Security and privacy at Google Gemini: Key insights

The use of generative AI in companies inevitably raises questions: How secure is our data? Is data being used to train public models? 

The first module of our Gemini Adoption Series focused on precisely these topics. We have summarized the most important points so that you can understand how Google Gemini will be integrated into your existing security architecture.

1. The principle: Your data remains your data

In the age of generative AI, concerns about the confidentiality of sensitive data are justified. Internal strategies or customer data must not be allowed to flow into the public knowledge of an AI. This is precisely where Google Workspace's comprehensive data protection guidelines come into play. 

The central security principle is: data must remain strictly separated.

Google consistently distinguishes between your company data and public AI models. Content from Google Workspace (e.g., in Docs, Gmail, or Drive) is not usedto train oroptimize the underlying foundation model.

This also applies to prompts that users enter within the Gemini app or sidebar, upload there, or link to Drive. There is a strict separation between customer data and AI model development.

When the Gemini model learns , it does so only in the context of your own workspace instance and specifically for your company. The data never leaves your defined domain environment.

2. Access control: Gemini as an "extension of the user"

A key concept for understanding security is that Gemini does not act as a separate user with super admin rights. It always works as an extension of the user who is currently using it.

, Gemini can only access data that the user already has access to. If an employee does not have permission to access the "Finance" folder in Google Drive, Gemini cannot answer any questions about the documents stored there.

The "oversharing" problem: If Gemini discloses information that a user should not see, the problem is never due to the AI, but rather to overly broad access rights within Google Workspace. 

3. Integration into existing security mechanisms

Gemini does not stand alone alongside Google Workspace, but is deeply integrated into existing security standards:

Good to know for system administrators:

• Data Loss Prevention (DLP): If downloading, printing, or copying is disabled for certain documents, Gemini will not access this content. This allows administrators to maintain control.
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• Client-Side Encryption (CSE): If you use your own encryption keys (Bring Your Own Key), this data is technically invisible to the Gemini model and is completely excluded.
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• Context-Aware Access: You can control that Gemini may only be used from verified devices or specific IP addresses (e.g., only from company laptops, not from private devices).

4. Configuration and admin control

For administrators, the Google Admin Console offers granular settings to control the rollout:

App-specific control: You can decide whether Gemini is active in all tools (Docs, Gmail, Drive) or disabled for sensitive areas.

Extensions & Gems: Access to extensions (such as Google Maps or YouTube) and the sharing of user-defined chat bots ("Gems") can be centrally enabled or disabled.

5. Compliance and monitoring

For companies in regulated industries, it is crucial that Gemini complies with common certifications (including ISO 27001, SOC 1/2/3). ISO 42001 certification also confirms the responsible use of AI systems.

In addition, administrators have access to comprehensive reporting tools, including tools for accurately tracking when and how Gemini was interacted with.

The same applies to Google Vault, which supports eDiscovery and retention policies that also cover interactions with Gemini.

Conclusion

The introduction of Google Gemini does not mean that existing security concepts have to be thrown out. On the contrary: since Gemini is based on the existing Google Workspace infrastructure, your defined rules on data protection, access, and compliance continue to apply without restriction.

Since the security of AI depends directly on the security of your workspace environment, it is advisable to conduct a security audit before the big rollout to ensure that permissions and approvals are configured correctly.