125 lines
7.4 KiB
Markdown
125 lines
7.4 KiB
Markdown
# Role Aware Hierarchical Context File System
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Mapping agents to user identities and **leveraging POSIX-style permissions combined with role-based context filtering** can give you precise, secure control over what context each agent “sees.” This is especially important when you're exposing sensitive credentials (e.g. API keys or SSH certs).
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Let’s break it down:
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---
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## 🔐 Agent as User | Role-Based Context Control via POSIX Permissions
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### 1. **Agents as POSIX Users or Service Accounts**
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* Each AI agent is assigned a unique identity—like a “user” or service account—and optionally belongs to **groups**.
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* POSIX permissions (owner/group/other) or ACLs (Access Control Lists) dictate what filesystem paths each agent can `read`, `write`, or `execute`.
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* You can map these identities to MCP clients via OAuth or token-based authentication, so context access is always controlled per-agent ([Playbooks][1], [Model Context Protocol][2]).
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### 2. **Role-Based Access Control (RBAC)**
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* Roles (e.g. "CSS-tuner", "db-tuner", "devops") are mapped to agent identities.
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* Path-level access and context exposure are controlled based on roles. A CSS agent lacks permission to access `/credentials/` or `/deploy/production` folders.
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* This aligns with classic RBAC: Permissions tied to roles, users assigned to roles ([IARJSET][3]).
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### 3. **Context-Aware / Attribute-Based Access Control (ABAC/PBAC)**
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* You can extend RBAC with **context-aware policies**: time of day, agent trust score, project state, or current task scope.
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* For example, an agent gets elevated access for configuration files during runtime deployment sessions, but otherwise operates in a restricted sandbox ([Tony Kipkemboi][4], [WorkOS][5]).
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### 4. **Graph-Based Access Control (ReBAC/GBAC)**
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* If agents have more complex relationships—like delegation, hierarchical roles, or cross-project scopes—you can use **graph-based policies**.
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* GBAC lets you model agents, roles, context blobs, and paths as nodes in a graph, and define dynamic rules based on attributes and relationships ([Wikipedia][6]).
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---
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## 🗂️ How This Could Work in Your HCFS + MCP Server
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### A. **Filesystem Interface (FUSE + MCP)**
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* Expose POSIX-style file operations: `readFile`, `listDir`, etc., subject to permission enforcement.
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* Under the hood, operations map to context blobs linked by path metadata.
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* Each agent is authenticated as a user; permissions at the FS level enforce role-based allowances.
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### B. **Authorization Engine**
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* Use a policy engine (e.g. Cerbos, OPA) to evaluate permissions dynamically based on agent roles, path, context attributes, and real‑time factors ([cerbos.dev][7], [cerbos.dev][8], [Cloud Security Alliance][9]).
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* Example:
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```text
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Allow CSS-agent to read /projects/website/css/*
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Deny CSS-agent to read /credentials/* or /deploy/prod/*
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```
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* You can implement ABAC rules like:
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* “Allow read if agent.role == ‘devops’ AND time in deploy window”
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* “Allow context subscribe only if task = 'analytics' AND trust\_score > threshold.”
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### C. **Auditing & Lifecycle**
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* Every context access is logged with agent ID, role, timestamp, and path info.
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* Supports revocation (role removal), role transitions, and temporary escalation via just-in-time tokens (e.g. via OAuth scope grants) ([Magai][10]).
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---
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## 🧠 Context-Aware Role Segmentation: Why It Matters
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* **Minimize attack surface**: Agents only see what they need—no risk of leaking keys or tuning internal modules.
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* **Operational clarity**: Each agent’s domain and context exposure is explicit, auditable, and explainable.
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* **Scalability**: Adding new agents/roles doesn’t require rewriting FS or context logic—just update the permission policies.
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---
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## 📋 Summary Table
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| Capability | Approach |
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| ------------------------- | -------------------------------------------------------- |
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| Agent identity & grouping | Map agents to POSIX users/groups |
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| Path-level access control | POSIX permissions or ACLs on FS subtree |
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| Role-based visibility | RBAC policies controlling read/write of context blobs |
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| Dynamic access context | ABAC/PBAC based on agent role, task, trust, time |
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| Complex relationships | Graph-based access (GBAC/ReBAC) for cross-role semantics |
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| Auditing & revocation | Authentication/auth logs and just-in-time role change |
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---
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## 🛠️ Example: CSS Agent vs DevOps Agent
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* *CSS Agent* (`css-tuner` role):
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* Access: `/projects/site/css/**`, `/projects/site/html/**`
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* No access: `/deploy/**`, `/credentials/**`, `/api_keys/**`
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* *DevOps Agent* (`devops` role):
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* Access: Everything except `/source-code/experimental/*` if not authorized.
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* Elevated access during deploy operations (time-bound via ABAC).
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---
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## 📝 Suggested Implementation Stack
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* **FUSE layer** backed by MCP endpoints, enforcing path-based checks.
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* **Policy engine**: Cerbos or OPA for RBAC/ABAC logic ([cerbos.dev][7], [Model Context Protocol][2], [Playbooks][1], [HackerNoon][11], [WorkOS][5]).
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* **Authentication**: OAuth flows via MCP; agents get scoped tokens with role claims ([stytch.com][12]).
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* **Graph-based augmentation**: If you need delegation or inheritances, build path and agent relationship graph (e.g. Neo4j + policy engine referencing it).
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---
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## ✅ In Conclusion
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Mapping agents to users/groups with POSIX semantics and augmenting with **role-based AND context-aware access control** is not only valid but strongly practical.
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It lets you **control who sees what context**, prevent sensitive blob exposure, and maintain flexible, secure context-sharing for agentic multi-agent environments.
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[1]: https://playbooks.com/mcp/shtse8-filesystem?utm_source=chatgpt.com "Filesystem MCP server for AI agents - playbooks.com"
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[2]: https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization?utm_source=chatgpt.com "Authorization - Model Context Protocol"
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[3]: https://iarjset.com/wp-content/uploads/2025/03/IARJSET.2025.12320.pdf?utm_source=chatgpt.com "Context Secure AI For Role Based Access Control"
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[4]: https://www.tonykipkemboi.com/blog/agent-authentication-rbac?utm_source=chatgpt.com "rbac for ai agents | Tony Kipkemboi"
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[5]: https://workos.com/blog/securing-ai-agents?utm_source=chatgpt.com "Securing AI agents: A guide to authentication, authorization, and defense — WorkOS"
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[6]: https://en.wikipedia.org/wiki/Graph-based_access_control?utm_source=chatgpt.com "Graph-based access control"
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[7]: https://www.cerbos.dev/blog/permission-management-for-ai-agents?utm_source=chatgpt.com "Access control and permission management for AI agents: building with ..."
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[8]: https://www.cerbos.dev/blog/dynamic-authorization-for-ai-agents-guide-to-fine-grained-permissions-mcp-servers?utm_source=chatgpt.com "Dynamic authorization for AI agents. A guide to fine-grained ..."
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[9]: https://cloudsecurityalliance.org/blog/2025/03/11/agentic-ai-identity-management-approach?utm_source=chatgpt.com "Agentic AI Identity Management Approach | CSA"
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[10]: https://magai.co/what-is-abac-for-ai-systems/?utm_source=chatgpt.com "What Is ABAC for AI Systems?"
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[11]: https://hackernoon.com/lang/rw/ai-agents-arent-production-ready-and-access-control-might-be-the-reason?utm_source=chatgpt.com "AI Agents Aren’t Production Ready - and Access Control Might Be the Reason | HackerNoon"
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[12]: https://stytch.com/blog/oauth-for-mcp-explained-with-a-real-world-example/?utm_source=chatgpt.com "OAuth for MCP explained with a real-world example"
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