While generative AI tools are enabling employees to reach new levels of productivity and creativity, it is critical to select a solution that prioritises AI safety and security. Organisations 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 defences, data protections and compliance , allowing organisations to get the most out of generative AI without making trade-offs to their security posture
With Google 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 minimises 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.
Using the capabilities of Google Cloud Data Boundary as a foundation, Gemini in Google Workspace apps offers authoritative sovereign controls for customer data. For digital resilience and survivability, customers can also store an independent copy of their Gemini in Google Workspace apps data in any country of their choice using local data storage.
Google does not use customers’ Google Workspace data to train or improve the underlying generative AI and large language models (LLMs) that power Gemini, Search and other systems outside of Google Workspace without permission. This has also been previously shared in a on how Google protects Workspace data and maintains these in the era of generative
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.
Google Workspace with Gemini is and comes with granular user access and data security controls, helping admins safely and securely deploy AI tools.
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