论文提出 agentic 归属架构，自动推断 AWS 云资源负责人

架构分两阶段工作：先离线对组织进行画像，生成经过测试的信号流程和一份 ownership playbook；再由一个协调智能体加多个并行调查智能体，从工作记录、代码历史、审计日志和身份目录中推断每个资源的归属。论文把所有权定义为按关系强度排序的五种模式，置信度由规则计算。

在一个组织的 100 个资源、50 种 AWS 资源类型上实测，该架构达到 92.1% 加权召回和 86.6% 加权精度，而未预置信息的通用智能体只有 54.8% 召回和 77.4% 精度，成本仅为其每单位覆盖的 0.55 倍。差异主要来自通过工作记录和代码历史找到那些没有显式 owner 的资源构建者。

Agentic Attribution of Cloud Resource Ownership

Security tools raise findings across cloud environments, and AI agents now investigate and prioritize them. A finding is still resolved by people: it has to reach the people responsible for fixing it and accountable for the resource it concerns. Ownership is the organizational context that decides who that is. Where recorded, it is declared by hand in configuration management databases or owner tags and goes stale unless kept current; accounts are shared, and automation creates much of the infrastructure. The knowledge sits in work records, code history, audit logs and identity directories, encoded in each organization's own conventions. Recovering it poses four challenges: the signal is scattered across sources, none of them authoritative; the conventions that carry it differ between organizations; reaching an owner takes multi-hop links; and those related to a resource differ in the strength of their relation. We define ownership as a set of entities, people or teams, each related to the resource by one of five patterns ordered by strength. We present a reference architecture for agentic ownership attribution that derives ownership from these records instead of declaring it: it profiles an organization offline into tested signal procedures and an ownership playbook, then attributes each resource with a coordinator agent and parallel investigators, with confidence computed by rules. On 100 resources of 50 Amazon Web Services (AWS) resource types in one organization, we pool the architecture's candidates with those of an unprimed general-purpose agent and verify every evidence piece against the records. The architecture reaches 92.1% weighted recall and 86.6% weighted precision, against 54.8% and 77.4%, at 0.55 times the agent's cost per unit of coverage. The difference lies in people who build and change resources without an explicit owner, found through work records and code history.

This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include AWS, 云安全, 智能体, ownership attribution.

Source attribution: curated from arXiv cs.AI (@Tiltan Gilat, Gal Engelberg, Ariel Zadok, Leon Goldberg).