> ## Content Index > Fetch the complete content index at: https://blog.pinta.sh/llms.txt > Use this file to discover other available public pages before exploring further. # What is Shadow AI? Causes, Risks, and Solutions - URL: https://blog.pinta.sh/what-is-shadow-ai-causes-risks-and-solutions/ - Published: 2026-07-14T05:47:49.000Z - Updated: 2026-07-14T05:47:49.000Z - Author: Pinta AI *AI is integrating into the workforce at an extreme speed as employees utilize it for development, marketing, operations, finance, and other fields. While AI integration has delivered a significant increase in productivity, it has also brought new security challenges. Teams and employees often adopt AI tools without oversight from the IT department, creating problems with guardrails, security, and visibility. This unsanctioned implementation of AI has formed a new phenomenon: shadow AI.* ## What is Shadow AI? Shadow AI is the usage of AI tools, models, or applications within an organization without approval and oversight from the IT department. In fact, [over one-third (38%) of employees](https://www.infosecurity-magazine.com/news/third-employees-sharing-work-info/?ref=blog.pinta.sh) admit to having secretly provided sensitive work information to AI without their employer. Shadow AI is growing more prominent among employees as organizations increasingly adopt AI tools. From 2023 to 2024, the integration of generative AI in companies grew from [74% to 96%](https://www.infosecurity-magazine.com/news/sensitive-data-sharing-genai/?ref=blog.pinta.sh). With more AI integration, employees are encouraged to utilize AI tools in their work, but when adopted incorrectly, shadow AI poses a potentially severe threat to the company’s security. ## Shadow IT vs. Shadow AI Shadow IT, a related term, refers to any technology usage in an organization without IT approval and oversight. Employees usually resort to shadow IT when approved measures feel too slow, ineffective, or inconvenient to use. Shadow AI falls under shadow IT and is the use of AI without IT approval or oversight. This not only means using unapproved AI tools but also using non-enterprise accounts for business matters or utilizing approved AI in prohibited ways. Shadow AI introduces new nuances surrounding AI usage and asset security in the workplace. Asset identification and management serves as the bedrock of security both for businesses and their clients. The act of sharing sensitive assets to shadow AI threatens the fundamental security of a business and frequently falls outside security compliance rules, like iso27001, iso42001, SOC2, HIPPA, and PCI/DSS, For example, [Anthropic recently changed its enterprise plan](https://www.digitaltoday.co.kr/en/view/48037/anthropic-shifts-claude-enterprise-to-usage-based-pricing-signalling-higher-costs-for-companies?ref=blog.pinta.sh) from fixed to usage-based pricing, raising costs for AI-heavy companies. As a result, many companies have begun turning a blind eye to employees who use their personal AI accounts for work in an effort to reduce the cost burden. However, this practice puts the companies’ sensitive assets at risk. ## Causes of Shadow AI In most cases, employees use shadow AI without malicious intent and are merely searching for more effective work methods. Employees turn to shadow AI occurs for a variety of reasons: - **Productivity Boost:** Employees feel a greater pressure to deliver more output following the introduction of AI into the workspace. As a result, they may use AI in order to boost their efficiency. - **AI Adoption Outpaces Policy Change:** Companies are adopting AI at a much faster rate than they are able to adjust their policies. Without AI regulations that keep pace with its developments, any usage of new AI tools will inevitably count as shadow AI. - **Lack of Awareness:** Employees may not be aware of the company’s AI tool regulations nor understand the risks of using AI without IT oversight. - **Ease of Access:** Many AI’s are browser-based and free, making them easy for employees to independently use. Furthermore, already approved SaaS’s may roll out AI services that employees then use without realizing the implications. - **Inefficient Approval Process:** Employees may find the request and approval process for new AI tools inefficient. They may then secretly adopt AI tools out of a sense of urgency. ## Risks of Shadow AI Despite employees’ overall good intentions when using shadow AI, their malpractices can carry severe risks. In fact, [a recent poll of CISO’s](https://artificialintelligenceact.eu/article/99/?ref=blog.pinta.sh) indicated that 3 quarters of respondents believe insiders pose a greater risk to the organization than external threats. - **Data Breaches:** Due to the complex ways AI processes data, IT departments have limited visibility when employees share data with an AI tool, including where the data is stored and how it’s used. If an AI gets breached, the data may become compromised. In the same poll of CISO’s, 1 in 5 UK companies suffered data leakage incidents because of employees using generative AI. - **Mishandling of Data:** Even if an AI is not breached, it may mishandle any data that employees share with it. AI providers may train their models with uploaded information, store the data indefinitely, or share it with third party vendors. - **Noncompliance with Regulations:** Many industries require companies to follow regulations that are non-negotiable and carry hefty penalties if violated. Using shadow AI can result in compliance issues, particularly regarding data protection and privacy. AI tools may not fully understand and bypass regulations, like the GDPR, HIPAA, or the DPDP Act. - **AI’s Limited Reliability:** AI can make mistakes such as fabricating facts, introducing undetected errors, and giving biased outputs. Shadow AI can reduce the quality of an employee’s work and harm the reputation of a company. - **Expansion of Attack Surface:** AI often requires widespread access to an employee’s individual assets and the company’s digital ecosystem. This far-reaching access provides shadow AI multiple avenues to damage across the company. - **Lack of Auditability:** Outputs from shadow AI aren’t easily traceable. If an issue occurs, there’s no way to know what data was used, how it occurred, and why the decision happened. A [Business Wire](https://www.businesswire.com/news/home/20250618738949/en/Zluri-Report-Exposes-Shadow-AI-Epidemic-80-of-Enterprise-AI-Tools-Operate-Unmanaged?ref=blog.pinta.sh) article reported 80% of AI tools operating within companies are unmanaged by IT or security teams. Without effective oversight, AI tools can become a double-edged sword, boosting productivity while simultaneously risking company stability. ## Examples of Shadow AI Shadow AI arises in various ways across many departments. Understanding the circumstances in which shadow AI can manifest helps highlight its ubiquity and the potential risks. - **Marketing and Sales:** A PR employee may consult an unsanctioned AI chatbot to respond to client inquiries. This practice can result in inconsistent or unreliable communication with the client, thus harming the company’s reputation. - **Data Analysis:** An analyst might use a machine learning model to generate insights on large datasets. Despite the benefits, the shadow AI may create errors or hallucinate, compromising the accuracy of the report. - **Engineering and Development:** A developer may search for ways to write code faster, troubleshoot issues, or solve other problems. However, without proper governance, they can unknowingly share sensitive information that violates regulation noncompliance issues - **Product and Strategy:** Teams might upload important information to a presentation-generation AI, such as company strategy over competitors. The AI may store the data, which can later get breached or disclosed to third parties. ## How to Manage the Risks of Shadow AI In order to effectively handle the risks of shadow AI, companies should consider multiple approaches that regulate AI usage while offering flexibility and encouragement to employees. - **Provide Alternatives:** Banning AI will only have the countereffect of driving shadow AI further underground. Employees will likely resort to AI anyway with methods that fall further outside of IT’s oversight. Providing employees with safer alternatives satisfies their productivity needs while containing AI tools within the company’s security environment. - **Gain Visibility:** Once a company implements alternatives that the IT department can overlook, it can effectively monitor the AI real-time and generate audits that record AI activity and data handling. - **Implement AI Governance:** AI governance refers to a series of standards for AI usage, including which tools are approved, what data can be used, and how to delegate accountability. An AI governance policy establishes a clear, common understanding within the company that helps employees understand the best AI practices and assume responsibility. - **Understand the AI’s:** Research on how a potential AI provider handles data can help a company choose a vendor that aligns with their interests. Common aspects include what data they collect, how long they store it, and whether they share it with third parties. - **Allocate Different Permission Tiers:** Employees with different roles may require varying levels of AI tool access. For instance, a data analyst may need complex data analysis tools, whereas a blog author may only require an LLM for writing. Strategically allocating privileges reduces a company’s attack surface . - **Build an Efficient AI Approval Pipeline:** Employees aim to incorporate new AI tools as quickly as possible. Designing a smooth process for requesting and reviewing new tools with the IT department will reduce the likelihood of employees resorting to using shadow AI as an alternative. - **Increase Employee Awareness:** Training employees about the AI governance policies can help them recognize the usage standards within the company. Additionally, reiterating the risks of shadow AI can encourage employees to follow the policies in order to protect the company. - **Add an AI Agent Runtime Security Layer:** Even with proper shadow AI prevention measures, AI agents can still get compromised by attackers or go rogue and execute unapproved actions. A runtime security layer is a software that constantly monitors an AI’s every action and intercepts any dangerous activity before it happens. ## Main Takeaways 1. Shadow AI is the usage of AI within an organization without approval or oversight from the IT department. 2. Shadow AI is in most cases the result of structural deficiencies that pressure employees to use it, not individuals’ ill intent. 3. Shadow AI can compromise a company’s data, risk noncompliance with regulations, and reduce output quality. 4. By implementing systems that foster safe employee AI-usage and contain it with security measures, companies can transform AI into a tool instead of a liability. ## The Other Threat Besides Shadow AI – And How Pinta Protects Your Company From It The truth is even if a company eradicates shadow AI, it still faces a risk: the AI’s they do allow. All AI agents, even approved ones, can still be attacked by hackers or independently mishandle your company's assets. Pinta AI prevents that. It’s an AI agent runtime security layer that always overlooks your AI agents and blocks unauthorized or high-risk behavior before it occurs. If you're interested in adopting your AI agents safely, [start a demo](https://forms.fillout.com/t/s71jJx3soTus?ref=blog.pinta.sh) with us.