How to govern Inventory in Gemini
A step-by-step guide to governing Inventory in Gemini with Rencore: detect, review by owner and severity, and remediate with an audit trail.
Governing Inventory in Gemini means finding where it goes wrong, reviewing the findings by owner and severity, and remediating with an audit trail. Rencore covers this concern for Gemini with the pre-built controls below, so it becomes a repeatable check rather than a one-off cleanup. The steps that follow apply the same detect, review, remediate loop to Inventory.
Steps
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Inventory Gemini
Connect Gemini and let Rencore build a continuous inventory of its resources, owners, and configuration, so governance starts from what exists rather than a stale export.
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Detect with policies
Turn on the pre-built policies that cover Inventory in Gemini to surface oversharing, sprawl, and misconfiguration on the first scan, before writing a single custom rule.
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Review by owner and severity
Use the Gemini reports to review findings by owner, category, and severity, and to share them with stakeholders who do not have a seat in the platform.
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Remediate and automate
Apply automations to fix findings at scale, route sensitive changes through approvals, and keep every action reversible and logged for the audit trail.
Gemini controls for Inventory
Grounded in the Rencore catalog. See the full Gemini catalog on the Gemini connector page.
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Gemini Project
Google Cloud project containing Vertex AI and Agent Builder resources
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Gemini Model
Custom or tuned ML models registered in Vertex AI Model Registry
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Gemini Endpoint
Model serving endpoints that host deployed models for prediction
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Gemini Pipeline Job
ML pipeline execution jobs in Vertex AI Pipelines
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Gemini Tuning Job
Fine-tuning jobs for foundation models in Vertex AI
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Gemini Dataset
Training datasets used for model training and fine-tuning in Vertex AI
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Gemini Agent
Deployed AI agents (Reasoning Engines) in Vertex AI Agent Engine
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Gemini Notebook Runtime
Managed notebook runtime instances for interactive ML development
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Gemini Engine
Search, recommendation, or chat engines in Vertex AI Agent Builder
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Gemini Data Store
Data stores for search, RAG, and recommendation in Agent Builder
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Gemini Document
Indexed documents within Agent Builder data stores
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Gemini Session
Conversation sessions within Agent Builder engines
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Gemini User
Users with access to Google AI resources, linked to Microsoft 365 accounts
