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AI is now fully embedded in where your work and data live. In 2025, you can’t escape its influence.
→ There are 450M monthly active users on the Gemini app (as of July 2025).
→ There are 2B+ AI ‘assists’ each month across organizations leveraging Gemini in Google Workspace.
With Gemini now completely woven into Google Workspace, teams can summarize contracts, draft proposals, analyze Sheets, and action tasks across Docs, Slides, and Gmail in seconds. It’s a massive productivity unlock, but also demands a shift in your security model.
For security practitioners operating in Google Workspace, the question isn’t “Should we use Gemini?” its → “How do we use Gemini safely without slowing the business?”
The risks are real and specific:
- Output-based data leakage that can surface sensitive information a user wouldn’t normally see.
- Agentic activity that changes permissions or content at machine speed.
- Third-party/credential misuse scenarios where a compromised agent or token leads to a takeover, and more.
In this guide, we’ll walk you through the exact risks Gemini poses to your Google Workspace security, warning signs to look out for, and how you can take a layered approach to secure your Google Workspace effectively without hindering your business’ productivity
Understanding Gemini in Google Workspace
Before we talk risk, we need to differentiate between the two ways Gemini exists in Workspace. Gemini in Google Workspace spans two interacting capability sets: LLM features (generate/summarize/analyze) and agentic features (autonomously take actions across Workspace apps). Each drives different security concerns.
LLM - This is Gemini’s LLM (large language model) that sits in the top right part of the screen in Google Drive. This LLM generates and summarizes text, analyzes Docs/Sheets, answers questions grounded in user/workspace content, and more.
Agentic - This is Gemini’s agent. Unlike the LLM, it takes steps across apps based on goals/prompts (ex: create/modify/share files, draft emails, adjust Slides, etc.)
Why Gemini Calls for a Change in Security Protocols
1. Access isn’t just about opening files anymore → Traditional security has always centered on who can open a file. But with new AI tools like Gemini, the question becomes: who can surface or summarize what’s inside that file? If the way information is retrieved isn’t perfectly matched to file permissions, sensitive details can slip out—even if the file itself is never directly opened.
2. AI moves faster than humans can react → Agent-driven tools can now edit documents, change sharing settings, or create new files within seconds. This speed can boost productivity, but it also shortens the time security teams have to detect and respond to risks.
3. Identity and context drive new risks → If an agent account is compromised - whether through a stolen token or a manipulated session - it can carry out high-impact actions across the environment. Protecting against this means enforcing least-privilege for agents, using short-lived tokens, and applying conditional access as a baseline.
What are the Security Risks of Gemini in Google Workspace?
Risk #1: Data leakage through AI outputs
What happens
When a user asks Gemini to find, summarize, or explain something, the response might pull details from files the user technically shouldn’t have access to. If retrieval isn’t perfectly aligned with existing sharing permissions, sensitive information can leak - even though the file itself was never opened.
Why this is dangerous
This type of exposure is subtle. Traditional security only looks at file opens or external shares, so it can easily miss sensitive data leaving the company through an AI-generated response.
Signals to monitor
- Retrieval or summarization events involving sensitive folders (Legal, HR, Finance).
- Mentions of sensitive entities (like customer PII or deal codes) in generated text, without a corresponding file-open.
- Spikes in file access from files containing sensitive data, indicating people are viewing it who shouldn't be.
- Supporting indicators: file access changes, unusual logins, odd geolocations, or AI-driven actions in short bursts.
Risk #2: Third-party risk & credential misuse
What happens
An attacker tricks a user (via phishing or social engineering) or compromises an AI session/token, then issues commands through Gemini using legitimate credentials. This is more common than most people realize - it even happened recently with Anthropic’s Claude.
Why this is dangerous
Because the credentials are valid, the activity looks legitimate. In reality, the attacker may be flipping permissions, creating links, or mass-sharing content at speed. Agent-driven tools expand the attack surface quickly, and without strong monitoring, a bad actor can operate as if they were a trusted user.
Signals to monitor
- Burst activity: multiple permission changes, link creations, or summaries happening in minutes across multiple sensitive drives.
- Anomalous context: late-night logins, odd geolocations, or unusual API patterns (ex. A summarize → share → export sequence).
- Actor signals: activity linked to service or agent accounts, without evidence of a parallel human session occurring.
Risk #3: Shadow data creation
What happens
Gemini can spin up new Docs, Sheets, or Slides - like drafts, summaries, or exports - that land in unexpected places. These files may bypass normal review, labeling, or retention processes.
Why this is dangerous
These “shadow” files can quietly pile up sensitive content. Because they aren’t labeled or clearly owned, they’re harder to track. Later, they can be shared or summarized without tripping existing DLP rules.
Signals to monitor
- Bursts of new file creation linked to AI activity, especially in personal drives or unusual folders.
- Sensitive data (detected via regex or classifiers) in newly created files that lack labels or clear owners.
- Rapid downstream sharing or summarization of those newly created files.
Risk #4: Autonomous asset modification & unmonitored sharing
What happens
AI agents can create, edit, or delete documents - or change file permissions - without human input. This includes external shares, especially when policies are weak or inconsistent.
Why this is dangerous
Small permission changes add up quickly, creating wide exposure before manual reviews or remediations can catch up. To truly understand the risk, you need to connect the dots between the content’s sensitivity, the actor’s behavior, and the sharing events happening around them.
Signals to monitor
- Permission changes on highly sensitive documents, especially shifts from “internal only” to “external/public.”
- Clusters of mass-sharing, public link creation, or owner changes within short time windows.
- AI-driven modifications followed by external reads or downloads.
What Companies Can Do to Protect Themselves
Adopting Gemini safely in Google Workspace comes down to four pillars: AI-aware DLP, visibility, monitoring, and granular access controls. Here are five steps every organization should prioritize:
1. Track file access, movement, and sharing at scale
Build a live picture of how data moves across Drives, Shared Drives, and external surfaces. Connect file creation, edits, permission changes, link creation, owner shifts, and external access to your organization’s users and baseline them against normal activity so you can spot exposure early - before it becomes a problem.
2. Monitor user and AI behavior to spot anomalies
Establish a baseline of how humans and AI agents usually act, then watch for deviations: burst downloads, mass sharing, late-night logins, unusual geolocations, or rapid sequences like summarize → share → export. Treat agent and service identities as you would human accounts - they can be compromised too.
3. Detect compromised accounts or sessions
Phishing and social engineering happen - especially in the most notorious data breaches that have been in the headlines recently. The key is spotting when control has been lost. Look for telltale signs: high-speed permission changes, activity spanning multiple drives within minutes, odd devices or geolocations, or agent strings in your logs.
4. Use automated workflows to contain risk in real time
Manual response can’t keep up with AI speed. Leverage automated workflows and playbooks that can quarantine risky files, revoke sessions, unshare or expire links, and alert owners or admins. Engage security teams for manual review only when necessary - ensuring security doesn’t slow down the business.
5. Prioritize with contextual risk scoring
Not every alert is urgent. Combine factors like data sensitivity, actor trust, action speed, and exposure pathway into a risk score that collects data on your employees. Who is doing the action? Does it make sense based on their role, scope, and usual business behavior? Context is what ties retrieval, generation, and sharing events together.
DoControl in the Mix
DoControl excels in all these key steps - and then some. We were purpose built for protecting Google Workspace environments from the incidents and exposures Gemini is now making a real possibility.
Our approach is simple: we’re a best in class SaaS driven DLP, that uses AI, context, automated workflows, and scalable remediation to protect your organization's most sensitive data.
The result? Teams can move fast and collaborate freely - while security teams stay firmly in control.
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Conclusion
Gemini is transforming how work gets done in Google Workspace - and your security model needs to evolve with it.
The way forward is practical and proven: AI-aware DLP, continuous visibility into data flows, behavioral monitoring for users and agents, fast anomaly detection, contextual risk scoring, and automated remediation, to focus your response where it matters most.
With these controls in place, security teams can confidently enable Gemini - achieving productivity and innovation without giving up governance.
Purpose-built solutions like DoControl bring all these capabilities together so you can share freely, control seamlessly.
