Dynamic Capability Scoping for Enterprise AI Agents: A Synthetic Dataset and Three-Source Permission Architecture

Enterprise AI agents often hold static, overly broad credentials for every possible task, expanding security risk. This work proposes dynamic least-privilege scoping via a three-source architecture combining role-based ceilings, a task-context classifier, and policy-derived prohibitions, ensuring agents only access credentials relevant to their current task. The authors release a validated synthetic dataset of 600 enterprise prompts labeled with minimum required permissions, achieving high human-agreement scores and reducing policy violations by 93% through iteration. Applications include enterprise AI security architecture, agent permission management systems, and providing behavioral signals for researching and mitigating LLM agent misuse and misalignment. Authors: Halil Burak Noyan Paper: https://arxiv.org/abs/2607.22445v1