Google Cloud Platform (GCP) has become a foundational environment for enterprise AI. As Gemini Enterprise – Google’s unified platform for building, scaling, governing, and optimizing AI agents – adoption grows, organizations are running more AI initiatives on Google Cloud, making infrastructure decisions increasingly important to operational resilience. The choices organizations make today will determine how reliably, securely, and recoverably those AI programs scale as adoption accelerates.
GCP provides strong native protections for core infrastructure. Its backup and disaster recovery (DR) service delivers immutable backups for Compute Engine virtual machines (VMs), VMware VMs, databases, and file systems through a centralized console, while Google's AI Protection helps organizations discover, secure, and manage risk across Gemini Enterprise assets.
The pressure on protection, recovery, and governance capabilities will only increase as AI adoption accelerates. Gartner predicts that 50% of cloud compute resources will be devoted to AI workloads by 2029, up from less than 10% today — a fivefold increase in AI-related cloud consumption in less than five years. That growth is amplifying pressure on the Google Cloud backup, DR, and governance capabilities supporting it. AI workloads introduce operational requirements that traditional infrastructure protections were not designed to address. They span multiple services, depend on evolving configurations, and don’t reside in a single cloud.
5 Design Considerations for AI Frameworks on Google Cloud
Five areas consistently require additional design beyond what native tooling addresses. Each maps to a specific characteristic of AI workloads – from configuration sensitivity to cross-cloud dependency – and each requires intentional design decisions rather than relying on default configurations.
1. Configuration Recovery for AI Services
AI workloads are configuration-heavy. Model endpoints, identity and access management (IAM) bindings, agent platform pipeline settings, service accounts, and network policies collectively determine how AI services operate. Google’s own security guidance for generative AI workloads emphasizes that much of the risk is not in the model itself but in configuration drift, where different projects enable different models, expose different notebook patterns, or handle secrets and networking inconsistently.
Google Cloud’s native persistent disk snapshots capture data volumes at a point in time, and backup and DR service protects supported workloads with immutable backup vaults. GCP backup and recovery at this layer restores infrastructure state, but it does not consistently capture the surrounding configuration state that AI services depend on. When a rollback is needed, restoring the data without restoring its supporting configuration can leave critical services unable to function as intended. The AvePoint Confidence Platform extends recovery to the configuration layer. It captures settings, permissions, and policy states across the environment so that when a rollback is required, the AI service is restored to a working baseline, not a partial one.
2. Data Isolation as AI Workloads Grow
AI workloads consume data from many sources. A single Gemini Enterprise deployment can draw from Drive, Cloud Storage, BigQuery, and connected SaaS applications. Without deliberate isolation, sensitive data can move into training sets, prompt contexts, or agent memory in ways that are difficult to detect after the fact.
The exposure risk is measurable. IBM reports that 13% of organizations experienced breaches involving their AI models or applications, and 97% of those organizations lacked proper AI access controls. Sixty percent resulted in a broad data compromise, and 31% led to operational disruption.
Google Cloud’s Sensitive Data Protection service extends automated discovery to Gemini Enterprise datasets, helping teams understand data sensitivity during training and tuning. This addresses the discovery layer. Cloud Storage backup and recovery through Backup and DR Service protects the object layer. AI workloads also require content-level controls across the collaboration environments that supply data to the model: knowing which users and agents can reach specific data, and where oversharing exists before AI activates on top of it. The AvePoint Confidence Platform provides continuous visibility into permissions, content sensitivity, and oversharing risks across Google Workspace and connected environments, helping organizations reduce exposure before AI scales access to that information.
3. Cross-Cloud Continuity
Most organizations do not operate in a single cloud. Recent data from the 2025 SANS Multicloud Survey shows that more than 76% of enterprises operate across Amazon Web Services (AWS), Azure, Google Cloud, and others, yet nearly half lack centralized visibility and control across those environments.
Recoverability becomes more complex in multicloud environments. IBM found that breaches involving data stored across multiple environments took the longest to identify and contain — 276 days on average, the highest of any storage category. The Organization for Economic Cooperation and Development (OECD) adds that the absence of widely adopted technical standards limits interoperability across providers, raising the time and cost of moving or recovering data across clouds.
AI workloads often depend on this cross-cloud reality. Data ingested from Microsoft 365 or Salesforce may feed a Gemini Enterprise agent running on Google Cloud. Google Cloud’s disaster recovery capabilities protect workloads running in Google Cloud, so continuity for data flowing in from other clouds and SaaS platforms must be designed separately. AI disaster recovery in this context is not a single-provider problem — it is an enterprise cloud backup problem that extends across the collaboration platforms and business systems supplying AI data.
The Confidence Platform is built for multicloud environments, protecting Google Workspace, Google Cloud, Microsoft 365, Salesforce, Azure, Dynamics 365, and Power Platform through a single control plane, and providing AI workloads with consistent recovery paths regardless of where the data originates.
4. Visibility Into AI Activity and Drift
AI workloads change constantly. Prompts evolve, models are re-tuned, and agents accumulate permissions as they connect to new systems. Without continuous visibility, routine operational changes can gradually increase risk exposure over time.
Enterprise infrastructure has also grown more concentrated. Amazon, Microsoft, and Google now account for 63% of enterprise spending on cloud infrastructure services, meaning that a disruption to any one of them would have broad ripple effects. AI workloads inherit many of the availability and dependency considerations associated with the underlying cloud infrastructure.
Google Cloud’s AI Protection, integrated with Security Command Center, provides a centralized view of AI posture and helps discover AI inventory, secure AI assets, and manage AI threats. This addresses infrastructure-level AI risk. It is not designed to track how AI agents interact with sensitive business content, whether their content-level permissions have drifted, or whether their behavior aligns with policy over time. The Confidence Platform adds this operational layer, providing continuous insight into AI and AI agent activity so risk can be identified and remediated before it escalates.
5. Tested Recovery, Not Assumed Recovery
Having backups is only one part of recovery readiness. IBM reports that 76% of organizations that fully recovered from a breach said recovery took longer than 100 days, and 26% said it took more than 150 days. For AI workloads that support customer-facing services or revenue-generating processes, that timeline is not viable.
Backup immutability is a core requirement here. Google Cloud DR through Backup and DR Service stores backups in a secure, Google-managed environment protected from tampering and early deletion, providing the air-gapped backups needed to recover from ransomware, user errors, or data corruption. These immutable backups protect the infrastructure layer, and recovery into new or existing Google Cloud environments is supported directly. Organizations should also validate recovery procedures across AI services, connected data sources, and cross-cloud dependencies to ensure recovery plans perform as expected when needed.
The Confidence Platform supports granular restores, full environment recovery, and self-service recovery options across the workloads AI depends on, allowing organizations to validate that AI workloads can be restored quickly and precisely when needed.
Designing for What Comes Next
AI on Google Cloud is not a static deployment. It is a continuously evolving set of workloads that depend on configuration integrity, data isolation, cross-cloud continuity, ongoing visibility, and tested recovery. Native Google Cloud capabilities provide a strong foundation, while complementary governance, protection, and recovery strategies help organizations build greater operational resilience for AI workloads.
Download the cheat sheet to map where Google Cloud’s native tools provide coverage and where the AvePoint Confidence Platform strengthens your AI infrastructure.
Ava Ragonese is a Product Marketing Manager at AvePoint, leading the GTM of data security solutions for Google Workspace and Cloud. She helps organizations focus on quality data and insights to drive innovation and how multi-cloud collaboration can impact businesses. Ava has a M.Eng. in Systems Analytics from Stevens Institute of Technology and enjoys bringing her technical acumen to complex business decisions such as AI adoption.