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 – Companies are deploying Gemini AI to power virtual assistants and chatbots, improving response times and reducing support costs. AI-driven systems can handle common customer queries, escalate complex cases to human agents, and personalise responses based on past interactions. While this improves efficiency, it also introduces risks, such as exposing sensitive customer data if AI models are not properly secured.‍
• Legal & Compliance – Legal teams are turning to Gemini AI to summarise lengthy documents, generate contracts, and conduct legal research. AI can quickly extract key information from regulatory updates, helping businesses stay compliant with changing laws. However, relying on AI for legal work requires caution, as errors in AI-generated content could lead to legal disputes or compliance failures.‍
• Software development – Developers are using Gemini AI to generate code snippets, suggest fixes, and automate documentation. AI-assisted coding tools speed up development cycles but can also introduce security vulnerabilities if not properly reviewed. AI models trained on public code repositories may also inherit insecure coding practices, making oversight crucial.

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?

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.

One major concern is that AI-generated responses can sometimes surface sensitive information, potentially exposing data that should remain private. This is especially risky when employees interact with AI tools without clear policies in place.

According to research, 38% of employees have admitted to sharing sensitive work information with AI without their employer’s knowledge. This makes it clear that businesses need to establish clear guidelines on what data can and cannot be processed through AI systems, reducing the risk of unintentional leaks.

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 data reaches Gemini AI, it must be properly classified and protected. Automated tools like Metomic, can label and restrict access to sensitive information, minimising the risk of exposure. Advanced data loss prevention (DLP) controls help block unauthorised sharing of confidential data across SaaS applications.

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 makes it easier to secure sensitive data, prevent AI-related risks, and stay compliant.

• Sensitive data discovery – Metomic automatically detects and classifies PII, PHI, and financial data across Google Drive, Gmail, and other connected SaaS apps to prevent exposure.
• Access control – the platform automatically enforces security policies by restricting AI interactions with confidential data and redacting sensitive information.
• Insider threat detection – Our platform automatically monitors for unusual activity, unauthorised access, and potential data leaks, triggering instant alerts.
• Compliance enforcement – Metomic helps businesses meet GDPR, HIPAA, and PCI requirements by integrating governance and security controls within Google Workspace.

By integrating Metomic, businesses can proactively protect sensitive information, reduce security risks, and ease the workload for 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.