National security agencies and other sovereign and highly regulated organizations handle incredibly sensitive data such as classified reports, field manuals, and sensitive multi-modal datasets. Search and multimodal intelligence that’s available in the public cloud can help teams manage and analyze that data, but due to strict data sovereignty mandates, that data cannot leave the customer premises.
The alternative has been to deploy custom agentic search inside a secure, disconnected perimeter, but until now, that meant months building and maintaining custom infrastructure — stitching together models, building speech-to-text and optical character recognition (OCR) pipelines, configuring vector databases, and managing a bespoke user interface.
Today, we're excited to announce enterprise search capabilities with Gemini on Google Distributed Cloud (GDC) air-gapped to help solve the above challenges. In preview, this fully managed retrieval-augmented generation (RAG) search experience runs entirely inside your physical security perimeter, with no connection to the public internet or to Google Cloud's global infrastructure. Your teams can now upload their files into a conversation and ask Gemini to find information about it, summarize it, compare it, or translate it. Every answer cites its sources, and conversations persist between sessions.
Enterprise search capabilities on GDC enable your teams to search across siloed data and unlock powerful insights. With these capabilities, you can now:
Users can work with PDF, Microsoft Word, PowerPoint, and Excel files, plus TXT, HTML, PNG and JPEG images, WAV audio, and MP4 video. Each file can be up to 100 MB, and each chat up to 500 MB.
Let's follow a user query through each layer of the stack: signing in, ingesting files, generating the response with Gemini, and storing the session. Every step happens on the organization's air-gapped network.
Identity and access
Users sign in through their organization's identity provider. Administrators map the service's user role to identity provider groups, and only members of those groups can access the application. Each user's chats and uploaded files are private to that user.
Document ingestion and embedding
When a user uploads a file, it goes straight to the app's object storage bucket. The backend records the file's metadata in the app's managed PostgreSQL database, which is optimized for search. The backend then splits the file into chunks, such as groups of PDF pages or tiles of an image, and writes them to the bucket. A Gemini embedding model running natively within the GDC deployment converts each chunk into a vector, which the backend stores in the app's PostgreSQL database.
When the user asks a question, the state-of-the-art (SOTA) Gemini model running entirely on GDC inside the customer’s data center first rewrites the question with context from the conversation. The backend then embeds the rewritten question with the same model and searches the vector database.
Model serving
The backend then combines the question, the conversation history, and the best-matching data into a single multimodal prompt and sends it to a Gemini endpoint running on NVIDIA B300 GPUs in that same GDC deployment. Gemini is natively multimodal — it accepts text, images, audio, and video as input, so it can reason across data coming from different file types in a single context, and doesn't require separate speech-to-text and OCR models. The answer then streams back to the user with citations to the source files.
Persistence
Lastly, the persistence layer keeps each conversation's state. Each question and answer is stored in the system’s database and uploaded files stay in object storage, so users can reopen an earlier session and pick up where they left off.
With Gemini and enterprise search on GDC, prompts, responses, uploaded documents, and embeddings never leave your organization's air-gapped network, and every model runs locally. Data is encrypted at rest with AES-256 disk encryption using locally managed HSM keys, and in transit with TLS using FIPS-approved algorithms. Local IAM integration with granular role-based access control (RBAC) governs access to resources. GDC air-gapped has U.S. DoD IL6 accreditation and is designed to support strict requirements in alignment with FedRAMP High security controls.
Code execution: Compliance and security
To deliver precise, deterministic answers from structured spreadsheet data (such as CSV and XLSX files), Gemini enterprise search leverages the Gemini inference stack's local code-execution engine.
When handling analytical queries — such as computing the average salary across departments — Gemini dynamically generates Python code and runs it securely within Agent Substrate, an open-source execution runtime built for large-scale agent orchestration.
To mitigate security risks inherent to executing LLM-generated code, Agent Substrate enforces strict network isolation and runs workloads inside gVisor sandboxes, helping ensure strong isolation boundaries without sacrificing inference performance.
You no longer have to choose between uncompromising security and cutting-edge multimodal intelligence. Enterprise search, Gemini 3.5 Flash and databases optimized for search run together inside the GDC air-gapped perimeter with no dependency on the internet to bring the best autonomous capabilities to the sovereign cloud.
Explore the GDC air-gapped documentation and contact your GDC representative to learn how you can best leverage these search capabilities in your sovereign cloud.