What AI Bots Know About You and Your Company

Artificial intelligence (AI) tools are becoming a natural part of our personal and professional routines. While these tools bring exciting possibilities for boosting productivity and creativity, their unsanctioned use—known as Shadow AI—can create serious challenges for individuals and organizations alike.

Understanding Shadow AI

Shadow AI refers to the use of AI tools and applications by employees without the knowledge or approval of their organization’s IT department. This includes generative AI tools, large language models (LLMs), and machine learning (ML) platforms used to automate tasks, generate insights, or simplify workflows. While Shadow AI can make work easier, it also introduces risks related to data security, compliance, and organizational trust.

Examples of Shadow AI

Shadow AI can take many forms, often driven by the desire for efficiency and innovation. Here are a few common examples:

• AI-Powered Chatbots for Customer Service: Employees might use unapproved AI chatbots to draft responses to customer inquiries. While this can speed up reply times, it risks inconsistent messaging and data exposure.

• Machine Learning Models for Data Analysis: A data analyst might use a third-party ML tool to process proprietary datasets, potentially exposing sensitive information to external servers.
• Marketing Automation Tools: Teams may adopt AI tools to schedule social media posts, analyze engagement data, or optimize email campaigns without IT’s oversight, risking non-compliance with data protection laws.
• Content Generation Applications: Writers might rely on generative AI to create reports, articles, or emails quickly, which could inadvertently result in sharing confidential details or generating biased content.
• Data Visualization Tools: Employees might use unauthorized AI-powered visualization platforms to create charts and reports, leading to inaccuracies or data leakage.

Shadow IT vs. Shadow AI

Shadow AI is a branch of Shadow IT, which includes any software, hardware, or IT resources used without formal IT approval. Examples of Shadow IT include personal cloud storage accounts, unapproved project management tools, or unauthorized messaging platforms.

Shadow AI focuses specifically on AI-powered tools. For instance, an employee might use an AI chatbot to generate a report or analyze data without considering the security implications. Unlike traditional Shadow IT, Shadow AI raises unique concerns such as data misuse, ethical risks, and unreliable outputs.

Why Employees Use Chatbots, LLMs, and Generative AI Tools

Generative AI applications are becoming increasingly popular in workplaces. Between 2023 and 2024, the percentage of employees using these tools jumped from 74% to 96%.

Employees turn to these tools for a variety of reasons:

1. Boosting Productivity: AI tools can automate routine tasks like drafting emails, analyzing data, or creating content.
2. Solving Problems Faster: These tools often bypass slower internal processes, helping employees address challenges quickly.
3. Encouraging Innovation: User-friendly, SaaS-based AI tools allow employees to experiment and innovate without waiting for IT approvals.

However, this convenience can sometimes overshadow the risks and consequences of using such tools without proper oversight.

The Alarming Statistics

Recent studies shed light on how Shadow AI is growing and the sensitive data risks it creates:

• 38% of employees who use AI tools admit to sharing sensitive work information without their employer’s knowledge, according to a survey by the National Cybersecurity Alliance (NCA) and CybSafe.
• Most workplace AI use was mostly through personal accounts rather than corporate accounts (73.8% for ChatGPT and 94.4% for Google Gemini).
• 27.4% of data sent to chatbots was sensitive, marking a 156% increase from the previous year’s report.
• A UK poll of CISOs found one in five companies experienced data leakage due to unauthorized AI use (IBM Think Blog).

Risks of Shadow AI

The risks tied to Shadow AI span several critical areas:

1. Data Breaches: Sensitive information entered into AI tools can be stored on external servers, where it may be accessed or exploited. This risk is heightened when organizations have limited visibility into how third-party AI providers handle data. Breaches can lead to financial losses, legal challenges, and erosion of customer trust.
2. Compliance Violations: The unauthorized use of AI tools often bypasses regulatory safeguards, such as GDPR, HIPAA, or CCPA. This can result in hefty fines and sanctions. For instance, failing to secure user data shared with AI tools may violate data privacy laws, causing legal liabilities and reputational harm.
3. Reputation Damage: AI-generated content or decisions can sometimes be inaccurate, biased, or unethical. For example, if an AI tool produces outputs based on flawed training data, it could propagate stereotypes or misinformation. Such outcomes can lead to public backlash, loss of client trust, and damage to a company's brand.
4. Loss of Intellectual Property: When proprietary data is shared with external AI platforms, companies risk losing control over valuable assets. AI tools may inadvertently retain sensitive information, potentially exposing trade secrets or business strategies. In competitive industries, this can undermine a company's market position.
5. Operational Inefficiencies: Over-reliance on unauthorized AI tools can lead to fragmented workflows. Without integration into approved systems, such tools might generate outputs that are difficult to verify, wasting time and resources on rectifying errors or ensuring compliance after the fact.

Keeping Your Data Safe When Using AI Tools

Organizations can take practical steps to manage Shadow AI and encourage responsible use of AI tools:

1. Set Clear Guidelines: Define which AI tools are approved and establish rules for their use.
2. Educate Your Team: Offer training on the risks of Shadow AI and how to stay compliant.
3. Monitor AI Activity: Use network tools to track unauthorized AI use and flag potential risks.
4. Create Safe Zones: Provide sandbox environments where employees can test AI tools securely.
5. Secure Your Data: Encrypt sensitive information and classify data to ensure it’s well-protected.
6. Enable Privacy Options: Encourage employees to turn off data-sharing features and regularly clear their AI usage history.

Shadow AI is a growing challenge that organizations must address with care. Employees will use AI tools whether companies approve or not, making it essential to have proactive strategies in place. By staying informed, educating employees, and putting strong safeguards in place, businesses can enjoy the benefits of AI while keeping their data and reputation secure.