ScopeMap: planning least-privilege permissions for AI agents
Giving an AI agent a useful task is only half the setup. The other half is deciding what that agent actually needs permission to do. I built ScopeMap , an educational permission-planning demo, with AI assistance in Lovable. It compares a requested capability set with a small, explicit set of requirements for a fictional workflow. Try ScopeMap What the app does Choose one of three synthetic…
ScopeMap is an educational tool designed to help plan the least-privileged permissions for AI agents. It works by comparing the requested capabilities with the specific requirements of a chosen workflow scenario. The tool presents three synthetic workflows: summarizing documents, drafting support responses, and analyzing CSV files.
Users can then select from seven potential capabilities, such as reading files, sending emails, and executing shell commands. The application highlights any unnecessary permissions while explaining why they are not needed for the specific scenario. It also provides a before/after diagram to visually demonstrate the reduction in permissions.
Users can apply the recommended permissions, modify a checklist, and export the plan as JSON. The demo emphasizes that ScopeMap is a rule-based educational planner, not a live AI model or permission-enforcement system. It does not establish the safety of a real tool or guarantee that an agent will respect its permissions. The application uses synthetic examples and has no access to user accounts, personal data, or live inference capabilities.
The purpose of ScopeMap is to help users identify and eliminate unnecessary permissions, keeping evidence separate from authority.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
