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页面加载中Hybrid Deployment Boundaries
Enterprise AI agents need to work with data that lives in different places — some on local machines, some in private data centers, some in cloud warehouses. This guide defines the boundaries for hybrid agent workflows, permission tiers across data classes, and the architecture decisions that keep data where it belongs while enabling AI-assisted work.
Design the data boundary first, before choosing tools. Classify every data source as local-only, hybrid (local processing with shared metadata), or cloud-authorized. Define permission tiers per data class — an agent that reads cloud warehouse data for analysis should not automatically gain write access to local databases. Require audit logs for every agent query, suggestion, and action. Use inchWorker for individual workflows (one person, local files, own keys) and InchStack for team workflows (multiple people, shared governance, approval chains, delivery receipts).
Define where each data source lives and what level of agent access is appropriate.
Data stays on the user machine. Agent processes locally. No metadata shared.
Examples: Client files, personal work documents, draft analyses, local spreadsheets
Data stays local or in private deployment. Metadata (field names, quality scores, delivery status) is shared through a control plane for team coordination.
Examples: Department databases, shared project files, team governance rules
Data resides in cloud warehouses or managed services. Agent access is read-only by default with explicit write permissions per workflow.
Examples: Cloud data warehouses, BI platforms, managed analytics services
| Permission Level | Local-Only Zone | Hybrid Zone | Cloud Zone |
|---|---|---|---|
| Read metadata | Allowed | Allowed | Allowed |
| Read data | User gate | Approval required | Read-only role |
| Suggest actions | Allowed | Human review | Human review + scope check |
| Execute (approved) | User confirms | Dual approval | Dual approval + audit |
| Execute (auto) | Not recommended | Not allowed | Not allowed |
Every agent interaction must be logged. The audit trail is your evidence that boundaries were respected.
Timestamp, agent identity, data source accessed, query or action type, data scope (tables/fields), result summary.
Proposed action, human reviewer identity, approval/rejection decision, rationale, timestamp, before/after state.
Automatic alerts when agent attempts access outside defined scope. Logged with timestamps and automatically blocked.
Formal record of completed work: what was delivered, who approved it, quality checks passed, and the evidence package.
Technically possible, but not recommended for initial deployment. Start with one zone, validate boundary enforcement, add a second zone only after the first zone has passed all audit checks for at least 2 weeks. Each new zone increases attack surface and complexity.
InchStack acts as a control plane that receives only metadata — field names, data types, quality scores, governance rules, delivery statuses — not the actual data rows. The raw data stays in its local or private-deployed environment and is only accessed by the agent on that machine.
The access should be automatically blocked and logged as a boundary violation alert. A human reviewer must investigate whether the attempt was legitimate (scope needs updating) or unauthorized (potential misconfiguration or prompt injection).
Yes. InchStack provides the governance control plane (permissions, approvals, audit, receipts) that wraps around agent execution. The agent framework handles the AI logic; InchStack enforces the data boundaries and human review checkpoints.
Start with one zone, one workflow, and clear permission rules.