What is Shadow AI?

Introduction

Artificial intelligence has moved from experimentation to everyday use incredibly fast. Employees across engineering, marketing, finance, sales, and operations are adopting AI tools to write code, analyze data, generate content, and make decisions with greater speed and efficiency than ever before. According to Index.dev, 78% of organizations now use AI in cloud systems, with 72% using it in daily operations. 

While this rapid adoption has brought productivity gains, it has also introduced new security challenges. Teams are adopting AI tools with good intentions to solve real problems, but often without IT or security approval, visibility, or guardrails. As AI becomes embedded into core workflows, it also becomes harder for IT and security teams to understand where and how it is being used. 

This rapid adoption has introduced a new and growing blind spot for organizations: Shadow AI.

What is Shadow AI? (Shadow AI Defined)

Shadow AI refers to the use of artificial intelligence tools, models, or applications within an organization without the approval, monitoring, or involvement of IT, security, or governance teams. 

This can include public generative AI tools, AI-powered browser extensions, external AI APIs embedded into internal applications, or AI features embedded in SaaS platforms that were never formally evaluated.

In most cases, Shadow AI is not malicious. Employees adopt these tools to improve productivity and meet deadlines. However, this behavior is more common and risky than many organizations realize. In fact, according to the National Cybersecurity Alliance (NCA), over one-third (38%) of employees admit to sharing sensitive work information with AI tools without their employer’s permission. Because these tools operate outside approved processes, organizations lose visibility into how data is used, where it flows, and how it is protected. 

Unlike traditional software, AI systems can retain, learn from, and reuse data, making unmanaged usage particularly risky, especially in cloud environments where data, identities, and workloads are so deeply interconnected.

How Shadow AI Happens

Shadow AI is rarely the result of bad intent. Most often, it comes from structural and operational gaps across organizations for multiple reasons:

• Ease of access: Many AI tools are free, browser-based, and require little to no setup, making them easy to adopt without approval or training.
• Speed over governance: Business pressure favors speed. When sanctioned tools lag behind employee needs, they turn to faster options.
• Lack of policy or awareness: Many organizations still lack formal AI governance, which leaves employees either unsure of what is allowed, or assuming anything is.
• AI everywhere: AI is increasingly embedded into SaaS platforms that were previously approved, expanding risk without teams even realizing a new capability has been introduced.

Common Shadow AI Examples

Shadow AI can appear in almost any role or department. Breaking Shadow AI into categories helps illustrate how widespread the issue has become, as well as highlight the types of data, workflows,and decisions affected. In each of these examples, productivity improves, but security and compliance are bypassed and new risk is introduced.

Engineering & Development

In engineering, Shadow AI often starts with developers looking for efficient ways to troubleshoot issues, write code faster, or find solutions to other problems they may have. Without realizing it however, the data they are sharing could be sensitive.

• Developers paste proprietary source code in public LLMs to debug issues.
• AI coding assistants trained on unknown datasets suggest insecure patterns.
• Internal APIs or architectural details are included in prompts.

Data Analytics

Analysts usually leverage AI to accelerate insights from large datasets. Shadow AI often appears when teams use external tools to clean, analyze, or summarize data, sometimes unaware of what the AI retains or how it could be used.

• Analysts upload sales or customer datasets into AI tools for faster insights.
• Internal financial or operational data is summarized using external LLMS.

Product & Strategy

Product and strategy teams adopt AI to summarize roadmaps, brainstorm ideas, and analyze market trends. Shadow AI often appears when internal plans or competitive intelligence information is fed into tools outside the organization’s governance.

• Product managers upload roadmaps or internal strategy documents for summarization.
• Competitive intelligence is input, updated, or analyzed with unclear retention policies.

Marketing & Sales

Marketing and sales teams increasingly rely on AI to generate creative content, draft customer outreach, and enrich prospect insights. 

• Generating images using brand assets and sensitive company information in unvetted AI platforms.
• Sales teams leverage AI-generated prospect insights without validating accuracy.

Shadow AI vs. AI Governance

This table highlights the core differences between Shadow AI and Governed AI.

Category	Shadow AI	Governed AI
Visibility	Limited or no visibility	Full visibility into tools, usage, and data access
Data Handling	Unclear data handling and retention	Defined data policies and controls
Security Review	Adopted without IT or security approval	Approved and reviewed by security and governance teams
Compliance	High Risk	Aligned to established frameworks
Accountability	Unclear ownership	Clear ownership and oversight

Shadow AI Risks

Shadow AI matters because of how it interacts with sensitive data and business processes, especially from an IT and security perspective. As AI becomes more deeply embedded into daily workflows, Shadow AI doesn’t remain isolated. It spreads across teams, tools, and processes, often faster than policies and controls can keep up.

Data Privacy and Leakage

One of the most immediate risks is data exposure. Employees may unintentionally share customer data, intellectual property, financial information, internal communications, or source code. Once data is shared with an unmanaged AI tool, organizations lose control over how long it is retained, where it is stored, or how it is reused.

Compliance and Regulatory Risk

Many regulations require organizations to maintain control over how sensitive data is processed and stored. Shadow AI can introduce violations related to GDPR, HIPAA, financial services regulations, and contractual obligations. Because usage is often invisible, organizations may not discover violations until after an audit or incident occurs.

Security and Attack Surface Expansion

AI tools often require broad permissions, integrations, or sensitive data within prompts to function effectively. When used without security oversight, they can introduce unsecured APIs, create new data exfiltration paths, expand the attack surface, or bypass identity and access controls. 

According to Business Wire, 80% of AI tools operating within companies are unmanaged by IT or security teams. The phrase “you can’t protect what you can’t see” is especially relevant for Shadow AI.

Trust, Accuracy, and Decision Risk

AI outputs are not always accurate or unbiased. AI tools may introduce undetected errors, biased outputs may influence decisions, and AI-generated content may be mistaken for verified facts. In regulated or safety-critical environments, this can have serious consequences.

Best Practices to Mitigate Shadow AI Risks

Reducing your risk from Shadow AI doesn’t mean blocking innovation. In fact, overly restrictive controls often increase the usage of Shadow AI. The goal is to enable AI adoption safely, with guardrails that protect the organization.

Establish AI Governance and Policy

Effective AI governance defines which tools are approved, what data can be used, how outputs should be reviewed, and who is accountable. Many organizations benefit from offering a corporate instance of a preferred AI platform along with implementing sensitivity labels and DLP policy enforcement, allowing centralized access, consistent controls, and increased data protection. Also, data sharing and model training can often (and should be) disabled on corporate inputs.

Increase Visibility

Organizations often struggle with Shadow AI because they lack visibility into which tools are in use, who is using them, and what data they access. Improving visibility may include monitoring network traffic, reviewing SaaS integrations, auditing API usage, or identifying where AI is embedded into existing platforms. Once organizations gain visibility, they can then make informed decisions about risk, prioritize controls, and identify areas where Shadow AI is most prevalent.

Educate Employees

Most employees are trying to improve efficiency, not introduce risk. Organizations should invest in training on effective and accepted AI usage, tailored to different roles where possible, to help employees understand data handling, prompt hygiene, output validation, and accountability. 

Provide Secure, Approved Alternatives

Providing secure, approved AI tools removes the need for employees to introduce Shadow AI. These tools should meet security and compliance requirements, integrate with existing workflows, and be easy to use. Organizations should also consider adding additional review steps before launching AI-powered tools or features developed with AI assistance.

Conclusion

Shadow AI is emerging as a critical challenge as organizations quickly adopt AI across cloud environments. While AI delivers productivity gains, unmanaged usage can quietly introduce data exposure, compliance gaps, and expanded attack surfaces. As AI becomes embedded into everyday workflows, the risk scales alongside adoption, making visibility and governance increasingly essential.

Managing Shadow AI doesn’t require slowing innovation. It starts with understanding where AI is used, what data it can access, and how it interacts with cloud identities and infrastructure. By addressing Shadow AI as part of a broader cloud security strategy, organizations can confidently embrace the benefits of AI while maintaining control, trust, and a strong security posture.

Learn More About Orca Security

Shadow AI is largely a cloud security problem. AI tools, models, data sources, and identities are tightly connected across modern multi-cloud environments, and unmanaged AI usage often expands risk through cloud-native services, APIs, and permissions rather than through traditionally monitored endpoints. As a result, managing Shadow AI requires visibility across the entire cloud estate.

Orca Security provides agentless, full-stack visibility that helps organizations continuously identify, prioritize, and remediate cloud security risk in context. Orca enables security teams to discover AI-related assets, integrations, and services running across cloud accounts, understand how sensitive data is exposed, and identify risky permissions, misconfigurations, and attack paths that Shadow AI can introduce. This allows teams to see not only that AI is being used, but where, how, and why it matters.

By continuously prioritizing risk across workloads, identities, and data, Orca helps organizations manage Shadow AI as part of a unified security strategy, enabling safe AI adoption without slowing innovation.

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