Shadow IT vs Shadow AI: What’s the Difference?

Imagine a customer service representative at your organization uploads sensitive customer data into an AI tool to draft emails more quickly. When an employee uses an AI tool without IT approval, it is known as shadow AI, and such scenarios are becoming increasingly common. Among employees who use AI at work, 78% report using tools that have not been formally approved by their organization, according to Microsoft’s 2024 Work Trend Index. While security teams have developed strategies to address traditional shadow IT, shadow AI introduces new risks that require a more modern approach. The main difference between shadow IT and shadow AI is that shadow AI not only transfers and stores sensitive data but also actively processes and potentially retains it.

Continue reading to learn more about shadow IT, shadow AI and how to detect and manage shadow AI effectively.

What is shadow IT?

Shadow IT refers to any software or cloud service that employees use without IT’s knowledge or approval. This can include using personal email accounts to share work files, installing unauthorized browser extensions or connecting personal devices to a company network. Because these actions bypass formal approval processes, they are not vetted by security teams before use. Although shadow IT is mainly driven by productivity instead of malicious intent, it can introduce a variety of security risks:

• Limited visibility: When IT teams are unaware of unauthorized applications, they cannot monitor usage or protect company data. Any security vulnerability in those applications can become a hidden entry point into a network.
• Compliance violations: Unauthorized software rarely meets the data handling criteria of regulations like GDPR or HIPAA. If data is handled improperly, organizations can face serious penalties and fines.
• Expanded attack surface: Each unapproved application is a potential attack vector for cybercriminals. As shadow IT grows, especially in cloud environments, securing the organization’s perimeter becomes more difficult.

What is shadow AI?

Shadow AI refers to the use of AI tools or applications without IT’s knowledge or approval. Common examples include employees using generative AI to draft internal communications with confidential data or developers running code through AI tools using personal accounts. What makes shadow AI particularly challenging is that employees aren’t always intentionally bypassing security measures. Many modern applications have AI features embedded by default, so employees may not realize they’re using AI at all.

Shadow AI introduces risks that go beyond what many organizations are prepared to address:

• Untraceable data leaks: When employees use AI tools through personal accounts, organizations typically have no access to interaction logs, even on platforms that offer logging at the enterprise tier. There is no audit trail of what data was entered, how it was processed or whether it was retained.
• Identity security implications: Shadow AI introduces new security risks that traditional security models weren’t designed to handle, mainly with the rise of autonomous AI agents. When employees create accounts on external AI platforms, organizations lose control over how those identities access sensitive data.

Key differences between shadow IT and shadow AI

Shadow IT and shadow AI share the same root cause of employees adopting tools to work more productively, but they differ in how they introduce risk.

Data processing and sharing

With shadow IT, data typically follows a structured process such as file uploads or document sharing. These actions create predictable patterns that security tools can detect. Shadow AI, on the other hand, operates through unstructured, conversational inputs. Employees enter sensitive data into prompts that are processed in real time and transmitted over standard HTTPS traffic, making it challenging to distinguish this traffic from normal activity.

Visibility and auditability

Shadow IT activity typically generates audit trails through application usage, file transfers or network monitoring, so security teams can investigate security incidents. In contrast, shadow AI often lacks centralized visibility since many AI platforms don’t provide organizations with detailed interaction logs. When employees use external AI tools, especially through personal accounts, organizations may have limited or no access to interaction data, making it difficult to determine how information is used or stored.

Data retention risk

Shadow IT introduces risks around unauthorized data storage, sensitive data ending up outside approved systems in identifiable locations. Shadow AI introduces a different kind of risk. On consumer-tier AI platforms, data entered into prompts may be used to train future models by default, though most enterprise-tier platforms disable this. The risk is highest when employees use personal accounts on consumer tools, bypassing the data protections enterprise licensing provides.

How to detect and manage shadow AI

Because shadow AI exposes sensitive data in ways that are difficult to detect, organizations must take a proactive approach to managing it. Traditional tools used to manage shadow IT do not address the same risks associated with employees entering sensitive data into AI platforms or granting AI access to internal systems. While many organizations jump to banning AI tools altogether, this often backfires because it drives employees to find unapproved tools without visibility. Organizations should focus on governance by doing the following:

• Create an AI acceptable use policy: Establish clear guidelines that define which AI tools are approved, what data can be shared and the consequences of misuse.
• Build an internal AI app catalog: Provide employees with a list of vetted AI tools they can use so they do not seek out unapproved and potentially risky alternatives.
• Deploy enterprise-grade AI solutions: Enterprise AI solutions offer greater control over data handling and storage compared to consumer-grade AI tools.
• Conduct regular AI compliance audits: Monitor which AI tools are being used and identify emerging security risks.
• Train employees on AI usage: Ongoing education builds organizational awareness that employees may not fully understand from reading a policy alone. Organizations with active training programs help employees understand how to use AI safely.

Take control of shadow AI

Shadow AI spreads quickly, operates through channels that are difficult to monitor and introduces risks that traditional security tools weren’t designed to catch. Governing it effectively requires visibility into every identity — human and machine — that interacts with AI systems and the data they access. As AI agents become embedded in enterprise workflows, the machine identities they rely on (i.e., API keys, service account tokens and infrastructure secrets) need the same governance as human user accounts. An AI agent with excessive permissions and no audit trail is the shadow AI risk at its most dangerous.

With a zero-trust Privileged Access Management (PAM) solution like Keeper^®, organizations can gain centralized visibility and control over users, systems and identities. Whether risk comes from unauthorized applications or unsanctioned AI usage, Keeper helps ensure that all access is closely monitored and secured.

Start your free trial of KeeperPAM today to ensure all identities in your environment are properly managed.