Enterprise-ready, secure AI

While generative AI tools are enabling employees to reach new levels of productivity and creativity, it is critical to select a solution that prioritizes AI safety and security. Organizations also need to be intentional about their policies, including limiting the widespread use of “shadow” AI tools that may increase the risk of data loss.

By leveraging an integrated generative AI assistant that respects user access controls and provides granular management settings, administrators can help safeguard data and reduce the risk of data loss. Google Workspace with Gemini has a robust set of built-in threat defenses, data protections, and compliance controls, allowing organizations to get the most out of generative AI without making tradeoffs to their security posture.

With Workspace, files are securely stored and automatically versioned in a single place - Google Drive. This makes it easier for IT teams to protect business data and for Gemini to reference one single version of the document based on applied user permissions. Gemini can only retrieve data that the user has permissions to access, which minimizes additional overhead in terms of managing access and costs for separate tools with access to these data sources.

Security capabilities, such as AI classification, can help identify, manage, and protect data at scale by leveraging labels and data loss prevention (DLP) rules automatically. With content restrictions, such as Information Rights Management (disable downloading, copying, or printing) and client-side encryption, admins can also help restrict Gemini’s access to sensitive data. Take a closer look at available data security controls here.

Using the capabilities of Google Cloud Data Boundary as a foundation, Gemini in Workspace apps offers authoritative sovereign controls for customer data. Customers can confine Gemini in Workspace data processing to the US or EU. For digital resilience and survivability, customers can also store an independent copy of their Gemini in Workspace apps data in any country of their choice using local data storage.

Furthermore, for their most critical data, customers can use client-side encryption, a unique technical control that provides end-to-end encryption and places the keys solely in the control of Workspace customers to help prevent all unauthorized access, even by Google, Gemini, and other generative AI assistants in an authoritative manner.

Google does not use customers’ Workspace data to train or improve the underlying generative AI and large language models (LLMs) that power Gemini, Search, and other systems outside of Workspace without permission. This has also been previously shared in a blog post on how Google protects Workspace data and maintains these privacy commitments in the era of generative AI.

Google has taken a layered security approach introducing security measures designed for each stage of the prompt lifecycle. From Gemini 2.5 model hardening, to purpose-built machine learning (ML) models detecting malicious instructions, to system-level safeguards, we are meaningfully elevating the difficulty, expense, and complexity faced by an attacker. This approach compels adversaries to resort to methods that are either more easily identified or demand greater resources.

Our model training with adversarial data significantly enhanced our defenses against indirect prompt injection attacks in Gemini 2.5 models . This inherent model resilience is augmented with additional defenses that we built directly into Gemini. Learn more about these additional defenses.

Google Workspace with Gemini is enterprise-ready and comes with granular user access and data security controls, helping admins safely and securely deploy AI tools. Check out our step-by-step AI adoption guidelines and whitepaper for further details, find additional information in the Generative AI in Google Workspace Privacy Hub, and get started with a no-cost trial.