Generative AI is revolutionizing workplaces, but with an underestimated threat— silent data leaks. Users inadvertently share sacred information with AI tools, often due to a misplaced trust in their effectiveness, while overlooking security threats. Unmanaged use of AI offers opportunities for trade secrets, customer information, and proprietary knowledge to be misused. Organizations must defend against such risks, as failure to address these risks could lead to breaches, regulatory issues, and damage to reputation.
Accidental Data Leaks Through AI Prompts
Employees unknowingly reveal company data by feeding AI prompts with confidential information, often without being aware of the stakes. Tools such as ChatGPT or Copilot used to aid work like summarizing a report or creating code, are frequently fed with contracts and legal documents, source code, trade secrets, or customer information to produce outputs of choice. Employees across the globe input sensitive business information into AI solutions, which increased by a staggering 485% between March 2023 and March 2024.
AI models can store sensitive data, such as proprietary business intelligence, for more general AI training and inadvertently disclose it, potentially leading to data breaches. Furthermore, if AI models are externally hosted, company data may be accessible to vendors or even competitors.
A growing cybersecurity concern is arising from accidental data leaks by employees. Bad actors are aggregating fragmented sensitive employee or customer data entered into AI prompts to create highly targeted attacks. Hackers use AI-extracted details to craft hyper-personalized phishing emails or impersonate executives, making scams more convincing and compelling.
Free-Tier AI Tools and The Risk of Shadow AI
Employees often use free-tier AI tools without IT oversight, contributing to shadow AI risks, where unauthorized AI usage bypasses security protocols and results in security vulnerabilities. IT teams face challenges in visibility, control, and governance over AI tools that employees deploy outside official protocols, leaving them with security blind spots and preventing them from identifying what data is being sent and who has access. Echoing this is the Enterprise GenAI Security Report 2025, which states that nearly 90% of logins to AI SaaS applications are conducted using personal or corporate accounts not backed by Single Sign-On (SSO), leaving security teams blind to how GenAI tools are used and what data is being shared.
Free-tier AI tools tend to have ambiguous data policies, i.e., companies may not understand how their employees’ inputs are being stored or utilized. Because IT teams don’t keep an eye on these illicit tools, sensitive company information may be unknowingly shared with AI models that could store or train on it. These tools can be lacking in GDPR, HIPAA, or other compliance controls, making companies vulnerable to legal liabilities. With most industries hounded by strict regulations on the usage and processing of data, any non-compliant data processing through untested AI tools may lead to legal penalties and loss of business reputation.
Additionally, the use of unverified AI outputs may compromise the organization’s decision-making quality. Without adequate governance, the outputs of AI models may be the outcome of biased data, an overfit model, or model drift, resulting in deceptive or unreliable outputs that deviate from the organization’s purpose or ethical guidelines.
Beyond the Individual: How AI Oversharing Puts Companies at Risk
AI oversharing isn’t just a personal mistake but a broader corporate risk. Employees sharing confidential details with AI models without understanding their data retention policies or failing to anonymize sensitive information can expose critical data to external entities, jeopardizing business operations. Proprietary business strategies or product plans shared with AI could be leaked or used to train models that competitors might access. Employees often trust AI-generated responses without verifying their accuracy, leading to the internal spread of misinformation. If these responses contain misinformation or biased content, it can harm the company’s credibility and customer trust.
Addressing the Risks of AI Oversharing
Creating an acceptable AI-use policy: Develop clear and easy-to-understand policies that define your organization’s expectations of how AI will be applied. Define what tools are acceptable, how they are to be used, and who is responsible for them. Timely and periodic notification of policies makes staff aware of and compliant with policies, minimizing confusion and misuse.
Implement AI safeguards: Organizations should educate employees on how AI solutions operate and what they do with data. Preventing users from entering confidential, proprietary, or sensitive information into open GenAI tools without IT approval will prevent unwarranted access to sensitive company information.
Security integrated into AI: A security-first approach to AI from the ground up prevents security from being an afterthought, but rather a fundamental component of the entire AI development process. What this entails is integrating protective measures into each step of the process, from data collection and model training through deployment and continuous monitoring. By incorporating security right from the start, organizations can avoid AI-facilitated leaks, manipulation, and unauthorized access.
Aligning ethical AI use with regulatory standards: Businesses can ensure ethical AI use through adversarial testing that would help uncover vulnerabilities in AI models. Human review of AI decision-making can prevent automation bias. Organizations need to bring legal compliance and ethical AI governance together to address AI risks associated with security and privacy. For instance, implementing the ISO/IEC 42001 standards involves creating an AI management system (AIMS) to manage AI governance, risk management, and accountability.
Although generative AI has tremendous benefits, organizations should exercise care with the associated risks. Through sound data governance, stringent AI policies, and responsible usage frameworks, organizations can achieve peak productivity with AI while protecting sensitive data.
About the Author
Erich Kron is Security Awareness Advocate for KnowBe4, the world-renowned cybersecurity platform that comprehensively addresses human risk management with over 70,000 customers and more than 60 million users. A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, he was a security manager for the U.S. Army’s 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and other certifications. Erich has worked with information security professionals around the world to provide tools, training, and educational opportunities to succeed in information security.
LinkedIn: https://www.linkedin.com/in/erichkron/
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