Shadow AI is the use of artificial intelligence tools, large language models (LLMs), or autonomous AI agents inside an organization without explicit approval, governance, or oversight from IT and security teams.
Shadow AI mirrors the core concept of unauthorized software found in shadow IT, but creates a more complex risk profile. Traditional file-sharing apps function passively, while generative AI tools increasingly support agentic capabilities. AI agents process large volumes of proprietary data, solve complex problems, and act autonomously on behalf of users. Provider policies and account types define whether submitted data is retained, logged, or potentially used for model training.
Agent-driven workflows can create poorly monitored data egress paths that bypass traditional security controls. Organizations may believe their data remains contained within approved systems. In reality, employees may unknowingly submit intellectual property, source code, or customer data to public AI models to accelerate tasks.
Recent industry research from the Cloud Security Alliance indicates that most organizations lack formally adopted policies for managing non-human identities (NHIs), leaving AI- and agent-driven identities largely ungoverned.
Key Takeaways
- Shadow AI has evolved: It now includes autonomous agents and non-human identities that act independently of users.
- The identity gap: A lack of NHI governance controls increases reliance on long-lived, non-rotating API keys and unmanaged service accounts, expanding the attack surface.
- Data and compliance risks: Depending on provider policies, account type, and configuration, submitted data may be logged, retained, or used to improve models, potentially leading to IP leakage and compliance exposure.
- The solution: Security teams must shift from “blocking” to “managing” by implementing identity-centric controls, automated discovery, and secure sandboxes.
Shadow AI vs. enterprise AI
How shadow AI differs from enterprise-grade AI initiatives.
Feature | Traditional / Enterprise AI | Shadow AI |
|---|---|---|
Deployment | Centralized, strategic implementation by IT or data science teams. | Decentralized, ad hoc adoption by individual employees or departments. |
Governance | Subject to formal risk assessments, legal review, and data processing agreements (DPAs). | Often bypasses procurement, legal, and security reviews. |
Data Privacy | Data is often isolated or processed within enterprise instances that are not used for model training, subject to contractual controls. | Data may be retained, logged, or used for model improvement depending on the provider, account type, and usage terms, particularly when consumer or free-tier accounts are used. |
Visibility | Full logging, auditing, and integration with identity and access management systems. | Limited visibility for IT teams. Activity may appear as standard web or SaaS traffic unless identity-aware controls are in place. |
Drivers of shadow AI: Low-code, agents, and speed
Shadow AI is growing rapidly, outpacing the development of formal governance frameworks.
Zero-infrastructure adoption
SaaS-based generative AI tools require no provisioned infrastructure to deploy. An employee can access advanced reasoning capabilities through a browser extension or a free web account in seconds.
The “citizen developer” and API sprawl
Development is no longer limited to technical teams. Low-code and no-code platforms allow citizen developers to build custom agents and integrations. Chatbots commonly authenticate through individual user sessions. In contrast, automated workflows often rely on API keys or OAuth tokens that persist beyond a user’s session, generating unmanaged NHIs.
Remote work and the eroding perimeter
Today’s workforce is increasingly distributed, weakening the traditional network perimeter. Employees working remotely often prioritize speed and output, especially under deadline pressure. When approved tools feel slow or restrictive, users turn to unsanctioned AI alternatives to get work done. This can include browser extensions, LLM plugins, or chatbots that integrate with enterprise systems, creating unmanaged non-human identities and additional shadow AI vectors.
Risks of shadow AI: Security, compliance, and quality
Unmanaged AI introduces risks that extend beyond policy violations. Agentic workflows, in particular, create de facto NHIs when they operate using embedded credentials, API tokens, or delegated access without formal identity registration.
For example, an employee might connect a custom AI agent to internal analytics or CRM systems using a long-lived API key to automate reporting. That agent then operates as an NHI with persistent access, outside standard provisioning, review, or revocation processes.
Data exfiltration and model training
The most immediate risk is data exfiltration. Public LLM providers may retain user inputs for logging, safety monitoring, or model improvement, depending on provider policies and account type. When an employee uploads a confidential document or proprietary code to a public chatbot, that data leaves the enterprise boundary. Depending on the provider’s controls, the data may persist outside the organization’s governance model.
Regulatory exposure
Regulations, including the GDPR, HIPAA, and the CCPA, set strict requirements on how personal and sensitive data is processed, stored, and shared. Shadow AI may bypass these safeguards when AI tools or agents handle regulated data outside approved workflows or DPAs.
For organizations subject to the EU AI Act, additional risk arises when unmanaged AI systems are deployed in regulated or high-risk contexts without documented controls, transparency, or human oversight. In these cases, Shadow AI can expose organizations to data protection and AI-specific compliance violations.
Hallucinations and decision quality
Shadow AI can degrade decision quality when outputs are produced without validation or oversight. Outputs from unvetted tools may include hallucinations or outdated information, particularly when they lack grounding, retrieval-augmented approaches, or human review. When employees rely on these outputs for strategic or operational decisions, the risk of flawed or suboptimal business outcomes increases.
Governance framework: How to secure shadow AI
Organizations can’t simply forbid AI adoption. The productivity gains are too significant. Instead, security leaders need to shift from a “block” mindset to a “manage” mindset.
From block to enable
- Acceptable use: Define which AI tools are approved and which are prohibited.
- Data classification: Specify which data types (e.g., public vs. restricted) may be used with AI.
- Least privilege: Ensure every AI agent connecting to corporate data has a distinct managed identity with scoped permissions, not a shared or hard-coded credential.
Visibility: Identity-centric monitoring
Traditional network monitoring can’t keep pace with agent-driven workflows. Organizations need identity-centric visibility that tracks human users and AI agents across app-to-app connections to reduce security blind spots.
Engaging employees in governance
Strict AI bans may increase shadow behavior. Successful programs take a more collaborative approach:
- Providing alternatives: Offer sanctioned, enterprise-grade AI tools.
- Sandbox environments: Create secure AI labs where employees can experiment without exposing production data.
- Education: Train employees on data protection and intellectual property risk, not just policy enforcement.
The future: From chatbots to autonomous agents
Shadow AI is evolving. The focus is shifting from unauthorized chatbots to unauthorized autonomous agents.
Trends to watch
- Agentic AI: AI agents can now execute multi-step workflows.
- Credential sprawl via Agents: As AI agents and other NHIs connect to applications, token sprawl can increase, expanding the non-human identity attack surface. Using ephemeral credentials or workload identity federation can mitigate this risk.
Potential solutions
Securing AI requires treating agents as first-class identities:
- Automated discovery: Continuously identify unmanaged NHIs, including AI agents, service accounts, and API-based workloads.
- Identity control planes: Centralize authentication, authorization, and lifecycle management for human and non-human identities, including AI agents, service accounts, and other workloads.
- Cross-app context: Enforce least-privilege access based on the task context, user delegation, and agent-specific permissions.
Frequently asked questions
Is shadow AI malicious?
In most organizations, shadow AI emerges unintentionally. Employees adopt AI tools to work faster, meet deadlines, or solve specific problems when approved options are unavailable. Shadow AI poses a risk not because of intent, but because it occurs without visibility, governance, or consistent security controls.
Does using an enterprise AI tool eliminate Shadow AI risk?
Even approved AI platforms can introduce Shadow AI if employees connect them to unsanctioned data sources, create custom agents without review, or reuse credentials in unintended ways. Governance must extend beyond tool approval to include how AI is configured, accessed, and integrated.
When does an AI tool become an AI agent from a security perspective?
An AI system becomes an agent when it can execute multi-step workflows autonomously, maintain context across steps, or perform actions on other systems without continuous human input. When doing so, it should be treated as an NHI with defined permissions, authentication, and monitoring.
Why are identity controls critical for managing Shadow AI?
Network and application controls alone cannot govern AI agents that operate across systems. Identity provides a consistent enforcement layer for both human users and AI agents. By implementing identity-based authentication and authorization, organizations can help ensure that AI systems access only data and perform only actions permitted by the human user or service they represent.
Secure your AI strategy with Okta
As AI evolves from passive tools to autonomous agents, identity becomes the primary and most scalable control plane. The Okta Platform helps organizations turn shadow AI from an unmanaged risk into a governed capability by identifying the unmanaged NHIs and API keys that power AI agents and app-to-app connections across the environment, and governing non-human identities with granular, identity-based policies.
