.png){kind=small}
In 2025, the most significant evolution in enterprise artificial intelligence isn’t generative anymore - it’s agentic.
Tools like Google’s Gemini and Microsoft Copilot have moved beyond passive Q&A chatbots into something far more powerful: AI systems that can act on behalf of users.
They write documents, schedule meetings, query data, and interact with enterprise SaaS apps - often with the same level of identity and access as the actual humans they’re meant to assist.
This evolution comes with a cost. As Gemini becomes deeply embedded within Google Workspace, its access to sensitive company data creates a new, largely unmonitored attack surface.
Security teams that once focused on user accounts, OAuth permissions, and SaaS misconfigurations must now consider a new variable - autonomous AI behavior.
This article explores the hidden Gemini AI security risks that emerge when autonomous agents begin making real decisions within enterprise environments - and why identity and SaaS security must evolve to keep pace.
What Is Agentic AI, and Why Is It Reshaping Enterprise Security?
For years, enterprises have focused on securing generative AI - models that generate text, images, or code. The risks were relatively contained: prompt injections, data leakage, or model hallucinations.
Agentic AI is a whole different beast. Unlike traditional generative models, agentic systems like Gemini 2.0 are fully embedded in your organization’s Google Workspace environment. They can interpret context, access company data, and execute tasks autonomously. They don’t just respond - they act.
That shift transforms Gemini from a productivity tool into an active participant in enterprise workflows. Each action - from fetching a document to modifying a calendar - involves delegated permissions, OAuth tokens, and user impersonation at the SaaS layer.
If those interactions are compromised or abused, attackers no longer need to breach user credentials; they only need to compromise the AI’s session or exploit its context window.
As enterprises rush to integrate Gemini deeper into their operations, the line between user and agent is blurring. And with that blurring comes a new security frontier: protecting not just people, but the autonomous systems acting on their behalf.
How Is Identity An Attack Vector in the Gemini AI Era?
Every major security breach in the past couple months has shared a common thread: identity compromise. Most notably, the recent Salesforce attacks that swept the nation, leaving a trail of destruction for enterprises everywhere.
In the age of agentic AI, that threat vector has multiplied.
When Gemini operates inside an enterprise, it does so through identity. It uses the permissions, OAuth tokens, and access scopes of the user/employee it assists.
In other words, Gemini inherits the digital identity of every employee it serves - and with it, their access to data, SaaS apps, and collaboration tools.
That means Gemini doesn’t just read information - it can act on it. It can send emails, move files, and modify documents, all while authenticating as a trusted user.
And, while these actions are governed by permissions and API scopes, most enterprises have no visibility into what Gemini does under the hood, or how those delegated privileges are being exercised.
The result is a blind spot: a powerful, persistent, and semi-autonomous identity layer that operates beyond traditional security controls.
If a threat actor compromises the AI’s API keys, intercepts OAuth tokens, or manipulates its input context, they could effectively hijack Gemini’s session - gaining indirect access to corporate systems without ever touching a human account!
This new reality introduces several identity-driven risks that CISOs can’t ignore:
- AI session hijacking: Attackers could exploit session tokens or cached credentials to impersonate Gemini within enterprise systems.
- Overprivileged scopes: Gemini often requests broader API access than necessary, creating unnecessary exposure across Workspace and connected SaaS apps.
- Cross-tenant impersonation: If Gemini is integrated across multiple Google Workspace domains, a compromised instance could act across tenants - an attacker’s dream scenario.
- Lack of activity logging: AI-initiated actions often lack the granular audit trails that human actions produce, complicating detection and forensics.
In short, identity is no longer just about who the user is - it’s about what their AI can do in their name.
This isn’t just a hypothetical scenario used within the industry to instill fear - it already happened a few weeks ago with Anthropic’s agentic AI, Claude.
Cybercriminals started using Anthropic’s technology to launch sophisticated cyberattacks, extortion campaigns, and even fraud schemes.
Hackers used Claude to conduct “vibe hacking” - targeting at least 17 organizations, including government entities, while using AI to decide what data to steal and how much ransom to demand.
These malicious actors were using AI not just as a tool for automation, but as an active participant in the attack.
As Gemini continues to evolve and take on more responsibilities, the enterprise’s traditional identity perimeter begins to erode, replaced by a dynamic mesh of human and AI identities that must be secured equally!
How Can Gemini AI Lead to SaaS Data Exposure?
Inside Google Workspace, Gemini has visibility into every object it’s allowed to see: documents, emails, chat messages, and shared drives.
When users ask Gemini to “summarize a client deal,” “draft a renewal proposal,” or “analyze team performance,” it ingests sensitive content to perform those tasks.
Each prompt becomes a data transaction, where confidential information moves through Gemini’s context window, temporary memory, and potentially through connected APIs.
The problem isn’t just what Gemini reads - it’s what it might retain or expose.
Even with strong data governance, prompt injection and context manipulation attacks can trick Gemini into revealing data beyond its intended scope.
A carefully crafted prompt could cause Gemini to summarize or output confidential data from other documents, or even from other users’ sessions, if isolation isn’t perfectly enforced.
And since Gemini often communicates across multiple Google Workspace tools - Drive, Docs, Gmail, Calendar, and Meet - each integration represents a new SaaS-to-AI data bridge that can be exploited.
Beyond the risk of direct data leakage or compromise, enterprises must also account for accidental exposure.
Many organizations using Google Workspace are unaware of how their sharing permissions have evolved over time - especially those that have been around for a while and have accumulated mass amounts of data. Employees share documents internally and externally, but don’t revoke access once it’s no longer needed.
As a result, former employees and contractors may retain access to critical information they shouldn’t, while current employees can access sensitive data in the absence of proper access controls.
When a user asks Gemini to find, summarize, or explain something, the response might pull details from files the user shouldn’t have access to - but they do - since there's no formal, automated, or governed access controls in place.
If retrieval isn’t perfectly aligned with existing sharing permissions, sensitive information can leak - even though the file itself was never opened.
These risks underscore a hard truth: in an agentic ecosystem, data doesn’t just live in SaaS anymore. It flows dynamically between humans, AI agents, and cloud systems - often outside the visibility of traditional DLP or CASB tools.
To manage this new attack surface, Gemini must be treated as a full-fledged identity - with the same protections, monitoring, and access controls applied to any human user or application.
Without that layer of visibility, enterprises can’t answer these three critical questions:
- What data did Gemini see?
- What data did it expose to employees?
- Where did the data go next?
How Can CISOs Secure Gemini and Other Agentic AI Systems Today?
Enterprises can’t simply disable Gemini or Copilot. These tools are rapidly becoming essential productivity tools for everyone. The challenge for security leaders isn’t stopping agentic AI; it’s governing it.
To mitigate Gemini AI security risks, CISOs must begin treating AI assistants as first-class entities in the enterprise threat model - with their own identity, access, and data flows to secure. Here’s how to get started:
1. Treat AI Agents as New Identities
Manage nonhuman identities - like Gemini - with the same rigor, protection, and governance applied to human users.
2. Implement SaaS Access Visibility
Ensure continuous visibility into which SaaS applications Gemini can access, what data it can reach, and whether those permissions are justified.
3. Apply the Principle of Least Privilege to Data Scopes
Gemini often has access to everything in your Google Workspace. Enforce automated policies to limit, revoke, and remediate file sharing when access is too broad or external.
How Does DoControl Protect Enterprises from Gemini AI Security Risks?
As agentic AI becomes embedded in daily enterprise workflows, traditional security controls struggle to keep up. Most visibility tools were built for static SaaS connections - not for AI systems that dynamically act, learn, and adapt.
This is where DoControl provides the missing layer of protection.
1. Data Access Governance for AI-Driven Environments
DoControl gives enterprises a unified view of every data event across SaaS applications - files, permissions, sharing links, and user activity - including actions performed by AI systems like Gemini.
Our Data Access Governance module continuously maps how information moves across users, apps, and now: AI. By enforcing granular access controls and policies, security teams can ensure Gemini only interacts or shows data to users that aligns with compliance and business intent.
2. Context-Aware Anomaly Detection
When dealing with an account compromise, the most important part of detection is knowing the context surrounding the actions that are happening. The power of DoControl lies in context. We correlate activity across users, groups, and applications - identifying not just what happened, but why.
When a hacker compromises Gemini and impersonates a legitimate user, DoControl evaluates the full context to detect and contain the threat. It asks critical questions such as:
- Who is performing the action?
- Is the activity originating from a suspicious or unfamiliar location?
- Do the behaviors - such as burst downloads or mass external sharing - align with this user’s normal job function, or do they indicate a compromised AI agent acting maliciously?
If the answer is “no,” DoControl automatically triggers alerts or enforcement actions - such as revoking access, pausing file sharing, or disabling risky integrations.
By combining contextual intelligence with automated remediation, DoControl gives organizations a control plane for agentic AI security - ensuring that innovation doesn’t come at the cost of exposure.
3. Automated Remediation for ITDR at the AI Identity Layer
Our context-aware anomaly detection is embedded within the Identity Threat Detection and Response (ITDR) module - enabling continuous monitoring of ALL identities, both human and nonhuman.
If an AI agent suddenly begins performing high-volume file operations, connecting to unusual SaaS applications, or modifying permissions at scale, our automated workflows immediately detect and remediate those behaviors in real time.
These remediations (like unsharing files, revoking sessions, suspending users) are done 100% automatically - eliminating alert fatigue and the manual investigations that are launched too late.
By the time a traditional SOC responds to an alert flagging unsuspicious activity performed by a compromised agent, the breach has already happened and the damage is done.
With DoControl, security teams can sleep peacefully at night, knowing that if a compromise originates from a suspicious location at 2 a.m., it will be contained and remediated before they even wake up.
DoControl ensures that even when Gemini acts autonomously (or is compromised!), enterprises retain full control over who - or what - is taking action across their SaaS environment.
Summary
The new wave of agentic AI brings forth a new class of exposure: AI-driven identity and data risk.
Enterprises can’t afford to wait for a breach to reveal where their AI security blind spots are. They need visibility into what their AI can access, how it behaves, and when that behavior crosses the line.
The challenge is scale. Agentic AI doesn’t just read or write - it acts, connects, and learns. Every one of those actions touches sensitive SaaS data, identity infrastructure, and business logic. Traditional security tools were never designed for that.
That’s why the next phase of enterprise security will be defined by AI-aware governance - platforms capable of understanding not just users and files, but also the non-human identities operating in between.
DoControl provides exactly that layer.
By combining data access governance, context-aware anomaly detection, and automated remediation for identity threat detection and response, DoControl gives security teams the visibility and control needed to protect the modern enterprise - human and AI alike.
