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NewsTopic · 智能体AI安全治理2 min read·2026-10-18

Microsoft Foundry Blog: Setting Testable Network Egress Boundaries for Hosted AI Agents

Muzz Imam from the Microsoft Foundry team explains why tool lists are not network boundaries and demonstrates how to define, attach, and test outbound destination rules for hosted agents using RAI policies, distinguishing between audit and enforced modes.

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2026-09-25 01:55 GMT+8

When building AI agents, developers typically focus on "which tools did I give the agent?" However, Muzz Imam of the Microsoft Foundry team argues that the more critical question is "where can this process send a request?" In a blog post about network egress controls in Microsoft Foundry Agent Service, Imam explains a core concept: a tool list is not a network boundary. If an agent is allowed to call an HTTP client, it can access any URL unless there are independent network-layer restrictions.

Imam illustrates this mechanism with an invoice-processing agent example. The agent needs to access a finance API and a vendor API, but if a document contains an unfamiliar upload link, or a helper library follows an unexpected URL, application-level checks alone are insufficient. He proposes moving the destination decision out of the code and onto the hosted-agent definition, controlling it via an explicit Egress Policy.

The article details how to create a JSON policy file containing FQDN rules, specifying allowed domains (such as finance.contoso.example) and defaulting to deny all other traffic. Subsequently, the policy is created via Azure CLI and attached to an agent version using the Python SDK. Imam emphasizes that successful configuration storage does not equal runtime enforcement, necessitating actual testing.

Testing occurs in two stages: first, using "Audit" mode to observe calls and confirm that requests expected to be denied generate "would-deny" evidence; then switching to "Enforced" mode to verify that unapproved destinations truly return 403 errors with no requests reaching the endpoint. This two-step approach ensures policy correctness while avoiding blocking potentially legitimate traffic in production environments directly.

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However, Imam also clarifies the limits of this approach. The feature is currently in preview with no SLA and is not intended for production use. Furthermore, egress policies only answer the question "Is this the right destination?" They do not address "Is the caller authorized at that destination?" or "Is this appropriate data to send?" Application-layer authentication and data validation remain indispensable.

Sources

  • 原始出处devblogs.microsoft.com↗
Topic

智能体AI安全治理

随着AI智能体从聊天机器人演变为能够自主规划、调用工具并跨系统操作的实体,其安全边界已从模型输出扩展至执行链、身份认证及网络出口。当前治理核心在于解决传统IAM无法应对的“非人类身份”风险:如权限动态累积、缺乏归因审计及未受控的网络连接。

4 steps3 piecesUpdated 2026-10-18 07:43

How it got here

  1. 2026-08-29清华大学DeepKernel Lab发布论文《AgentKernel》,提出首个信任原生智能体操作系统,主张将安全作为第一类设计约束,通过内核级强制服务解决提示注入和内存中毒问题。
  2. 2026-10-14IBM举办网络研讨会,强调智能体身份管理的四个关键缺口:无治理的持久访问、未经审查的权限提升、缺乏归因以及碎片化控制,推荐采用Vault和Verify实现零信任动态凭证。
  3. 2026-10-18微软Foundry博客演示如何通过RAI策略为托管智能体设置可测试的网络出口边界,区分审计与强制模式,明确工具列表不等于网络边界。

What is still unsettled

  • 操作系统级安全架构(如AgentKernel)在实际生产环境中如何平衡性能开销与安全强制性?
  • 在开放智能体网络中,基于区块链的信任机制能否有效解决跨组织身份验证与价值结算的可扩展性问题?
  • 现有的应用层治理工具(如Microsoft AGT)是否足以应对日益复杂的代理间协作攻击,还是需要更底层的内核支持?

Also in this thread

  • Former Google Cloud CISO Phil Venables on AI Agent Governance2026-10-18
  • UiPath CISO Scott Roberts: Identity and Trust Boundaries for AI Agents2026-10-18
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