decision-makers should ask how internal systems and policies can evolve to support real-world workflows. By activating sanctioned, secure AI solutions that deliver the flexibility employees want, leaders can rechannel that energy into innovation that remains within the bounds of governance and security.

While shadow AI is primarily used with positive intent, its unregulated status creates hidden security risks

Most employees using unapproved AI tools have good intentions. They’re problem-solvers who want to meet deadlines and push projects forward. But even good intentions can cause serious exposure. Once sensitive or proprietary data enters an external AI tool, the organization loses visibility and control. Few employees understand how long their input data is stored, who can access it, or how it may be reused.

Nigel Peri, a security expert, explained that these risks are not caused by malice but by a lack of visibility, governance, and consistent security controls. That means the threat lies in the unknown, companies don’t know what data has been shared, where it’s stored, or what it’s connected to. When employees experiment without guardrails, even one misstep can create a long-term vulnerability.

Executives should treat shadow AI as a governance issue, not an employee conduct problem. Proper awareness training, regular audits, and a clear catalog of authorized tools reduce the risk created by unregulated AI use. Business leaders must ensure their teams understand not just what is allowed but why controls exist. This approach strengthens cultural responsibility around AI, closing the gap between innovation and security.

Enterprises should adopt a collaborative approach with employees

Heavy restrictions won’t stop employees from seeking tools that make their work easier. Instead, companies should channel that drive through structured collaboration. Leaders need to engage employees directly to understand where productivity gaps exist and what functionality teams are seeking from AI solutions. That insight forms the foundation for developing governance frameworks that balance innovation and risk management.

Secure test environments, or AI sandboxes, allow employees to experiment safely while IT and security teams maintain oversight. This ensures that innovation continues under controlled conditions, preserving both creativity and compliance. A governance model built jointly with employees also builds trust and transparency, which strengthens adoption and enforcement across the organization.

For business leaders, this approach turns AI governance into an enabler rather than a barrier. By creating secure spaces and clear policies that support exploration, executives encourage responsible innovation. Effective frameworks not only prevent data exposure but also establish a culture where employees view governance as a shared commitment, not a limitation. This alignment is essential for scaling AI safely and productively across the enterprise.

Continuous evaluation and enhanced visibility of AI tools are critical for robust enterprise security

AI systems inside companies evolve quickly, often faster than governance frameworks can keep up. That speed creates gaps in oversight, where invisible or outdated access permissions can lead to breaches or operational disruptions. Continuous evaluation of every AI agent, what it does, what data it touches, and what permissions it holds, is now an essential part of digital security practices. It’s not enough to set policies once; they must be reviewed and refreshed regularly to keep pace with the rapid integration of new AI tools into enterprise systems.

Nigel Peri emphasized this urgency, warning that many technology leaders have an illusion of control over their AI environments. He explained that organizations should constantly ask what agents exist, what they can access, and what they’ve been authorized to do. Without that clarity, even a well-intentioned AI deployment can become a point of weakness in the company’s infrastructure.

For executives, this means investment in visibility tools and governance processes is not optional, it’s foundational. A modern enterprise can’t maintain control over AI-driven operations without knowing precisely where and how these systems interact with corporate data. Regular audits, permission tracking, and dynamic monitoring form the baseline for a secure, scalable AI environment. Business leaders should promote internal transparency as a core value, ensuring everyone, from developers to decision-makers, shares responsibility for sustaining the organization’s digital integrity.

Key highlights

• Close the policy perception gap: Executives must align with employees on AI policy clarity. Simplifying access to guidelines and strengthening communication reduces confusion and minimizes security risks from inconsistent understanding.
• Address productivity-driven shadow AI use: Employees turn to unapproved AI tools when official options fall short. Leaders should invest in sanctioned tools that match employee needs to keep innovation secure and compliant.
• Recognize the hidden risk in good intentions: Shadow AI adoption often stems from problem-solving. Executives should improve visibility and governance to protect data integrity while enabling safe experimentation.
• Build governance through collaboration: Company-wide AI frameworks are more effective when developed with direct employee input. Creating secure testing environments keeps innovation active while ensuring accountability and data protection.
• Maintain continuous visibility and evaluation: Leaders need constant oversight of AI tools, access permissions, and data flows. Regular audits and transparent governance should be treated as core to sustainable enterprise security.