Shadow AI Is Bigger Than Unsanctioned AI Tools

What is shadow AI? Why is it a risk? 

Shadow AI is AI usage that lacks adequate visibility, governance, or security oversight. It includes both unsanctioned AI tools and risky or poorly governed use of sanctioned AI systems. 

Shadow AI is often described as the next version of shadow IT. The comparison is useful, but incomplete.

Gartner has called attention to shadow AI as a growing enterprise risk, particularly as employees use prohibited public GenAI tools without security or IT oversight. That concern is real. Unauthorized AI usage can expose sensitive data, create compliance gaps, and leave security teams without a clear view of where company information is going.

But shadow AI is becoming broader than the use of unapproved tools. In the enterprise, the harder question is what happens after AI has been approved.

Many organizations are now moving from AI experimentation to sanctioned AI adoption. They are rolling out enterprise AI assistants, coding agents like Claude Code and Codex, productivity tools, copilots, connectors, and internal agents. These tools may be approved, paid for, and deployed through official channels, but that does not automatically mean security teams have visibility into how they are being used.

What does shadow AI mean in the enterprise?

Shadow AI refers to AI usage that lacks clear visibility, governance, or security oversight. In practice, that can include both unsanctioned AI tools and sanctioned AI systems with hidden usage patterns.

An employee pasting company data into an unapproved AI app is the obvious example. A less obvious example is an employee using an approved AI assistant in a way that creates business, privacy, or data risk. They may use it to search company files for personal purposes, summarize sensitive internal documents, or connect it to systems that were never meant to be part of that workflow.

The risk is not limited to whether the AI tool is allowed. The risk also depends on what the AI can see, what it can infer, which connectors are enabled, and what actions it can take.

Traditional vs. modern shadow AI

The initial worries about shadow AI that come to mind are oftentimes too narrow for the full spectrum of risks that enterprises need to consider when it comes to mitigating shadow AI and unregulated use of their AI tools.

Examples of Shadow AI

• An employee pastes confidential company information into an unapproved public AI tool.
• An employee uses an approved enterprise AI assistant to search internal documents for an unauthorized purpose.
• An AI assistant has access to Google Drive, SharePoint, or OneDrive containing sensitive information.
• An agent has permissions to call enterprise systems beyond what its business purpose requires.
• An employee enables an AI connector without understanding what data it exposes to the AI system.

The key distinction is that shadow AI is not defined only by whether an AI tool is sanctioned. It is defined by whether the organization's security and governance teams have sufficient visibility and control over its use.

Can sanctioned AI still create security risks? Why does it still need visibility?

Sanctioned AI can still create blind spots. A company may approve an AI assistant, coding agent, or productivity tool, but approval does not automatically answer the governance questions that matter.

Security teams still need to know which employees are using the tool, what connectors are enabled, what files are being accessed, whether labels like INTERNAL USE ONLY or CONFIDENTIAL are being respected, and whether the AI is being used in ways that align with company policy.

How do AI-SPM and Agent-SPM help with governance?

As AI adoption spreads, organizations need a more precise way to manage AI posture. AI-SPM and Agent-SPM help security teams move from broad AI policy to continuous visibility and governance.

AI-SPM vs. Agent-SPM

AI-SPM focuses on the security posture and governance of AI systems across the organization, including their usage, data access, configurations, and risks. 

Agent-SPM extends that visibility to AI agents and their agentic capabilities, including permissions, tools, connectors, MCP servers, actions, autonomy, and potential blast radius.

How do they work together to mitigate risks of shadow AI?  

AI-SPM helps teams understand where AI is being used, what data it touches, and whether usage aligns with policy. Agent-SPM goes deeper into the agentic layer, where AI systems can call tools, use connectors, interact with MCP servers, and take action across enterprise workflows.

This matters because agentic AI changes the governance model. Security teams are no longer only asking whether an AI tool was approved. They also need to understand the agent’s access, permissions, connected systems, autonomy, and potential blast radius.

What should security teams do first about shadow AI?

Security teams should start by building visibility across both unsanctioned and sanctioned AI use. That means identifying active AI tools and agents, mapping users and owners, reviewing connectors, understanding data access, and classifying risk based on what each AI system can do.

From there, teams can create governance policies that are specific enough to matter. Some AI use cases may only require ownership and documentation. Others may require tighter permissions, adversarial testing, runtime monitoring, or limits on which files, labels, and systems an agent can access.

It’s best to have all of those actions and controls centralized. 

How does Straiker let enterprise teams secure and fight against shadow AI? 

Straiker helps security teams discover, assess, and defend AI across the enterprise, including the sanctioned AI that traditional shadow AI programs can miss.

Discover AI gives security teams a complete view of their AI and agentic environment, including agents, permissions, tools, integrations, and MCP connections. Ascend AI puts those systems to the test, continuously identifying vulnerabilities and attack paths before they can be exploited. Defend AI protects AI agents at runtime, detecting and blocking threats such as prompt injection, data exfiltration, and agent manipulation.

Key Takeaways

• Shadow AI is more than unsanctioned AI. It includes AI usage that lacks sufficient visibility, governance, or security oversight, even when the underlying AI tool is officially approved.
• Sanctioned AI can still create security risk. Enterprise AI assistants and agents may have access to sensitive data, business systems, connectors, and tools that create risk when improperly configured or used.
• AI visibility must extend beyond the application. Security teams need to understand what AI can access, which permissions and connections it has, who is using it, and what actions it can take.
• AI-SPM and Agent-SPM provide the visibility and governance needed to manage this risk. AI-SPM focuses on the broader AI environment, while Agent-SPM addresses the additional risks created by autonomous agents, tools, permissions, and agent-to-system connections.
• Securing AI requires continuous discovery, testing, and protection. Straiker's Discover AI, Ascend AI, and Defend AI help security teams identify AI and agent exposure, uncover vulnerabilities through adversarial testing, and protect AI agents against threats at runtime.

Shadow AI Security Frequently Asked Questions (FAQs)

What is shadow AI?

Shadow AI is AI usage that lacks sufficient visibility, governance, or security oversight. It can include both unsanctioned AI tools and risky use of sanctioned AI applications, assistants, and agents.

Is shadow AI the same as unsanctioned AI?

No. Unsanctioned AI is one form of shadow AI. Shadow AI can also occur when employees use approved AI systems in ways that security teams cannot adequately see, govern, or control.

Can sanctioned AI still create security risks?

Yes. An approved AI assistant or agent can still create risk if it has excessive permissions, access to sensitive data, risky connectors, or the ability to take actions that fall outside its intended use.

What are common examples of shadow AI?

Examples include employees using unapproved AI tools with company data, using sanctioned AI assistants to access information for unauthorized purposes, connecting AI to sensitive enterprise systems without adequate controls, and deploying AI agents with excessive permissions or untrusted tools.

What is AI-SPM?

AI-SPM, or AI Security Posture Management, helps security teams discover and assess AI systems across their environment and understand their configurations, usage, data access, and security risks.

What is Agent-SPM?

Agent-SPM focuses on the security posture of AI agents, including their permissions, tools, connectors, MCP servers, data access, and ability to take autonomous actions. It helps security teams understand and manage the risks created by agentic AI.

How can organizations manage shadow AI risk?

Organizations should establish visibility across both sanctioned and unsanctioned AI, map AI systems to users and owners, assess data access and permissions, identify risky connections and configurations, continuously test for vulnerabilities, and enforce runtime protections where needed.

How does Straiker help secure shadow AI?

Straiker provides a continuous approach to AI security through three products: Discover AI identifies AI and agent exposure and surfaces security risks; Ascend AI continuously tests AI systems and agents for vulnerabilities and attack paths; and Defend AI detects and blocks threats against AI agents at runtime. Together, they help security teams discover, test, and defend AI across the enterprise.