Products
Managed Services
Self-Hosted
Open Source
Resources
AI Amplifies Both Opportunity and Risk
47%
of companies experienced negative consequences from their AI systems use
$4.88M
average cost of a data breach in 2024, up 10% from 2023
4 of 10
OWASP Top 10 AI risks require authorization controls as primary mitigation
8x
increase in enterprise AI application investment
Your AI System Has Three Critical Vulnerabilities
These vulnerabilities are real and they're happening now.
AI can surface any data it can access - including information users shouldn't see
Without boundaries, AI agents can perform damaging actions beyond their intended scope
Traditional access controls break down when data becomes embeddings
The Permission Foundation Your AI Systems Need
How AuthZed Secures Your AI Systems
Permissions-Aware RAG
Make your vector databases respect source systems' permissions
- ✓Maintain Google Workspace, SharePoint, and other external systems' permissions
- ✓Pre-filter or post-filter strategies for optimal performance
- ✓Real-time permission synchronization
Multi-Tenant AI Isolation
Build AI features without mixing customer data
- ✓Flexible architecture beyond database partitioning
- ✓Scale from 10 to 10,000 tenants without changes
- ✓Complete audit trails per tenant
Agentic AI Guardrails
Define and enforce boundaries for autonomous AI systems
- ✓Define what agents can and cannot do before deployment
- ✓Require human approval for high-impact decisions
- ✓Adapt permissions as AI capabilities and trust evolve
The Permission Layer in Practice
The Same Query. Two Different Outcomes.
Scenario: Employee asks AI assistant "What was our Q3 revenue?"
Without AuthZed
- •AI searches all documents
- •Returns confidential board materials
- •No audit trail
- •Compliance violation risk
With AuthZed
- •Checks user's permissions first
- •Returns only authorized financial data
- •Complete audit log created
- •Compliance maintained
Drop-In Authorization for Any AI Stack
Integration in Minutes, Not Months
# 1. Check permissions before vector search
accessible_docs = authzed.lookup_resources(
resource_type="document",
permission="view",
subject=f"user:{current_user}"
)
# 2. Search only authorized embeddings
results = vector_db.search(
query_embedding,
filter={"id": {"$in": accessible_docs}}
)
# 3. Generate response with authorized context
response = llm.generate(query, context=results)
