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Agent Trust Cloud
BlueprintPage 04
04STRATEGY

Executive Summary

Become the system of record and runtime control plane for the enterprise AI workforce.

Priority
P0
Phase
Foundation
Primary owner
CEO / Product
Status
Blueprint

Objective

Agent Trust Cloud should be built as a vendor-neutral control plane for enterprise AI agents. The platform must create a trustworthy operating boundary around agents without forcing customers to replace their existing identity, SIEM, cloud, model, or SaaS investments. The commercial thesis is simple: as autonomous software gains the ability to read sensitive data and execute actions, enterprises need a dedicated system to identify each agent, constrain its authority, observe every action, and prove what happened.

What to build

  • Establish a single enterprise inventory of AI agents, agentic applications, MCP servers, tools, service identities, credentials and owners.
  • Issue or bind every agent to a durable identity and policy context that can survive model/provider changes.
  • Enforce runtime decisions: allow, deny, redact, rate-limit, sandbox, require approval, or terminate.
  • Capture evidence sufficient for security operations, audit, compliance, investigation and cost governance.

Implementation decisions

  • Build the platform as API-first control infrastructure; the dashboard is a management layer, not the security boundary.
  • Start with discover → register → policy → gateway → audit as the minimum end-to-end loop.
  • Design connectors to coexist with Entra, Okta, CyberArk, SIEMs, cloud providers and model platforms.
  • Keep deterministic controls separate from probabilistic AI detection so high-risk enforcement remains explainable.

Definition of done

  • A customer can discover an agent, assign an owner, restrict a dangerous action, observe the enforcement event and export the evidence.
  • The same policy model works across at least two model providers and two enterprise systems.
  • Security, IT, audit and finance can each use the same underlying event record without duplicate integration work.

Success metrics

  • Time to first discovered agent
  • Percent of agent actions policy-evaluated
  • High-risk actions blocked or approved
  • Audit evidence generation time

NORTH STAR

Become the system of record and runtime control plane for the enterprise AI workforce.