Role Based Access Control For Llm Interactions

via “role-based access control and team collaboration”

Open-source AI observability with conversation replay and user tracking.

Unique: Implements role-based access control at the dashboard and API level, with optional SSO/SAML integration for centralized identity management. Roles control access to conversations, prompts, and settings.

vs others: More secure than shared credentials because roles are granular; more integrated than external access control because RBAC is built into Lunary.

via “multi-tenant-authentication-and-authorization”

Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM]

Unique: Implements hierarchical access control with model access groups supporting wildcard patterns (e.g., 'gpt-4*' to allow all GPT-4 variants), combined with per-key budget caps and rate limits enforced at the proxy layer before requests reach LLM providers

vs others: More granular than cloud provider IAM; supports model-level access control and per-key budgets without requiring separate cloud infrastructure, enabling fine-grained cost control and access policies

via “multi-tenancy and role-based access control”

Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.

Unique: Implements multi-tenancy at the database level with row-level security, ensuring complete data isolation between tenants. RBAC is enforced at the service layer, preventing unauthorized access to agents, conversations, and memory blocks.

vs others: More secure than application-level multi-tenancy by using database-level isolation; differs from single-tenant deployments by supporting multiple organizations on shared infrastructure without code changes.

via “role-based-access-control-with-skill-permissions”

Open-source enterprise AI workforce platform — containerized roles, declarative skills, MCP tools, policy-driven security, K8s-native scheduling

Unique: Implements declarative, fine-grained RBAC where each agent role has explicit permissions for skills and tools, with enforcement at the gateway and executor layers. Permissions are checked before execution, not after, preventing unauthorized access.

vs others: Provides stronger access control than agent-level permission checks in LangChain or AutoGen, with centralized enforcement and detailed audit trails. Requires more upfront configuration but enables enterprise-grade access governance.

via “llm-security-and-safety-considerations”

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Unique: Provides dedicated security section with coverage of prompt injection, data privacy, model poisoning, and compliance. Links to both security research and practical frameworks, enabling practitioners to implement security and safety measures appropriate to their threat model.

vs others: More LLM-specific than generic security guides; more practical than research papers because it includes implementation guidance and best practices

via “read-write capability gating with permission control”

Enhanced PostgreSQL MCP server with read and write capabilities. Based on @modelcontextprotocol/server-postgres by Anthropic.

Unique: Implements MCP-level query classification and gating to enforce read-only or read-write policies before execution, preventing LLMs from executing unintended mutations through a declarative policy model

vs others: Provides application-level permission control without requiring PostgreSQL role-based access control (RBAC) configuration, making it easier to deploy with existing databases

via “built-in authentication and authorization enforcement”

** (Python) - Open-source framework for building enterprise-grade MCP servers using just YAML, SQL, and Python, with built-in auth, monitoring, ETL and policy enforcement.

Unique: Integrates declarative policy-as-code (YAML/Python) directly into the MCP request pipeline with support for RBAC and ABAC patterns, evaluated before tool execution, rather than relying on external authorization services or database-level permissions alone

vs others: Provides centralized, MCP-aware access control that can enforce policies across heterogeneous tools and data sources in a single configuration layer, versus scattering authorization logic across individual tool implementations or relying solely on database permissions

via “access control and data governance through llm context”

** - Windsor MCP (Model Context Protocol) enables your LLM to query, explore, and analyze your full-stack business data integrated into Windsor.ai with zero SQL writing or custom scripting.

Unique: Integrates Windsor's permission model directly into query execution, enforcing row-level and column-level access controls transparently to the LLM while exposing access constraints through MCP so the LLM can understand and reason about data availability

vs others: Provides transparent access control enforcement at query time rather than requiring manual permission management; differs from generic database access control by optimizing for LLM-driven queries and exposing permission constraints through the MCP interface

via “bidirectional-llm-user-communication-loop”

** 📇 - Enables interactive LLM workflows by adding local user prompts and chat capabilities directly into the MCP loop.

Unique: Implements synchronous bidirectional communication where LLMs can pause execution to request user input via blocking MCP tool calls, receive responses, and incorporate them into reasoning, creating a true collaborative loop rather than one-way communication.

vs others: Differs from context-injection approaches where user input is pre-loaded into context; instead, LLMs actively request input when needed, reducing hallucination and enabling dynamic decision-making based on real-time user responses.

via “tool call access control with role-based policies”

Vloex MCP Gateway — stdio proxy for MCP tool call governance

Unique: Implements RBAC at the MCP proxy layer, allowing centralized tool access policies without modifying individual tool implementations or requiring client-side enforcement

vs others: More maintainable than distributing access control logic across multiple MCP servers, and more reliable than client-side enforcement since policies are enforced at the protocol boundary

via “role-based-access-control-and-team-collaboration”

Open-source LLMOps platform for prompt management, LLM evaluation, and observability. Build, evaluate, and monitor production-grade LLM applications. [#opensource](https://github.com/agenta-ai/agenta)

via “configurable path-based access control with allowlist enforcement”

** - Secure file operations with configurable access controls

Unique: Uses a declarative allowlist model enforced at the tool invocation layer, validating paths before any filesystem operation executes. The reference implementation demonstrates this pattern clearly, making it easy for operators to understand and audit what access is granted.

vs others: More explicit and auditable than capability-based security or role-based access control, making it easier for non-technical operators to understand what an LLM agent can and cannot access.

via “data access policy enforcement and auditing”

Transcend MCP Server — Data Discovery tools.

Unique: Implements access control as a first-class MCP server capability rather than delegating to external systems, enabling policy enforcement at the protocol level with built-in audit logging and fine-grained sensitivity-aware access decisions

vs others: Unlike database-level access controls that operate on entire tables, this enables field-level and operation-level access control with sensitivity-aware policies, and unlike external policy engines, this keeps enforcement close to the data access point

via “role-based access control with granular permissions”

** - MySQL database integration with configurable access controls and schema inspection

Unique: Implements access control at the MCP server boundary rather than relying on MySQL user accounts, enabling fine-grained per-client restrictions without creating separate database users for each agent or client identity

vs others: Provides centralized access control for multiple agents sharing a single MySQL connection, whereas alternatives like separate MySQL users require managing N user accounts and connection strings for N agents

via “role-based access control and contract visibility management”

AI powered contract management software

via “role-based access control for llm interactions”

via “fine-grained-access-control”

via “user-and-application-access-control”

via “granular permission and access control”

via “role-based access control and data isolation”

Unique: Implements real estate-specific role definitions (agent, team lead, broker, lender, title company, attorney) with transaction-aware access control where external parties see only documents relevant to their role in a specific transaction, rather than generic user/admin role models

vs others: More sophisticated than basic user/admin access control because it understands real estate transaction roles and can dynamically filter data based on stakeholder role in a specific transaction, enabling secure multi-party collaboration