Gemini has become deeply embedded across Google Workspace, enabling automation, summarisation, and AI-assisted decision-making. But as Gemini’s capabilities have expanded, including retrieval across shared drives, long-context reasoning, and deeper Workspace integration, so has its blast radius.

The real risk isn’t Gemini itself. It’s the unmanaged SaaS data sprawl that Gemini can suddenly surface, summarise, or expose. Sensitive files employees forgot about five years ago can now reappear instantly in a prompt response.

This article breaks down the current, up-to-date security risks of using Gemini in 2026, how new regulations like the EU AI Act and DORA change the stakes, and how organisations can adopt AI safely with modern AI governance and SaaS-native DLP controls.

Key points

• Gemini AI launched in 2023, to compete with ChatGPT, and has since expanded significantly across Google Workspace, with new features such as long-context reasoning, updated Workspace data controls, and tighter admin policies. These advancements increase productivity — but also widen the potential exposure surface if sensitive data exists in shared drives, email, or collaboration tools.
• Core security concerns remain unchanged: Gemini can surface or summarise sensitive information employees already have access to, making historical data sprawl a primary risk.
• Regulatory requirements have evolved, with the EU AI Act, DORA enforcement, and increased scrutiny from US regulators requiring clearer visibility into how AI tools handle data.‍
• Organisations need strong AI governance foundations, including data classification, access control, monitoring of AI interactions, and clear usage policies.‍
• ‍Metomic supports safe Gemini adoption by discovering and minimising sensitive data in the SaaS ecosystem, enforcing access policies, and monitoring AI-related data flows to prevent unintended exposure.

‍Gemini AI is one of many AI tools that is quickly becoming invaluable to businesses, with 110 companies already using it to automate tasks, generate reports, and improve customer interactions. While it’s designed to boost efficiency, like any AI system handling sensitive information, it comes with security risks.

With AI tools increasing productivity by up to 66%, their adoption is inevitable. Rather than resisting the shift, businesses should instead concentrate on reinforcing their security. A critical first step is understanding how Google’s Gemini AI processes and stores data. Without proper safeguards, organisations risk data leaks, compliance violations and unintended exposure.

In this article, we’ll look at the main security risks with using Gemini AI and provide actionable steps you can take as a security professional to protect your organisation’s data.

How businesses are using Gemini AI

Gemini AI is a flexible and intuitive tool that can be used across a wide range of industries. It is now embedded across Google Workspace, supporting a range of everyday tasks including summarisation, drafting, reporting and data retrieval. As organisations look to scale AI-driven productivity, Gemini has become a central tool for automating routine workflows and improving operational efficiency across teams.

Since January 2025, Google has expanded Gemini’s availability across Business and Enterprise Workspace plans, making AI features accessible to more employees by default. With deeper integration into Gmail, Docs, Drive, Sheets and Chat, Gemini can now access and summarise a broader set of organisational information.

AI adoption is growing rapidly, with 65% of organisations now using AI in at least one business function. As more businesses turn to Gemini AI, understanding and managing these risks is becoming more important.

Here’s how Gemini AI is making an impact in different sectors:

• Finance teams use Gemini to accelerate reporting, support customer operations, and analyse large sets of financial information. AI-assisted tools help teams detect anomalies, prepare documentation, and streamline internal workflows. However, financial data is highly sensitive including PII, PCI, and bank account details. Gemini’s ability to surface information across shared repositories means organisations must ensure strong controls to prevent unauthorised access or accidental disclosure. Financial data is a prime target for attackers, and AI models must be carefully monitored to prevent data leaks and bias in decision-making.
• Healthcare organisations use Gemini to assist with documentation, summarise patient records and streamline administrative tasks. These capabilities support staff workloads but also increase the importance of maintaining strict controls around patient information. Given the sensitivity of healthcare data and regulatory requirements such as GDPR and HIPAA, organisations must ensure that only authorised personnel can access and process information through AI tools.Some providers are also exploring its use in diagnostics, though this raises concerns about accuracy, liability, and data privacy. As AI adoption grows in healthcare, securing patient data against leaks and unauthorised access remains a top priority.‍
• Customer support teams use Gemini assistants to draft responses, summarise tickets, and speed up resolution. But real deployments show clear risks: AI chatbots have exposed sensitive customer records when given broad access to legacy tickets or shared databases. A 2025 incident saw a major chatbot builder leak hundreds of thousands of support records due to insecure defaults. To use AI safely, organisations must tightly restrict data sources, enforce role-based access, log all AI interactions, mask sensitive fields, and review outputs — or risk regulatory, security, and reputational fallout.‍
• Legal and compliance teams use Gemini to review documents, extract key information and accelerate regulatory research. These capabilities can support faster decision-making, but organisations must carefully manage how sensitive contracts, legal correspondence and internal documents are accessed and processed. Ensuring proper data handling and review processes is essential to avoid inaccuracies or unintended exposure.‍
• Engineering teams use Gemini AI to support code generation, documentation and knowledge sharing. When AI tools have access to internal codebases, technical documentation, or developer chats, they may unintentionally surface sensitive information such as credentials, API tokens, architectural diagrams, or proprietary logic. Organisations also need to ensure that AI-generated code meets internal security standards and does not introduce vulnerabilities from public training data. Reviewing AI-generated content and implementing clear usage policies helps maintain code quality and prevent accidental exposure of sensitive information.

For a full list of how Gemini AI is being used by businesses, read more here.

What are the data security risks of using Gemini AI?

Using Gemini introduces several data security considerations that organisations must actively manage. The most common risk is unintentional exposure of sensitive information. When employees use Gemini to draft messages, summarise documents, or analyse files, the model may process customer data, internal records, or credentials unless guardrails are in place.

Another concern is AI over-reach: Gemini can surface information from shared drives, tickets, or historical documents that employees may not realise they have access to. This increases the impact of existing permission gaps.

There are also risks related to model outputs. AI systems can generate inaccurate or overly revealing responses, potentially including sensitive details drawn from prior inputs. Insider misuse, whether intentional or accidental, still remains a factor, particularly in environments without monitoring of AI interactions.

To mitigate these risks, organisations should implement strict data access policies, limit which datasets Gemini can reach, monitor prompts and outputs, and ensure sensitive fields are masked or removed before being processed by AI tools.

1. Sensitive data exposure

AI tools like Gemini AI make it easy to automate tasks and generate insights, but they also create risks around sensitive data. Businesses may enter confidential information—such as customer records, financial data, or internal reports—without realising that it could be stored or even used for training future AI models.

According to a recent survey 57% of enterprise employees using generative-AI assistants at work admitted to entering confidential company data into publicly available AI tools. It is essential for businesses to set clear guidelines on what information can and cannot be processed through AI systems to minimise the risk of accidental data exposure.

2. Lack of control over data processing

Cloud-based AI solutions like Gemini mean businesses don’t always have full visibility into how and where their data is stored or used. This creates challenges, particularly when handling sensitive information or meeting compliance requirements.

According to data and analytics firm Dun & Bradstreet, 46% of organisations are concerned about data security risks, while 43% are concerned about potential data privacy violations when implementing AI.

Regulations like GDPR and DORA require businesses to protect personal and financial data, but without direct control over AI models and infrastructure, compliance can become difficult.

As AI adoption grows, businesses need clear policies on data handling and transparency to reduce these risks.

3. Insider threats and accidental data sharing

AI tools like Gemini make work easier, but they also come with hidden risks—especially such as insider threats and accidental data sharing.

Research shows that 56% of breaches were due to negligent insiders, while 26% came from malicious insiders. That’s more than half of breaches happening because someone inside the company made a mistake.

In the context of AI, employees might upload sensitive files or confidential business information without realising how it’s stored or used later on. And without the ability to set up proper controls, AI-generated insights could end up in the wrong hands.

Clear policies, proper training, and strict access controls are key to stopping sensitive data from being shared—intentionally or not.

4. Regulatory and compliance challenges

For businesses in highly regulated industries such as finance, healthcare, and law, AI tools introduce additional compliance hurdles.

These industries not only handle unprecedented amounts of highly sensitive data, but at the same time need to ensure that they are staying compliant with regulations. Any misalignment between AI and industry regulations can result in serious consequences that can have a lasting impact.

The global average cost of a data breach now stands at $4.88 million , but the numbers climb even higher in industries with stricter regulations. In healthcare, a breach costs an average of $9.77 million, while financial organisations face an average of $6.08 million per incident.

As AI adoption accelerates, businesses must keep up with evolving regulations. That means regularly reviewing how AI tools handle data, ensuring compliance with laws like GDPR and DORA, and implementing in place to avoid damaging and costly mistakes.

How to reduce security risks when using Gemini AI

1. Classify and protect sensitive data

Before information is processed by Gemini, it should be accurately classified and governed. Tools like Metomic can automatically identify sensitive data, apply the right labels, and enforce access restrictions to prevent unnecessary exposure. Integrating modern DLP controls adds an additional safeguard by blocking unauthorised sharing of confidential information across SaaS applications, reducing the likelihood of accidental leaks.

2. Restrict AI access and monitor usage

Without proper controls, employees may unintentionally share sensitive data with AI tools, increasing the risk of leaks and misuse.

Employees will find ways to use AI tools, even if access is restricted— blocking one platform just pushes them to another. Instead of trying to ban AI, the focus should be on strengthening security awareness. Clear, transparent processes, and smart safeguards can help protect sensitive data without disrupting productivity.

Businesses should also set clear guidelines on what data employees can input into Gemini AI. Despite these concerns, only around 10% of companies have a formal AI policy in place , leaving many organisations vulnerable to security and compliance failures.

3. Real-time security monitoring and alerts

The sooner a security issue is spotted, the less damage it can cause. Real-time monitoring helps detect unusual activity, unauthorised access attempts, and potential data leaks before they escalate.

Security teams rely on automated alerts to detect threats, but an overload of false positives (some rates being as high as 90% ) can make it harder to spot real attacks. In 2024, organisations took an average of 194 days to detect a data breach — often because critical threats were buried in the noise. Real-time monitoring and smarter alert prioritisation help teams cut through the clutter, ensuring genuine risks get immediate attention.

4. Employee training on AI security risks

Human error remains one of the biggest security risks responsible for 82% of breaches. Without proper training, employees might inadvertently expose sensitive information. Yet, 55% of employees using AI at work have no training on its risks, leaving businesses vulnerable to data leaks and compliance failures. Regular security awareness programmes and real-time security prompts are essential to help employees navigate AI tools safely and mitigate potential risks

How Metomic can help

Metomic gives security teams the visibility and control they need to use Gemini safely. By cleaning up sensitive data across SaaS tools and enforcing AI-specific policies, it reduces the blast radius of AI adoption and removes uncertainty around compliance.

• Sensitive data discovery – Metomic automatically identifies and classifies PII, PHI, financial records, secrets, and client data across Google Drive, Gmail, Slack and other connected SaaS apps. This helps teams eliminate hidden exposure before Gemini can surface it.
• AI-safe Access control – The platform enforces least-privilege policies and prevents AI tools from accessing or processing restricted data. Sensitive fields can be redacted automatically, ensuring only safe information reaches Gemini.
• Insider risk monitoring – Metomic detects unusual sharing patterns, overshared documents, and potential data leaks in real time, flagging issues before they escalate.
• Compliance enforcement – Built-in policies support GDPR, HIPAA, PCI and emerging AI-governance requirements, with audit-ready reporting.

By integrating Metomic, organisations can deploy Gemini with confidence, reduce the risk of accidental data exposure, and cut down the manual workload placed on security teams.

Getting started with Metomic

Adding Metomic to your security stack makes it easier to protect sensitive data, enforce AI access controls, and reduce the workload for security teams.

Here’s how to get started:

• Identify exposure risks: Use our free security assessment tools to scan for sensitive data across your SaaS applications and understand where AI access could create vulnerabilities.
• See how it works: Book a personalised demo to explore Metomic’s features, see how it integrates with your existing security setup, and learn how it helps prevent unauthorised AI interactions.
• Talk to our team: Have specific concerns about AI security? Talk to our experts. They can guide you through the setup process and ensure Metomic meets your organisation’s needs.