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思科概述了人工智能代理从访问控制到行动控制的转变

思科的身份团队认为,传统的 IAM 平台不足以满足人工智能代理的需求,因此提出了一个基于运行时身份和细粒度操作级别授权的新框架。

4 min readRead the original reporting
Source-provided image accompanying Cisco outlines shift from access control to action control for AI agents
归因报告来源记录
出版商
venturebeat.com
来源链接
venturebeat.comhttps://venturebeat.com/security/ai-agents-need-more-than-access-control-they-need-identity-at-runtime
来源类型
新闻媒体的报道——不是第一方文件。

我们无法独立确认的内容: 此声明归因于指定的商店。我们没有根据第一方文件对其进行验证。 (venturebeat.com)

背景60 秒内了解这一点

从这里开始

关键术语

MCP(模型上下文协议)
一种开放协议,允许人工智能应用程序以标准方式连接到外部工具、数据源和上下文提供者。
人工智能代理
一种可以观察、推理并采取行动来实现目标的软件系统,通常使用工具和内存。
测试一下自己AI 代理测验

发生了什么

Cisco’s identity division, represented by VP of product Matt Caulfield, has outlined a new security framework for managing AI agents in enterprise environments. The report argues that existing Identity and Access Management (IAM) systems, designed for human users, are inadequate for the rapid, non-human scale of deployment. Cisco proposes moving beyond static access control toward 'action control,' which requires continuous verification and cryptographic identity binding at runtime.

Cisco’s identity team asserts that the current enterprise approach to AI security is failing because it treats agents as extensions of human users rather than distinct entities. Because agents are deployed in minutes rather than the weeks or months required for human onboarding, they lack the background checks and identity verification processes standard for employees.

The proposed framework requires four core capabilities: discovery of active agents, hardware-bound cryptographic credentials, authorization at the level of individual actions, and continuous audit logging. This moves security away from 'least privilege'—which might grant an agent access to an entire GitHub repository—toward 'action control,' where an agent is authorized only to perform a specific task, such as merging a single pull request, for a limited window of time.

Cisco is integrating these concepts into its Duo platform, which now treats agents as first-class identities. Through the acquisition of Astrix, Cisco aims to provide visibility into non-human identities, including the secrets and permissions they utilize. The platform supports OAuth and authorization specifications used by the Model Context Protocol (MCP) to manage these scoped permissions.

来源详情: venturebeat.com ↗

为什么这很重要

As enterprises deploy AI agents that operate at machine speed, traditional security models—which grant broad, role-based permissions—create significant risks of unauthorized data access or system manipulation. By shifting to action-level authorization, organizations can restrict agents to specific, time-bound tasks rather than granting them the full permissions of the human users they emulate. This approach addresses the 'identity gap' where agents currently bypass security by inheriting human credentials, providing a necessary layer of governance for autonomous software.

The primary risk identified is that agents currently act as 'proxies' for humans. If a human uses a password-based authentication method, they may inadvertently or intentionally pass those credentials to an agent, making the agent indistinguishable from the human in the eyes of the system.

Traditional Zero Trust architectures, while effective for users and devices, often fail to account for the specific, high-frequency actions of AI agents. By implementing action control, organizations can prevent an agent from performing unauthorized operations, such as force-pushing to production branches, even if the agent has legitimate access to the broader application environment.

This shift represents a fundamental change in enterprise security strategy, requiring organizations to re-evaluate network, endpoint, and data security through the specific lens of agentic behavior rather than relying on legacy IAM assumptions.

Interactive Mechanism

互动机制:它实际上是如何运作的

以交互方式探索这一发展背后的基础技术。

Agent Lifecycle Stage:
1
User Intent & Planning: "Audit customer refund request #4092 and settle payment."
2
Tool Calling: Emits structured JSON call crm_get_transaction(id='4092').
3
Guardrail & Verification:🛡️ Paused: High-value action requires human operator sign-off.
4
Final Settlement: Refund recorded, email receipt dispatched, and audit log stored.
Core takeaway: An AI agent is not just a language model—it is a closed loop of planning, tool invocation, and environment feedback. Production systems require self-healing retries and strict human approval guardrails.
交互式概念检查+10 Points
AI Agents Quiz

An agent must create a draft calendar event for Tuesday at 2 p.m. Which evidence would establish the requested result?

接下来看什么

Security leaders are increasingly prioritizing the discovery of 'shadow' agents already operating within their networks. Future developments will likely focus on how effectively platforms like Cisco’s Duo and its integrated Astrix technology can map non-human identities and enforce granular, just-in-time permissions across diverse cloud and on-premises environments. The industry's ability to standardize these agent-specific identity protocols remains a critical, unresolved challenge.

The effectiveness of discovery tools in identifying 'rogue' or unmanaged agents remains a key metric for security teams. Without a complete inventory of agents, governance policies cannot be enforced.

The industry is moving toward standardizing how agents authenticate. Watch for further adoption of phishing-resistant authentication and hardware-bound credentials as the baseline for non-human identity.

The integration of agent-specific security into broader enterprise stacks will be a major trend. Cisco’s focus on the Model Context Protocol (MCP) suggests that interoperability between agent frameworks and security platforms will be a critical area for development in the coming months.

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