Axonius 推出 AI Workforce 虚拟员工,AI Companion 与 MCP Server 正式可用
Axonius launches AI Workforce virtual workers as AI Companion and MCP Server go live - SiliconANGLE
Axonius 在 Asset Cloud 平台上线 AI Workforce,让安全团队把持续的修复工作交给自主 AI 虚拟员工,同时 7 月发布的 AI Companion 和基于 Model Context Protocol 的 MCP Server 转为正式可用。AI Workforce 提供 Off、Shadow、Live 三种模式控制自主程度,对外消息和连接系统变更需审批,每个客户在专属环境运行私有 LLM 实例,审计记录区分智能体与人工操作。该平台基于 1,400 多个双向集成和 80 多种资产与暴露实体类型的数据整合,现已向 Axonius 客户开放。
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UPDATED 09:00 EDT / OCTOBER 19 2026
Axonius launches AI Workforce virtual workers as AI Companion and MCP Server go live
by Duncan Riley
Asset intelligence company Axonius Inc. today added Axonius AI Workforce to its Asset Cloud platform to let security teams hand ongoing remediation work to autonomous, artificial intelligence-powered virtual workers.
AI Workforce is meant to take over follow-up work that security teams rarely have time for. A lot of that work exists because asset data ends up split across dozens of tools, and the scheduled jobs most teams rely on only catch conditions somebody thought to define ahead of time. Anything outside those rules is left for a person to sort out, Axonius says, down to working out why a single endpoint agent stopped reporting.
Two tools Axonius announced in July also moved to general availability today. The Axonius AI Companion takes plain-language questions about a customer’s environment and can act on the answer in the same conversation, opening a ticket or tagging devices when asked. Outside AI tools and agents get at the same asset data through the Axonius MCP Server, which runs over the Model Context Protocol.
A mode setting controls how far a worker goes on its own, and Axonius recommends a starting mode for each job based on its own best practices. In Off mode, the worker waits for approval on everything. Teams that want to watch first can use Shadow mode to preview what the worker would do, then switch to Live and let it act automatically within set guardrails. Outbound messages and changes to connected systems count as sensitive and are held for approval before they execute.
Each customer also runs a private large language model instance inside its own dedicated environment, and an audit trail marks every action as either agent-driven or human-driven.
Moshe Ben Simon, chief product officer at Axonius, said every vendor is shipping AI agents now and “the question nobody is answering is what those AI agents are acting on.” An agent that acts on one vendor’s incomplete view of an asset is going to be wrong, he said.
Axonius spent years building a platform that draws on more than 1,400 bidirectional integrations, and Ben Simon described AI Workforce as what can safely be built on top of it. The company made the same data-quality argument in April, when it launched Axonius Verified Assets on the premise that AI agents act on their inputs without questioning them.
AI Workforce, the AI Companion and the MCP Server all run on the same asset model that already powers the Asset Cloud. Axonius builds it by reconciling and deduplicating data across more than 80 asset and exposure entity types. If the software can’t verify something, it is designed to say so.
Theresa Lanowitz, principal analyst for cybersecurity at Omdia, said what stands out is how directly the launch ties autonomous remediation to a verified view of the environment. She expects that pairing, plus human oversight where it matters, to separate AI that security teams “can actually trust” from tools that merely add noise.
AI Workforce is generally available to Axonius customers now. Security engineering teams can see how many AI credits their users and the virtual workers spend, and what that costs.
Photo: Axonius
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