Shadow AI Explained: Causes, Consequences, and Best Practices for Control

Artificial intelligence adoption is accelerating across industries, with organizations embedding AI into critical business functions at an unprecedented pace. According to a McKinsey Global Survey on AI, half of respondents say their organizations have adopted AI in two or more business functions, a sharp increase from less than a third in 2023. This rapid expansion extends to SaaS investments, with 77.6% of IT leaders reporting upgrades or increased spending on AI-powered SaaS applications in the past year.

However, as AI adoption grows, so does the risk of shadow AI—using artificial intelligence tools and models without IT oversight. Just as shadow IT introduced unmanaged software risks, shadow AI presents new security, compliance, and governance challenges that organizations must address. Companies face potential data leaks, regulatory breaches, and uncontrolled AI-driven decision-making without visibility into AI usage.

What Is Shadow AI?

Shadow AI refers to the unauthorized use of artificial intelligence tools within an organization, often bypassing IT oversight and security protocols. Similar to shadow IT, where employees introduce unapproved software, shadow AI emerges when teams deploy AI models, chatbots, or automation tools without visibility from IT or compliance teams.

While traditional software stores information, AI models can access it in new and different ways. Sensitive information could become part of the model’s training data, taking it from inside the company to the public domain on a broad scale. As AI processes sensitive company data, it can also generate decisions that impact operations, and even introduce compliance risks. 

Without proper monitoring, organizations may unknowingly expose proprietary or customer information, create biased AI outputs, or breach industry regulations. As AI capabilities continue to expand, managing shadow AI becomes a necessary part of IT governance to mitigate risks while still enabling innovation.

What Causes Shadow AI?

The rapid adoption of AI tools is driven by a variety of factors, many of which are outside the direct control of IT teams. From easy access to AI-powered applications to decentralized purchasing, organizations must navigate a complex landscape to maintain visibility and control.

Accessibility of AI Tools

AI tools are more available than ever, with many offering free or low-cost access. Employees can now integrate AI models into their workflows with minimal effort, whether through standalone platforms or embedded features in existing software. Open-source AI models, such as large language models (LLMs) and image-generation tools, further lower the barrier to entry, allowing business users to experiment without IT oversight.

Decentralized Purchasing

Rather than centralized IT teams, individual business units increasingly control enterprise software spending. Employees and lines of business are responsible for 84% of applications and 74% of SaaS spending. This decentralized approach makes it difficult to enforce AI governance, as purchasing decisions are made without consistent oversight or standardization.

Existing Software and Tools Adding AI Capabilities

SaaS providers are integrating AI into existing platforms, often without requiring separate purchases or approvals. 

Collaboration tools like Microsoft Teams, CRM platforms such as Salesforce, and business intelligence software like Tableau now include AI-powered automation, recommendations, and predictive analytics. For example, Salesforce Einstein enhances CRM workflows with AI-driven insights, while Microsoft Copilot brings generative AI into workplace applications. 

As a result, companies may be using AI without realizing the extent of their exposure, making it difficult for IT teams to track and assess AI-related risks.

Lack of Governance and Policies

Without clear governance models, AI tools enter organizations without security assessments, compliance checks, or purchasing approvals. Many enterprises lack formal policies for AI use, creating inconsistencies in how different teams adopt and manage AI solutions. In security and procurement reviews, AI functionality may not be flagged or assessed, leading to uncontrolled adoption across multiple business functions.

Demands for Efficiency

Organizations are under pressure to improve efficiency, and AI presents a compelling solution. Employees use AI to automate repetitive tasks, generate content, or analyze large datasets without waiting for IT to vet and approve tools. This speed-first mindset can result in shadow AI deployments that bypass risk assessments, increasing the likelihood of data security vulnerabilities and compliance violations.

A Gap in Workforce Readiness

Many employees lack the training to use AI responsibly, leading to unintentional risks. Without proper guidance, employees may upload sensitive data into AI models, trust AI-generated outputs without verification, or fail to recognize security risks associated with AI-generated content. A lack of AI literacy across departments contributes to the uncontrolled spread of Shadow AI.

The AI Utility Gap

AI models often promise high-value insights but may not align with actual business needs. As a result, employees may experiment without IT support.

The Diversion of Business and Employee Vision

Business and employees alike are growing more enthusiastic about using AI. However, employees may introduce AI tools that serve individual needs rather than company-wide goals. This can create fragmented and unaligned AI adoption.

The Danger of the Occasional User

Casual users who intermittently engage with AI tools may overlook security best practices, increasing the risk of data leaks or compliance violations.

The Difference Between Shadow AI and Shadow IT

Shadow AI and shadow IT both involve technology that operates outside IT’s direct oversight, but their risks and impacts differ. Shadow IT refers to unauthorized applications or software used by employees, while shadow AI specifically involves artificial intelligence tools and models that function without governance.

Unlike shadow IT, which is largely about unapproved software adoption, shadow AI introduces new concerns related to data privacy, automated decision making, and AI-generated risks. AI tools can process sensitive company data, make predictions, and even automate workflows—all without IT visibility, increasing the potential for compliance issues, security breaches, and financial loss.

Shadow IT

Shadow IT has long been a challenge for organizations, with employees using unauthorized software or SaaS applications to improve productivity. This often includes file-sharing platforms, collaboration tools, and personal cloud storage solutions that bypass IT security protocols.

Many companies have implemented SaaS Management tools and access controls to mitigate the risks of Shadow IT. However, as more enterprise software integrates AI, traditional IT oversight strategies may no longer be sufficient to address the growing challenge of AI-driven tools entering the workplace.

Shadow AI

Shadow AI takes the risks of shadow IT further by introducing self-learning models, generative AI, and predictive analytics that can operate outside IT’s control. Employees may use AI-powered assistants, automation tools, or external AI APIs to process company data, often without understanding how data is stored, shared, or used by these models.

Unlike unauthorized SaaS tools, which can often be detected through network activity, shadow AI may exist within approved platforms or be embedded within internal workflows. As AI capabilities expand, companies must develop governance models that specifically address unregulated AI adoption, ethical risks, and compliance challenges.

The Risks and Impact of Shadow AI

As shadow AI adoption increases, organizations must consider the broader risks and business implications. From security concerns to compliance challenges, the lack of AI oversight can result in significant financial, legal, and reputational damage.

Data Breaches and Security

Shadow AI introduces serious cybersecurity risks, as employees may unknowingly input sensitive data into AI models that store or process information externally. Without IT controls, AI-generated outputs may also be used in critical business decisions without proper validation.

The 2025 SaaS Management Index report found that 89.4% of IT leaders have concerns about the security risks associated with AI tools. These risks include data leaks, unauthorized access, and vulnerabilities in third-party AI platforms. Companies must establish AI security policies to prevent employees from exposing proprietary information through unmanaged AI applications.

Noncompliance with Regulations

AI models that process and store corporate data may violate industry regulations such as GDPR, HIPAA, and SOC 2, particularly when data handling policies are unclear. Shadow AI can result in unintentional compliance breaches, as companies struggle to track where data is being processed, stored, or used in AI workflows.

Organizations operating in highly-regulated industries face additional risks, as unapproved AI models may generate incorrect financial reports, biased hiring decisions, or unauthorized medical recommendations, leading to regulatory penalties and legal liabilities.

Financial Implications

The financial impact of shadow AI extends beyond security risks, as uncontrolled AI adoption can drive up IT costs. Many organizations are investing heavily in AI-powered applications, with spending on AI-native applications rising 75% in 2024, averaging $398,271 per organization.

AI adoption also introduces unpredictable costs due to consumption-based pricing models. Many SaaS vendors now charge based on usage, API calls, or AI-generated content, making it difficult for IT teams to track spending.

• Of the SaaS companies monetizing AI features, 25% employ usage-based pricing, while 22% use a hybrid strategy combining both subscription- and usage-based models (2024 SaaS Benchmarks Report, High Alpha).
• 65% of IT leaders reported unexpected SaaS charges due to consumption-based or AI pricing models.

Without centralized visibility into AI-driven SaaS spending, organizations risk overspending on AI-powered features that may not be fully utilized or necessary for business operations.

Reputational Damage

Uncontrolled AI use can impact an organization’s reputation, especially when AI-generated content, recommendations, or decisions are made without oversight. AI tools can produce biased hiring decisions, inaccurate financial insights, or misleading marketing content, damaging brand credibility. If customers or stakeholders discover that a company is using unregulated AI tools that compromise security or privacy, it can lead to loss of trust, negative media coverage, and legal challenges.

Misinformation and Biased Outputs

AI models, particularly generative AI tools, can produce misleading, inaccurate, or biased content. Employees relying on AI-generated insights without verifying their accuracy may introduce errors into financial reporting, marketing strategies, or customer service interactions.

Bias in AI models is another growing concern, as algorithms trained on incomplete or skewed datasets can reinforce discriminatory patterns. Without proper AI governance, organizations risk deploying biased AI models that result in legal and ethical complications.

Managing and Mitigating Shadow AI Risks

To effectively control shadow AI, organizations must take a proactive approach that includes leadership buy-in, policy development, employee education, and continuous oversight. Without proper governance, AI tools can introduce security risks, compliance violations, and unexpected financial burdens. The following strategies help businesses manage shadow AI while still enabling innovation and productivity.

Educating the C-Suite and Leadership on Shadow AI Risks

Executive leadership plays a critical role in AI governance. Yet, many organizations struggle to communicate the full scope of risks associated with shadow AI. IT teams must engage C-suite leaders and department heads to ensure they understand how unmanaged AI adoption can impact data security, compliance, and operational efficiency.

Shadow AI governance requires more than just IT intervention—it demands cross-functional collaboration from legal, finance, compliance, and HR teams. By ensuring leadership is aligned on the risks and responsibilities associated with AI, organizations can develop a more structured, strategic approach to AI oversight.

Establishing Governance and Policies for Generative AI Tools

AI adoption is accelerating, and IT leaders recognize the need for formal governance. According to the 2025 SaaS Management Index, 81.8% of IT leaders have documented policies specifically governing AI tools. However, the effectiveness of these policies depends on consistent enforcement, regular updates, and employee awareness.

Organizations should create clear guidelines outlining how AI tools can be used, which tools are approved, and how data should be handled. Governance models should include risk assessments, procurement approvals, and compliance reviews to ensure AI adoption aligns with security best practices.

Employee Communication and Awareness Around Shadow AI

Many employees use AI-powered tools without realizing the risks involved or whether their usage aligns with company policies. IT teams must develop clear communication strategies to ensure employees understand:

• Which AI tools are approved and which are restricted
• How AI-generated content should be reviewed for accuracy
• What security risks exist when using AI-powered automation tools
• How to report shadow AI usage for proper review

By keeping employees informed, organizations can reduce the likelihood of unintentional security breaches or compliance violations tied to unauthorized AI tools.

Deciding How and When Employees Can Use Generative AI Tools

Rather than banning AI tools outright, companies should define when and how employees can leverage AI for their work. A structured approach includes:

• Identifying appropriate AI use cases that align with business goals
• Establishing permissions and access controls for AI-powered platforms
• Ensuring employees verify AI-generated outputs before using them for decision making

By prioritizing risk management rather than complete restriction, organizations can encourage responsible AI adoption without stifling innovation.

Collaboration Across Departments

Shadow AI governance should not be limited to IT teams—it requires collaboration between security, legal, finance, HR, and business unit leaders. Cross-functional teams can help assess AI risks, establish best practices, and monitor AI adoption trends across the organization.

By integrating AI governance into existing risk management frameworks, businesses can develop a unified approach that balances innovation with security and compliance.

Training Employees on AI Use and Risks

Employees need proper training to ensure they understand AI’s limitations, biases, and security implications. AI literacy programs should educate employees on:

• How AI models process and store data
• The risks of relying on AI-generated insights without validation
• How to securely use AI-powered tools without exposing sensitive data

Regular training ensures that employees remain aware of evolving AI risks and best practices, reducing shadow AI exposure across the organization.

Continually Updating Policies and Processes

AI technology evolves rapidly, making static governance policies ineffective. Organizations should regularly review and update AI policies to account for:

• New AI features and integrations introduced by existing SaaS vendors
• Emerging regulatory requirements related to AI governance
• Changes in AI pricing models that impact IT budgets

By keeping policies agile and adaptive, organizations can stay ahead of AI-driven risks rather than reacting to them.

Monitoring AI Features in Existing Business Tools

Many AI-powered features are introduced through software updates, often without IT teams being fully aware. Organizations should establish a process for reviewing AI functionality in SaaS platforms to prevent:

• Unintentional AI adoption through embedded features in productivity tools
• Security risks tied to AI-powered automation within enterprise applications
• Unexpected cost increases due to AI-based consumption pricing

Regular monitoring and full visibility ensures that AI capabilities are properly assessed and aligned with business needs before they become unregulated shadow AI risks.

Open communication with your SaaS vendors is also key to understanding the risks their AI tools may introduce to your business. Learn what questions to ask your vendors about AI in the short video below.

Additional Strategies for Managing Shadow AI

• Define your organization’s risk appetite by establishing clear guidelines on acceptable AI risks and those requiring strict governance controls.
• Identify AI use cases that align with business needs and those that introduce unacceptable risks.
• Implement guardrails such as technical and security controls that prevent unapproved AI usage in sensitive workflows.
• Prioritize AI solutions by risk and business impact, classifying tools based on their potential risks and strategic value.
• Engage employees in AI adoption strategies to ensure they understand governance policies and feel included in AI decision-making.
• Establish clear accountability for AI governance by assigning ownership of AI oversight to specific departments or leadership roles.
• De-risk the occasional user by educating infrequent AI users on how to safely engage with AI tools to prevent misuse or security risks.

What to Do When You Spot Shadow AI: How to Get Started

Once an organization identifies shadow AI within its environment, the next step is to assess its impact, mitigate risks, and establish governance processes to prevent further unapproved AI use. Addressing shadow AI requires visibility, collaboration, and structured policy enforcement.

• Assess the extent of AI usage: Identify which AI tools are in use, who is using them, and whether they integrate with existing enterprise systems. This can be done through SaaS discovery tools (like Zylo), expense tracking, and security audits.
• Determine the risks and compliance gaps: Evaluate how these AI tools handle company data, whether they comply with security policies, and what potential risks they introduce.
• Engage stakeholders: Bring in IT, security, legal, and business unit leaders to determine the best course of action for mitigating risks while allowing safe AI adoption.
• Decide whether to restrict, monitor, or formally adopt the AI tools: Some AI-powered applications may be beneficial, while others pose too much risk. Establish a process to evaluate which tools can be integrated into official workflows.
• Communicate findings with employees: Ensure employees understand why certain AI tools are approved or restricted, and provide clear guidance on how they can request AI-powered solutions that align with governance policies.

With this approach, organizations can transition from reactive AI discovery to proactive AI management, ensuring that AI-powered innovation happens within a secure, compliant framework.

Best Practices for Managing Shadow AI

Effectively managing shadow AI requires more than just detection. It involves ongoing monitoring, employee education, and governance updates to keep AI adoption aligned with business needs and security requirements. Here are a few strategic best practices you should follow.

• Implement AI visibility tools: SaaS Management platforms that detect AI-powered applications can help IT teams monitor AI adoption across the organization.
• Develop clear AI policies: Establish well-defined policies for AI adoption, usage, and security, ensuring employees know which AI tools are approved and how they should be used responsibly.
• Regularly review AI adoption trends: Since AI tools evolve rapidly, organizations should frequently reassess AI applications in use and adjust governance strategies accordingly.
• Educate employees on AI security and compliance: Provide ongoing training to ensure employees understand the risks of unapproved AI use, including data privacy concerns, security vulnerabilities, and compliance issues.
• Integrate AI governance into broader IT policies: AI oversight should be part of an organization’s overall IT governance framework, ensuring that AI risks are managed alongside software, security, and compliance considerations.

By following these best practices, organizations can reduce the risks of Shadow AI while enabling employees to leverage AI-powered tools effectively and securely.

Shadow AI FAQs

What is shadow AI?

Shadow AI refers to the use of artificial intelligence tools within an organization without IT oversight or formal governance. Employees may adopt AI-powered applications for automation, data analysis, or content generation, often without understanding the security or compliance risks.

How does shadow AI happen?

Shadow AI emerges due to the accessibility of AI tools, decentralized purchasing, and the rapid adoption of AI-powered SaaS applications. Many employees use AI without IT approval, either through existing software integrations or standalone AI platforms.

What are the risks of shadow AI?

Unregulated AI adoption can introduce security vulnerabilities, compliance violations, financial risks, and reputational damage. AI tools may expose sensitive data, generate biased or misleading outputs, or result in unexpected SaaS costs due to consumption-based pricing models.

Why is shadow AI becoming more of an issue?

AI adoption is growing rapidly, with employees and business units expensing AI-powered tools at increasing rates. Without visibility into AI usage, organizations risk data exposure, operational inefficiencies, and compliance challenges.

Why is shadow AI a problem?

Shadow AI removes IT oversight from AI-driven decision-making, security monitoring, and cost management. Without governance, organizations cannot assess how AI tools handle data, whether they comply with regulations, or if they introduce operational risks.

How is shadow AI different from traditional AI?

Traditional AI adoption follows structured implementation processes, with IT teams ensuring security, compliance, and integration with existing systems. Shadow AI, on the other hand, enters the organization unmanaged, often through individual employees or business units.

Are there any legal or ethical considerations associated with shadow AI?

Yes, shadow AI can lead to data privacy violations, regulatory noncompliance, and biased decision making. Many AI tools store and process data externally, potentially conflicting with industry regulations like GDPR or HIPAA. Ethical concerns arise when AI-generated outputs lack transparency or introduce discriminatory biases.

How can companies ensure compliance with policies to protect against shadow AI?

Organizations should establish AI governance policies, monitor AI adoption, educate employees on AI risks, and integrate AI oversight into security and compliance frameworks. Regular audits and SaaS Management tools can help detect unauthorized AI use.

Gain Visibility into Shadow AI with Zylo

Managing shadow AI requires clear governance, proactive monitoring, and enterprise-wide awareness. Zylo provides visibility into SaaS applications, including AI-powered tools, helping organizations track AI adoption, manage risks, and optimize spending.

Want to learn more about AI? Download the 2025 SaaS Management Index and join us at SaaSMe on April 15 for a deep dive into the era of AI and consumption pricing.