Main takeaways from this article:
Shadow AI refers to unauthorized AI tools and technologies adopted without organizational oversight, often driven by the increasing accessibility of solutions like generative AI that users can leverage without technical expertise.
Unlike shadow IT, shadow AI's unpredictable and evolving models create a wider attack surface. Its adoption spans all roles, often by users unaware of proper security practices, increasing potential vulnerabilities.
Shadow AI presents risks like data exposure, biased or misleading outputs, and failure to meet regulatory compliance, which can lead to financial, legal, and reputational damage.
Banning AI outright can backfire, pushing users toward unauthorized tools. Instead, organizations should adopt responsible AI governance, balancing security needs with productivity and innovation benefits.
Effective shadow AI management requires incremental governance, employee engagement, cross-department collaboration, and regular auditing to align AI use with organizational goals while minimizing risks.
What is shadow AI?
Shadow artificial intelligence (AI) refers to the use of AI tools without an organization’s visibility or governance. In other words, employees use AI tools in their day to day without a security review by their company.
AI tools are an increasingly common part of workflow, with 75% of workers using it, according to Microsoft. Of those workers, 78% of them are “bringing their own AI tools to work.”
The accessibility of AI through open-source datasets and generative AI (GenAI) tools has made shadow AI emerge, enabling individuals to use these technologies without technical expertise.
Take ChatGPT as an example: within a year of its launch, it grew to 100 million weekly users. While its capabilities offer significant productivity and personalization benefits, its use poses security risks. OpenAI, the company behind ChatGPT, uses interactions for model training unless users opt out, creating the potential for private or sensitive training data to be inadvertently exposed. This has prompted many organizations to draft AI-specific security policies to mitigate such risks.
Banning AI outright, however, can backfire, leading to greater use of unauthorized tools and missed opportunities. To safely unlock AI's business potential, organizations must strike a balance. Encouraging responsible adoption within secure frameworks can curb the spread of shadow AI while leveraging its transformative benefits.
According to Gartner, 41% of employees in 2022 installed and used applications that were beyond the visibility of their IT departments. This figure is forecasted to rise to 75% by 2027.
Gartner
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Shadow AI vs. shadow IT
Shadow IT refers to the general use of unauthorized technology, like apps or devices, outside an organization’s IT framework. It often stems from employees finding workarounds to meet their needs but can create security vulnerabilities.
Shadow AI is similar to shadow IT, but specifically focusing on unauthorized AI programs and services. It involves unpredictable and constantly evolving models, making them harder to secure. Governance frameworks for AI are still being developed, adding to the difficulty.
Unlike shadow IT, which is often limited to developers or tech-savvy users, shadow AI is adopted by employees across all roles—most of whom lack the knowledge to follow proper security practices. This creates a much wider and less predictable attack surface.
Addressing shadow AI requires a focused approach beyond traditional shadow IT solutions. Organizations need to educate users, encourage team collaboration, and establish governance tailored to AI’s unique risks.
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Shadow AI risks
Without proper oversight, Shadow AI poses significant risks that are as far-reaching as its attack surface. Let’s delve deeper into the top three risks:
1. Data exposure and loss of confidentiality
Shadow AI users may unintentionally leak private user data, company data, and intellectual property when interacting with AI models. These models can be trained on users’ interactions, such as prompts for large language models, and sensitive customer data provided by users can become accessible to third parties who haven’t signed NDAs or non-compete agreements. Such scenarios compromise confidentiality and result in potential data breaches, with malicious actors exploiting the exposed information for harmful purposes.
Here’s one real-world example: Multiple Samsung employees pasted lines of proprietary code into ChatGPT to streamline their work. Because ChatGPT can be trained on user input unless they have opted out, there’s a chance that Samsung’s code could be included in future model releases.
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2. Misinformation and biased outputs
Users of shadow AI systems may act on misinformation generated by their interactions with AI models. GenAI models are known to hallucinate information when they’re uncertain about how to answer. One prominent example? Two New York lawyers submitted fictitious case citations generated by ChatGPT, resulting in a $5,000 fine and loss of credibility.
Bias is another pressing issue with AI’s information integrity. GenAI models are trained on data that is often biased, leading to equally biased responses. For instance, when prompted to generate images of housekeepers, Stable Diffusion demonstrates racial and gender bias by almost always generating images of black women.
If users rely on the output of AI models without fact-checking responses, the consequences can include financial and reputational hits that are difficult to bounce back from.
3. Non-compliance with regulatory standards
Shadow AI is not yet protected by auditing and monitoring processes that ensure regulatory standards are met. Around the world, new GDPR regulations related to AI and new AI-specific data protection regulations are being drafted and released, such as the EU AI Act. Organizations doing business in Europe must be ready to comply with these new standards. And future compliance requirements are one of the “known unknowns” of AI security that add to the complexity of the field.
Regulatory non-compliance poses legal risks as well as risks to brand image: The public’s opinion on the use of AI can change quickly, after all. When it comes to costs, it’s fair to estimate that due to its complexity and unpredictability, the financial costs of shadow AI will surpass those of shadow IT.
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The benefits of embracing and managing shadow AI technologies
Addressing shadow AI directly allows organizations to streamline operations and empower teams across departments. Here’s what your organization can gain:
Improved process efficiency
AI tools take repetitive tasks like data entry or scheduling off your team’s plate, freeing them to focus on work that matters most. Automating these processes not only speeds up operations but also reduces errors, making workflows smoother and more reliable.
Enhanced personal productivity
AI can help employees get more done in less time by automating routine tasks or assisting with complex ones. Whether it’s generating creative ideas or analyzing data, AI allows individuals to focus on what they do best, boosting productivity across the board.
Better customer engagement
With AI-powered insights, you can tailor customer interactions to their unique preferences and needs. Personalized recommendations and proactive support improve the overall experience, leading to stronger relationships and long-term loyalty.
Support for security and GRC teams
AI can play a crucial role in strengthening security and compliance efforts. It helps identify potential threats, streamline incident response, and close gaps traditional approaches might miss. This extra layer of support allows your security teams to stay ahead of risks.
Enhanced policy evaluation
The use of shadow AI often highlights weaknesses in existing policies. Analyzing how and why employees turn to unauthorized tools provides valuable insights for refining governance frameworks, making them more practical and effective.
When managed thoughtfully, shadow AI becomes an asset rather than a liability, helping your organization work smarter while staying secure.
Better customer engagement
With AI-powered insights, you can tailor customer interactions to their unique preferences and needs. Personalized recommendations and proactive support improve the overall experience, leading to stronger relationships and long-term loyalty.
Support for security and GRC teams
AI can play a crucial role in strengthening security and compliance efforts. It helps identify potential threats, streamline incident response, and close gaps traditional approaches might miss. This extra layer of support allows your security teams to stay ahead of risks.
Enhanced policy evaluation
The use of shadow AI often highlights weaknesses in existing policies. Analyzing how and why employees turn to unauthorized tools provides valuable insights for refining governance frameworks, making them more practical and effective.
When managed thoughtfully, shadow AI becomes an asset rather than a liability, helping your organization work smarter while staying secure.
Shadow AI can even be a benefit by highlighting places where current GRC policies are failing so that organizations can better evaluate and enhance existing governance processes.
10 best practices to mitigate shadow AI
Here are 10 practical steps to mitigate shadow AI and ensure its safe integration into your workflows.
1. Define your organization's risk appetite
Determining your organization's risk tolerance is critical before you deploy AI solutions. Consider factors such as compliance obligations, operational vulnerabilities, and potential reputational impacts. Evaluate factors like compliance obligations, operational vulnerabilities, and potential reputational impacts. This analysis will highlight where strict controls are needed and where more flexibility can be allowed.
Once your risk appetite is clear, use it to guide AI adoption. Categorize applications based on their level of risk and start with low-risk scenarios. High-risk use cases should have tighter controls in place to minimize exposure while allowing innovation to thrive.
2. Adopt an incremental AI governance approach
Taking on too much at once with AI governance can overwhelm teams and create resistance. Start small by piloting AI tools in controlled environments or within specific teams. As results are observed, refine your governance approach and expand adoption gradually.
This measured strategy minimizes risks and builds confidence among employees. Teams can provide feedback during each phase, enabling governance policies to evolve in a way that aligns with both organizational needs and practical realities.
3. Establish a responsible AI policy
Employees need clear guidance on acceptable AI use, which makes a well-defined Responsible AI policy essential. This policy should outline the types of data that can be processed, prohibited activities, and security protocols everyone must follow. It should also address data management practices to ensure sensitive information is handled securely and consistently, with a strong emphasis on maintaining data privacy. Additionally, require all new AI projects to undergo review and approval by your organization's IT department before implementation.
Regular updates to this policy are equally important. AI technology evolves rapidly, and so do the risks it presents. Treat the policy as a dynamic resource that adapts to new challenges and opportunities, keeping it aligned with the organization’s needs and security priorities.
4. Engage employees in AI adoption strategies
Employees often adopt shadow AI tools to fill gaps in approved technology. Hosting surveys or workshops can uncover the tools they’re using and the reasons behind them. This insight helps pinpoint governance weaknesses and identify opportunities to meet their needs with sanctioned solutions.
Involving employees helps ensure AI initiatives align with their workflows. This collaboration makes governance strategies more practical and reduces reliance on unauthorized tools.
5. Collaborate across departments to standardize AI usage
AI adoption touches multiple areas of an organization, so ensuring all teams are aligned is critical. IT, security, compliance, and operations must work together to create consistent standards for selecting, integrating, and monitoring AI tools.
Unified policies simplify oversight and reduce risks. When every department follows the same rules, gaps in security are easier to spot, and the overall adoption process becomes more streamlined and efficient.
6. Provide training and enable adoption support
Educating employees about AI risks and best practices is one of the most effective ways to reduce shadow AI. Focus on practical guidance that fits their roles, such as how to safeguard sensitive data and avoid high-risk shadow AI applications.
Alongside training, offer ongoing support like help desks, detailed guides, or digital adoption tools. These resources empower employees to use AI tools responsibly while giving them the confidence to navigate challenges securely.
7. Prioritize AI solutions by risk and business impact
Not all AI tools are created equal, so focus first on low-risk, high-value applications. Automating simple tasks without handling sensitive data can yield quick wins with minimal exposure. These tools serve as a foundation for demonstrating the benefits of AI to your teams.
After establishing a strong governance framework, you can introduce more advanced tools. For high-risk applications, apply stricter controls to effectively manage their business value against potential risks.
8. Regularly audit usage of shadow AI tools
Unauthorized AI usage can remain hidden unless actively monitored. Conduct routine audits to identify shadow AI tools, assess their data security risks, and decide whether they should be removed or formally adopted into the approved technology stack.
These audits also reveal patterns in how employees use AI, providing valuable insights for refining governance. If certain tools are repeatedly used without approval, it may signal a gap in your sanctioned offerings that needs addressing.
9. Establish clear accountability for AI governance
Assigning accountability ensures AI policies are implemented and monitored effectively. Designate a team or leader responsible for overseeing AI usage, maintaining compliance, and managing risks. Make their role and authority clear across the organization.
Having a dedicated point of contact for AI governance simplifies communication and decision-making. This clarity helps address risks promptly and ensures consistency in enforcing policies.
10. Continuously update AI governance processes
AI technology changes rapidly, and governance must evolve alongside it. Schedule regular reviews of your policies to incorporate new best practices, address emerging risks, and align with evolving business goals.
Involve cross-departmental teams and solicit employee feedback during updates to keep your governance processes remain relevant and practical, creating a culture of adaptability that keeps your organization ahead of potential challenges.
Best practices for balancing turnaround and risk
One strategy is to introduce AI solutions based on turnaround and likelihood of risk. To define AI solutions of interest, the committee should solicit feedback from employees through workshops and surveys.
First, introduce AI solutions of interest that have a high turnaround and come with low risk. These can be on-prem or third-party solutions that do not keep conversation logs, do not have access to queries, and do not use user interactions for model training unless explicit consent is given. Next, start planning for high turnaround AI solutions that have high risk while developing AI solutions that have low risk in the meantime.
For less sensitive workflows, a good solution is to provide gated API access to existing third-party AI systems that can introduce guarantees for data confidentiality and privacy requirements for both inputs and outputs. For more sensitive workflows, the safest approach is to develop AI solutions where the data lives since there is no risk of transferring data to external systems.
To complete support for a new AI offering, relevant information needs to be shared in a digital adoption platform that can help gather insights and put walkthroughs, workflows, and contextual help in place to ensure correct usage.
Uncover shadow AI with Wiz
Organizations can’t protect themselves from what they don’t know about. To uncover shadow AI, encouraging and supporting transparency within and across teams is the first step. The next step is to set up an automated solution that can detect unauthorized implementation and usage of AI solutions.
Wiz is the first cloud native application protection platform (CNAPP) to offer AI risk mitigation with our AI Security Posture Management (AI-SPM) solution. With AI-SPM, organizations gain full visibility into AI pipelines, can detect AI misconfigurations to enforce secure configuration baselines, and are empowered to proactively remove attack paths to AI models and data.
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