Authorization for Enterprise AI
Starting with Permission-aware layer for RAG
Control what enterprise AI systems can access and return, without breaking your existing data, IAM, or RAG architecture.
Architecture Overview
The Challenge
Enterprise RAG systems break authorization by default.
File-level permissions do not survive chunking, embedding, and retrieval. Once that context is lost, AI systems can surface information users were never allowed to see. This is not an edge case. It is a structural flaw in how RAG systems are built today.
Data Leakage Risk
RAG returns data regardless of user permissions, exposing confidential information.
High Engineering Overhead
Teams rebuild authorization logic inside every RAG pipeline.
Inconsistent Behavior
Different data sources enforce different rules.
Compliance Violations
Organizations can't audit data access, failing compliance requirements.
Trust Erosion
Teams don't trust AI systems with sensitive internal data.
No Integration Story
Requires rewriting pipelines to fit specific frameworks.
What Asgar AI changes
✓ Authorization correctness
AI systems only retrieve data users are allowed to access.
✓ Auditability by design
Every access decision is explicit and traceable.
✓ Infra-agnostic, Framework Agnostic, Drop-in Solution
No refactoring of data pipelines or IAM systems. Deployed in your own cloud. Zero data leaves your environment.
✓ Infrastructure independent
Works across vector databases, LLMs, and data sources.
Where teams use Asgar AI
Internal Assistants
Ensure each user only sees what they're cleared for.
Production RAG platforms
Prevent permission leakage as usage scales.
AI Agents and Workflows
Control what your agents can read or return.
Knowledge and support systems
Deliver answers without violating access policies.
Platform Teams
Standardize access control across all internal AI apps.
A Simple Layer Between Identity and AI
1. ASGAR Permission Agent
Runs alongside your ingesting pipeline and identity provider, syncing ACLs and user access levels in real-time.
2. ASGAR Retrieval SDK
Integrates into your retrieval pipeline, enforce authorization based on user permissions on source data, before passing to LLMs as context.
3. ASGAR Compliance Audit
Full audit logs of who accessed what, when, enabling compliance reporting and forensics.
Key Features
✓ Real-time Permission Syncing
Syncs access control between Okta, Azure AD, or your IdP and your original Datasource (Sharepoints, Slack, Confluence etc.) in real-time.
✓ Enforce authorizations based on permissions
Ensuring RAG outputs respect user permissions based on source data before LLM processing.
✓ Audit Logs
Complete compliance trail showing access patterns and permission decisions.
✓ Infra-agnostic, Framework Agnostic, Drop-in Solution
Work with LangChain, LlamaIndex, LiteLLM or any curreny ingesting RAG pipeline or Identity provider you are running, easily integrated with popular data sources like Confluences, Sharepoints, OneDrive etc.
✓ Deploying into your own Environment
Deployed in your own cloud. Zero data leaves your environment. Enterprise-grade access control for all your AI systems.
✓ Chunk-based level Permission
Chunk-level permissioning ensures only the specific sections a user is allowed to see ever reach the AI system.
Why It Matters
For AI, Engineering, Data or Security leaders
- →Safeguard sensitive data while enabling AI innovation
- →Meet regulatory requirements with audit trails
- →Reduce security risks from data leakage
For Engineers
- →No complex permission logic to build
- →Integrate in minutes, not months
- →Stop reinventing permission frameworks
We're witnessing the shift from RAG to Context and Semantic Layers where multiple AI Agents and humans collaborate on complex tasks on top of sensitive, proprietary internal data.
Asgar AI is the permission fabric that makes this possible.
Built by people who've felt the pain first hand
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