What Is Shadow AI?

Origins of the Term “Shadow AI”

The phrase “shadow AI” emerged alongside the broader concept of shadow information technology (shadow IT), which describes any unsanctioned tech adoption in a company. In that sense, “shadow AI” fits neatly under this umbrella: employing AI solutions outside the awareness of official oversight. Experts have drawn parallels to employees installing unsanctioned software on their machines, resulting in heightened compliance issues.

Over time, “shadow artificial intelligence” has come to represent more than just hidden algorithms and unapproved AI systems. It spotlights the tendency for well-meaning innovators to circumvent established processes, typically to resolve a problem in real time or boost productivity. The result can be new efficiencies—even breakthroughs—but at the cost of potential security risks and limited visibility and control.

Common Examples of Shadow AI

Organizations may not realize how common shadow AI can be. In many cases, employees or entire departments turn to these hidden solutions in search of a faster route to solving challenges:

• Unapproved predictive analytics tools: Some teams deploy AI tool plug-ins to forecast customer trends without informing IT.
• Surreptitious chatbot implementations: Department heads might experiment with self-built or free chatbot services, unknowingly exposing sensitive information.
• Personal data analysis spreadsheets: Eager employees harness advanced AI macros in standard spreadsheets, ignoring established access controls.
• Unvetted cloud services: Within shadow IT refers to many employees uploading data and running new AI routines on external platforms without formal approval.

Risks and Challenges Associated with Shadow AI

While it can be tempting to adopt hidden AI systems, there are notable threats organizations must address. From data leakage to unauthorized system access, these pitfalls can be detrimental if not identified promptly:

• Potential data breach: Unapproved AI projects can inadvertently expose personally identifiable information (PII), payment card industry (PCI) data or protected health information (PHI).
• Compliance issues: Violations of data privacy regulations and legal risks can result from unregulated data collection.
• Security measure gaps: Disconnected or unknown solutions typically ignore standard protocols, heightening the risks of shadow usage.
• Data management confusion: Handling large volumes of information through an unsanctioned AI application can muddy ownership and hamper effective information security efforts.

Compliance and Regulatory Risks

Shadow AI introduces new compliance challenges by bypassing established controls for responsible technology use. When employees deploy unapproved AI tools, they often sidestep essential checks outlined in frameworks like the NIST AI Risk Management Framework, the EU AI Act, or broader data privacy laws. This lack of visibility can lead to violations, increase the chances of audit findings, and expose organizations to steep fines or inquiries about inadequate oversight.

With AI regulations evolving rapidly, organizations cannot afford shadow AI slipping through the cracks. Noncompliance can undermine client and partner trust, especially if unregulated tools misuse sensitive data or produce biased results. Repeated incidents driven by poor visibility and policy misalignment could spark a cascade of regulatory actions, transforming innovation into a persistent compliance risk for the business.

Steps to Identify Shadow AI in Your Organization

Recognizing the presence of hidden AI practices can be challenging. Nevertheless, there are actionable ways to bring the situation to light:

1. Engage in comprehensive audits: Check for unauthorized cloud services or AI software, mapping every tool and integration.
2. Continuously monitor your environment: Use real-time analytics to detect anomalies in data collection and usage patterns.
3. Conduct employee interviews: Speak with teams and individuals to uncover unapproved experimentation or hidden AI applications.
4. Review access logs: Correlate user privileges and access controls to ensure no unsanctioned connections exist.

Tools and Techniques for Detection

Once you suspect risks associated with shadow AI might affect your operations, advanced detection methods go a long way:

• Automated discovery solutions: Specialized software tools scan networks for unregistered AI systems.
• Endpoint security agents: Lightweight solutions identify any shadow information technology running on devices.
• Centralized logging and SIEM: Collect logs across your ecosystem to reveal patterns consistent with shadow IT or AI misuse.
• Vulnerability assessments: Routine scans help highlight misconfigurations in new or existing AI deployments.
• Data security posture management (DSPM): Maps and monitors sensitive data, quickly spotting any exposure from shadow or misused AI systems.
• AI security posture management (AI-SPM): Tracks AI models and configurations, surfacing unapproved deployments and risky access patterns.

Learn how to detect and defend against shadow AI in your organization. Download our checklist outlining 6 steps to secure and optimize your GenAI usage experience.