Operational boundaries
Control patterns that turn AI use into observable work: owners, boundaries, approvals, logs, exceptions, and evidence.
Validate inputs to and outputs from AI agent tool calls, plugins, and retrieval pipelines to prevent injection attacks from crossing trust boundaries.
A control pattern that enforces identity-bound authorization and policy decisions outside the agent runtime before tool calls execute.
A maintained record of approved AI tools, allowed use cases, owners, data limits, account requirements, and review status.
Classifying data before AI use defines what may be shared, transformed, summarized, retained, or logged.
Human approval prevents sensitive AI-assisted actions from moving directly from model output to execution.
Prompt and output logging preserves enough interaction evidence to support review, monitoring, audit, and incident response.
Enterprise identity controls reduce account takeover risk and improve visibility for approved AI tools.
