Security Overview

Treasure AI Studio is a multi-tenant AI agent execution platform designed with security at every layer. This page covers the architecture, data protection measures, execution environment, and monitoring capabilities that protect your data.

Understand the security boundaries that protect your data when using Treasure AI Studio — the architecture, data protection, server-side sandbox, external service connections, network audit logging, and your responsibilities when working with AI-generated output.

• Signed in to Treasure AI Studio (Getting Started)
• Admin role required for Network Audit Log and Network Policy access

Treasure AI Studio operates as an AI orchestration layer hosted on AWS. Key architectural points:

• Multi-tenant platform. Each organization's data is logically isolated from other tenants.
• CDP integration. Studio accesses customer data through CDP APIs — it does not connect directly to your underlying data stores.
• Server-side execution. All agent operations run in isolated server environments, not on your local machine.

All data is encrypted both in transit and at rest:

Studio uses Amazon Bedrock for LLM inference with the following data protections:

LLM inference requests are processed within a geographic boundary based on your Treasure AI account region:

Amazon Bedrock Cross-Region Inference is used for throughput optimization. Geographic inference profiles ensure that customer prompts and model responses never leave the geographic boundary. All inter-region data transmission remains on the AWS private network, encrypted in transit.

All user data — including chats and files — is isolated per user. Administrators cannot access other users' chats or files.

Unlike Treasure Studio Labs — which ran commands directly on your local machine — Treasure AI Studio executes all CLI commands and queries in an isolated server-side sandbox.

This means:

• No local filesystem access. The AI cannot read or write files on your computer. File operations happen in a managed server environment.
• Sandboxed execution. Each session runs in its own isolated worker with restricted network and filesystem access. If a sandbox crashes unexpectedly, you'll see the message: "The sandbox environment crashed unexpectedly and is restarting. Please wait a moment and try again."
• Credential isolation. Your Treasure AI credentials are managed server-side and are not stored in the browser. The AI accesses your Treasure AI account through a server-side access token — you do not need to provide credentials directly.

Studio agents can connect to external services (such as Google Drive, Slack, or other third-party tools) to retrieve data or perform actions.

• Users authorize each external service connection explicitly through OAuth.
• Credentials are encrypted at rest and scoped to the individual user.
• Users can revoke connections at any time through Settings.

External connectivity is controlled by a deny-by-default network policy. Administrators can:

• Allow specific domains. Configure an allowlist of permitted external domains.
• Block all external access. Disable external connectivity entirely for the organization.

Account administrators can monitor all network activity through the Network Audit Log in Settings.

1. Open Settings (click your user avatar in the sidebar footer)
2. In the left navigation, under the Organization section, click Network Audit Log

Studio uses AI to generate outputs including queries, configurations, segments, and recommendations. These outputs:

• May be incorrect or incomplete. Always review generated SQL, segment rules, and configurations before deploying to production.
• Should be validated. Use the sql-skills:trino-optimizer skill to check query performance, ask the AI to run tdx sg validate to validate segment definitions, and tdx journey validate for journeys.
• Are your responsibility. Review and approve all AI-generated changes before pushing them to your Treasure AI account.

1. Use work folders to enforce guardrails. Create work folders with instructions like "Always validate before pushing" and "Never modify production segments directly."
2. Monitor via Network Audit Log. Review network activity regularly, filtering by time range and domain to identify unexpected API calls.
3. Configure Network Policy. Restrict external connectivity to only the domains your organization needs.

1. Review tool calls. Every action the AI takes is visible in the chat stream. Watch for queries and API calls as they execute.
2. Leverage skills for validation. Skills like validate-segment and validate-journey catch configuration errors before they reach your account.
3. Manage external connections. Only authorize external services you actively need, and revoke unused connections.

You are responsible for:

• Reviewing and validating all AI-generated outputs before deployment
• Not embedding secrets, passwords, or access tokens directly into chat messages
• Reporting any security concerns to security@treasure-data.com

• (Admins) Access the Network Audit Log under Settings > Organization
• (Admins) Review Network Policy settings for external connectivity

• Data Retention — Chat history retention and data lifecycle
• Access Control — PBP, IP allowlists, and AI feature access
• Getting Started — Set up Studio on your platform
• SSO Login — Authentication details
• Core Concepts — Work Folders, models, and credits