Understand your Cloud Storage footprint with AI-powered queries and insights

Google Cloud Storage offers a new capability called storage insight generation, which leverages the Gemini AI model to provide valuable insights about your Cloud Storage environment. This feature allows you to eliminate manual data analysis, identify potential security and compliance risks, and find cost-savings opportunities. To get started, you need to set up the Storage Insights Dataset, which collects and centralizes all bucket and object metadata across your Google Cloud projects and regions in a BigQuery linked dataset. The dataset is refreshed every 24 hours and can retain up to 90 days of historical data. You can access a short summary of your dataset and use pre-curated prompts with validated responses to analyze your data. The feature also includes multi-turn chat for deeper insights and interactive analyses. Despite the powerful capabilities of generative AI, there may be occasional hallucinations, so the tool includes informational indicators such as SQL queries, curated prompts with high accuracy tags, and data freshness information. Your object and bucket metadata is completely yours and not used for training Google Cloud’s AI models. The tool follows Google’s Responsible AI approach to validate answers and increase content safety.