Guardrails for Growth: Secure AI Adoption in Multi-Cloud Environments

Today’s processes are powered by more than one AI tool, where using multiple platforms is quickly becoming the norm. Digital workspaces use a patchwork of large language models (LLMs) like ChatGPT and Claude, enterprise platforms such as Microsoft Copilot, Google Gemini, and Salesforce Einstein GPT, and even AI agents designed to autonomously execute tasks or workflows. This diversity is powerful, but the reality of managing these tools securely in a multi-cloud environment is a significant challenge.

According to the State of Cloud and AI Security 2025 survey, 63% of organizations now operate across multiple cloud providers, averaging 2.7 environments, while 55% actively use AI for business needs. Alarmingly, 34% of those have already experienced AI-related breaches, often due to misconfigurations and identity gaps.

Gartner’s AI TRiSM framework makes one thing clear: trust, risk, and security should not be afterthoughts; they are the foundation of AI success with any tool. This report also underscores how critical those components are from an internal perspective, since internal violations of enterprise policies are the biggest threat to unauthorized AI transactions. Multi-cloud security isn’t just about endpoint security and monitoring external attack vectors; organizations have to look internally at their own data-sharing configurations and behavior.

The stakes are high, but the path forward doesn’t have to be complicated. This article lays out a cohesive blueprint for organizations to adopt AI tools securely across multi-cloud environments without sacrificing agility.

Why a Unified Blueprint is Non-Negotiable

The promise of multi-cloud AI is compelling: flexibility, resilience, and access to best-of-breed services. Yet, without a unified strategy, these benefits can quickly unravel into chaos.

As organizations expand their AI footprint across multiple clouds, their attack surface grows exponentially. Misconfigurations and identity gaps go beyond technical oversights to systemic risks that demand a proactive, structured response.

The numbers tell the story:

• 82% of organizations operate hybrid environments, and 63% use multiple clouds, creating fragmented infrastructures that outpace traditional security models.
• Identity-related weaknesses remain the top risk, with 59% citing insecure identities and risky permissions as their greatest concern.

This fragmentation introduces three critical obstacles:

• Data fragmentation. Managing data across separate clouds makes it nearly impossible to gain a unified view. Data silos hinder visibility, complicate compliance, and create blind spots for security teams.
• Governance disconnect. Manually applying consistent security and compliance rules across different platforms is a recipe for inconsistency. Each cloud provider has its own policies, controls, and interfaces, making it difficult to enforce organization-wide standards.
• Shadow IT. The easy adoption of AI tools can bypass IT and security, creating unseen risks. Employees may spin up new AI services without proper vetting, exposing sensitive data and undermining governance.

Beyond these obstacles, organizations must also address the lack of shared-responsibility clarity and inconsistent access controls across clouds. A resilient AI foundation depends on proactive governance – anchored by data security posture management (DSPM) and cloud security posture management (CSPM) – to maintain visibility, enforce consistent policies, and secure data flows across environments.

The Blueprint to Secure AI Adoption

Utilizing multiple AI tools while minimizing data risks is a balancing act. A structured, proactive approach, however, changes the game. Here are three practical steps for organizations aiming to maximize their AI:

1. Assess Your Environment

Before deploying any new AI tools, you must understand your current landscape. Start by auditing your organization’s use of Google Workspace and Microsoft 365 to identify where AI tools are already in use, both formally and through shadow IT. This discovery phase is crucial for understanding your AI readiness. 

Identify business use cases for Gemini and Copilot across different departments. Where are these tools driving productivity? Where are they introducing risk? Evaluate your data landscape using workspace analytics to assess AI readiness and exposure. Beyond counting licenses, understanding how data flows, where it’s stored, and who has access is crucial to data security.  

2. Enforce Unified Governance Across Clouds

This is where your strategy truly becomes scalable and secure. Start by cleaning up your collaborative data in both Microsoft 365 and Google Workspace to establish secure, AI-ready workspaces. Adopt unified multi‑cloud security insight tools to gain end‑to‑end visibility into data classification, permissions, and cross‑cloud data flows in Google Workspace — reducing silos, tightening least‑privilege/zero‑trust access, and pre‑empting migration/configuration gaps that expand the attack surface.

Make sure users only have access to relevant content to prevent overexposure through AI tools. Automate data classification, lifecycle management, and organization-wide sharing policies to sustain data confidence. Ensure auditability, access control, and compliance across both platforms with cross-cloud governance.

Pair your governance model with automated policies to standardize external sharing and guest-access lifecycles, enforce least-privilege by default, and operationalize adaptive governance at scale across Teams, SharePoint, OneDrive, and Groups.  

3. Drive Adoption with Purpose and Control  

With governance foundations in place, it is time to scale AI adoption across your organization.  

• Launch targeted rollout programs that align with business goals and security policies.  
• Leverage usage intelligence tools to surface risks and track performance metrics.  
• Support responsible AI use through training and change management initiatives.  
• Explore opportunities to leverage AI tools (like Google Gemini and Microsoft Copilot) together to maximize multi-cloud productivity.  
• Run dual-tool pilots that pair each platform’s strengths to a single outcome, ensuring each tool is applied where it delivers the most value.

What is Data Security: The Complete Guide

Building an AI Strategy for the Multi-Cloud Era

Secure AI adoption is an ongoing process. Organizations must adopt layered controls across trust, risk, security, and compliance (e.g., explainability and bias testing, runtime inspection/enforcement, and auditable trails mapped to regulations) to make resiliency an ongoing discipline rather than a one‑time project.

A unified, automated governance platform is the central piece of this blueprint, enabling organizations to scale AI confidently without compromising security.

Through these steps, businesses can transform AI from a security liability into a strategic advantage.  

Learn how AvePoint can help you build secure, governed AI workflows across Google and Microsoft.

Transform your AI experience with confidence and security