Cloud Asset Inventory Overview – Stay Organized with Collections

Create a primary collection to tag all cloud assets. Use an ai-powered model to classify items by status as soon as you ingest them, and attach a consistent tagging policy.

Implement an algorithm that recognize duplicates, links assets to related collections, and records the last seen date in a dedicated tabla. Configure transitions so status updates propagate automatically when assets move between stages.

For in-store assets and cloud inventories, create parallel collections by product line and location. This helps comerciantes y organizations identify gaps within last-mile assets and compare same-category items across stores.

Launching new collections requires a premium workflow. When you publish a collection, provide ready-made dashboards for teams, and set reviews every 7 días to keep status aligned.

heres a concise guide to get results in days: create owners for each collection, apply a standard tag set, run the ai-powered model on new inputs, and schedule a weekly check to verify accuracy across tables. launching a new collection becomes straightforward when you reuse templates and policies across environments.

Understand how Cloud Asset Inventory and Collections map to your environment

Implement a mapping scheme that links Cloud Asset Inventory assets to your environment by grouping assets into Collections tied to clouds, projects, and workloads. This helps recognize what assets exist, what bindings apply, and how each asset relates to the running model of your infrastructure. Use a lightweight tool to capture behavior across assets and stay aligned with policy requirements.

Categorize assets by type and usage: define categories for instance, storage, network, and policy; assign a type and a following tag to each asset. Collections act as a shelf where introduced asset types are stored with associated metadata. This full mapping stays updated as clouds introduce new types and bindings.

Each asset carries bindings that attach to IAM roles, service accounts, and policies. This mapping helps recognize risk exposure and guides remediation efforts. It also shows which assets are associated with which clouds and projects, so you can target controls where they matter most.

Plus, leverage premium tools to enrich the inventory: anomaly detection, change tracking, and impact analysis. If you introduce new asset types, CAI will reflect them and you can return to review the structure.

Stay aligned as assets update: depending on scale, set updated scans and alerts for changes in type or bindings, and keep shelf mappings in sync. A modular model lets each instance be tracked and categorized under its collection.

Create, rename, and organize Collections for quick access

Create a Collection named ‘Active Resources’ and pin it for quick access. Using ai-based tagging, group items by their type (images, model, resources) and by status, then export a snapshot of their data for sharing with your team.

To build a Collection, click New Collection, assign a clear label, and add items by reference (request, instance_name, or URL). The view shows their data fields, such as type, runtime, and updatetime. The label you assign uses clear terms.

Rename as needs change: choose a label that reflects scope, for example ‘Images–Active’ or ‘GVNICs by Association’. The relationship between resources and their associated data becomes visible in the view.

Organize by relationship and data flow: tag items by their association, group related resources side by side, and use algorithm-based sorting by updatetime to keep the most recent work at the top.

Control access: assign roles, set request-based access, and enable export permissions for teammates. Keep items organized and expose fields you can filter on, such as instance_name and images, making it easy to locate what you need.

Keep Collections current: whenever a runtime change or new image is added, refresh the Collection so the view remains accurate. Use ai-based insights to surface items that are used most.

Workflow: export a manifest of your Collections to external tools; map resources to their gvnic or other associated networks; maintain order by updatetime to reflect recent activity.

Search and filter assets by type, project, location, and resource name

Enable the asset search bar and apply four filters: type, project, location, and resource name to quickly locate assets.

Use Collections to keep the data organized: each filter combination creates a focused listing, and related resources appear in the same shelf for fast comparison across metadata, compliance status, and runtime details.

For retail sites and shopping catalogs, map assets to sites and listing entries for accurate shelf-level tracking. This helps you stay aligned with product data and available metadata across projects.

1. Filter by type: choose from common asset types such as instance, storage, database, or network. In deployments with multiple instances sharing the same name, reference metadata.instance_name to disambiguate; this improves relationships visibility between the instance and its resources.
2. Filter by project: select the project ID to scope the search. This expands the scope to show all resources under that project, making it easier to assess compliance, data ownership, and access controls across services.
3. Filter by location: pick a region or site. For multi-site environments, location helps you see how assets align with sites, warehouses, or storefronts, plus aids in geo-based auditing and change tracking.
4. Filter by resource name: enter exact or partial resource names to tighten the listing. Partial matching accelerates discovery when you know only a prefix or suffix, and the system updates the results instantly.
5. Expand results for context: click expand to view related resources and metadata fields such as runtime, updated timestamps, and instance_name. This view supports quick assessment of relationships and potential compliance gaps.
6. Review and act: examine the data columns to decide which assets need attention. If an asset hasn’t updated recently, flag it for review and schedule a refresh to keep the data current.
7. Save and reuse: store your filters in a Collection as a named listing. This plus workflow ensures teammates access consistent views and keep data governance aligned with policies.

By combining type, project, location, and resource name in a single search, you obtain a clear, actionable listing that supports data-driven decisions, from everyday asset management to compliance reviews.

Track changes with time-based snapshots and audit activity

Recommendation: Enable time-based snapshots at the asset level with a daily cadence for the past 90 days and weekly backups up to 12 months, and enable audit activity to log every change. This supports compliance and delivers a consistent experience for teams managing projects, their assets, and metadata. Stay aligned with governance and reduce drift across filetype and field values.

Time-based snapshots

Time-based snapshots give you a recoverable history of assets. Configure a daily cadence for the past 90 days and a weekly cadence for the past year. Each snapshot records updatetime, runtime, and metadata, enabling you to compare versions and assess behavior shifts. Use an algorithm to detect unexpected changes and trigger alerts if the field values diverge from expected names or types. If an asset havent changed since the previous snapshot, the delta shows no new values. This approach drives compliance and improves the management experience across projects and drive environments.

Audit activity and governance

Audit trails expose who performed changes, when, and which asset names were affected. Use methods to record operation type, the field involved, and the previous and new values, and maintain a full history for each asset with detailed metadata. Filter by past time ranges, asset type, and metadata to prepare compliance reviews and issue investigations. This clarity strengthens management across projects and their asset inventories, helping teams stay accountable and informed.

Export inventories and integrate with BI tools or pipelines

Formats and data model

Export inventories as JSON or CSV to a centralized storage layer and connect them to BI tools or data pipelines for immediate analysis. Use the latest API to pull assets such as images, folders, and instances, and stage them in a single place for consistent reporting. Keep the data organized with a stable schema that includes name, asset_type, create_time, update_time, and location, and map nested details via listing responses and discovery results. For field reference, use computegoogleapiscomprojectsproject_idzoneszoneinstancesinstance_name to anchor instance names in BI mappings. This ai-based export supports attributes like create, response, and preferences to tailor which asset types are included.

Design the export to be field-level and scalable, with a return path to the destination and a track of changes over time. Include images and folders as asset types when applicable, and expose the full listing and discovery results so BI tools can segment by zone, project, and resource type.

Automation and integration workflow

Create an end-to-end pipeline that triggers on schedule or on new discoveries, uses a system to transform assets, and loads them into your BI workspace. Use a data flow that can be consumed by Looker Studio, Tableau, Power BI, or your own ai-based analytics stack. Orchestrate with Cloud Composer (Airflow) or Cloud Functions, and return a status update to your monitoring system after each run. Maintain preferences for refresh cadence and asset coverage, and store versioned snapshots to support rollback and auditing. The result is an effective, repeatable process that keeps inventories organized and ready for analysis in BI tools or pipelines.