As AI tools become more accessible, employees are adopting them without formal approval from IT or security teams. These tools may increase productivity, automate tasks, and fill gaps in existing workflows, but they also operate behind the scenes of security teams, bypassing their controls and creating new blind spots for so-called shadow AI. Although similar to the phenomenon of Shadow IT, Shadow AI goes beyond unauthorized software by involving systems that process, generate, and potentially retain sensitive data. The result is risk categories that most organizations do not yet have the ability to manage: uncontrolled data exposure, expanded attack surfaces, and weakened identity security.
Why is shadow AI gaining popularity so quickly?
Shadow AI is rapidly expanding across organizations because it is easy to deploy, immediately useful, and largely unregulated. Unlike traditional enterprise software, most AI tools require little or no setup, so employees can start using them right away. According to a 2024 Salesforce study, 55% of employees reported using AI tools that have not been approved by their organization. Many organizations do not have clear AI usage policies, leaving employees to decide for themselves which tools to use and how to use them, without understanding the security implications.
Employees may use generative AI tools like ChatGPT and Claude in their daily workflows, which can increase productivity but potentially share sensitive data externally without monitoring. Whether the AI vendor uses that data to train models depends on the platform and account type, but in all cases, the data is beyond your organization’s security perimeter.
At a departmental level, shadow AI can occur when teams integrate AI APIs or third-party models into applications without formal security reviews. These integrations can expose internal data and introduce new attack vectors that security teams cannot see or control. Rather than trying to eliminate shadow AI completely, organizations should proactively manage the risks that shadow AI creates.
How Shadow AI is a Security Problem
Although shadow AI is often seen as a governance issue, it is essentially a security issue. Unlike traditional shadow IT, where employees deploy unauthorized software, shadow AI involves systems that actively process and store data beyond the scope of security teams, turning unauthorized AI use into a broader risk of data breaches and access abuse.
Shadow AI can lead to untraceable data breaches
Employees can share customer data, financial information, or internal business documents with AI tools to complete tasks more efficiently. Developers troubleshooting code can accidentally paste scripts that contain hardcoded API keys, database credentials, or access tokens, unknowingly exposing sensitive credentials. Once data reaches a third-party AI platform, organizations lose visibility into how it is being stored or used. As a result, data can leave an organization without an audit trail, making breaches difficult, if not impossible, to track and contain. Under GDPR and HIPAA, this type of uncontrolled data transfer may be a reportable violation.
Shadow AI rapidly expands attack surface
All AI tools create new potential attack vectors for cybercriminals. If unapproved tools are adopted without oversight, they can include unvetted APIs and plugins that are unsafe or malicious. Employees who access AI platforms through personal accounts or devices keep their activity completely outside the organization’s security controls and cannot be seen by traditional network monitoring. The risks become even more acute as organizations begin deploying AI agents that operate autonomously within workflows. These systems interact with multiple applications and platforms, creating complex and largely hidden pathways that cybercriminals can exploit.
Shadow AI bypasses traditional security controls
Traditional security controls were not built to accommodate today’s use of AI. Most AI platforms operate over HTTPS. This means that standard firewall rules and network monitoring cannot inspect the content of these interactions without SSL inspection in place. This control is not in place in many organizations. Conversational AI interfaces also don’t behave like traditional applications, making it difficult for security tools to monitor and log their activity. This allows data to be shared with external AI systems without triggering alerts.
Shadow AI impacts identity security
Shadow AI poses significant challenges to Identity and Access Management (IAM). For example, if employees create multiple accounts across AI platforms, their identities can become fragmented and unmanaged. Developers also use service accounts to connect AI tools to systems and create non-human identifiers (NHI) without proper oversight. When organizations lack centralized governance, these identities can be poorly monitored and difficult to manage throughout their lifecycle, increasing the risk of unauthorized access and long-term exposure.
How organizations can reduce the risk of shadow AI
As AI becomes more integrated into daily workflows, organizations should aim to reduce risk while enabling safe and productive use. As a result, security teams must move from blocking AI tools outright to controlling how they are used in the workplace with a focus on visibility and user behavior. Organizations can reduce the risk of shadow AI by following these steps:
Establish clear AI usage policies: Define which AI tools are allowed and what data can be shared. Security policies need to be easy to understand and intuitive, as overly restrictive rules will only encourage employees to use unapproved tools. Provide approved AI alternatives: If employees don’t have access to useful tools, they’re more likely to find their own tools. Providing an approved, secure AI solution that meets your organization’s standards reduces the need for shadow AI. Increase visibility into AI usage patterns: Complete visibility is not always possible, but organizations should monitor network traffic, privileged access, and API activity to better understand how employees are using AI. Educate employees about the security risks of AI: Many employees focus solely on the productivity benefits of AI tools, rather than the security risks. Providing training on the safe use of AI and data handling can significantly reduce unintended exposure.
Benefits of effectively managing shadow AI
Organizations that proactively manage shadow AI gain greater control over how AI is used across their environments. Effectively managing shadow AI provides several benefits:
Gain complete visibility into which AI tools are being used and what data they are accessing Reduce regulatory risks under frameworks such as GDPR, HIPAA, and EU AI legislation Deploy AI faster and more securely with vetted tools and thorough guidelines Accelerate adoption of approved AI tools and reduce reliance on unsafe alternatives
Security needs to consider shadow AI
AI in the workplace is becoming the norm, and employees will continue to demand tools that help them work faster. Given how easy it is to access AI tools and how usage policies rarely keep up with adoption, some degree of shadow AI adoption is inevitable in large organizations. Rather than trying to block AI tools completely, organizations should focus on enabling them to be used securely by increasing visibility into AI activity and ensuring that both human and machine identities are properly managed.
Keeper® directly supports this approach, allowing organizations to control privileged access to the systems their AI tools interact with, enforce least-privilege access for all identities, including human users and AI agents, and maintain a complete audit trail of activity across critical infrastructure. As AI agents become more prevalent in enterprise workflows, managing the identities and access paths they rely on becomes just as important as managing the tools themselves.
Note: This article was thoughtfully written and contributed by Keeper Security content writer Ashley D’Andrea for our readers.
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