Enterprise RBAC
Granular AccessControls.
Super-defined role-based access control for every AI interaction. Control who can use which LLMs, what data they can share, and what governance policies apply - down to the team level.
Backed & Trusted By Industry Leaders
The Access Problem
of enterprises lack role-based AI access controls
Gartner, 2024
higher breach risk with flat AI permissions
Ponemon Institute
Most organizations take an all-or-nothing approach to AI access. Either everyone has unrestricted access (massive risk), or AI tools are locked down entirely (zero productivity gain). Neither approach works.
Too Open
Junior employees can access the same LLMs and data as C-suite executives. An intern can query AI about M&A strategy. A contractor can extract customer databases.
Too Restrictive
IT blocks all AI tools, pushing employees to use shadow AI on personal devices. Zero governance, zero visibility, maximum risk - and frustrated employees.
The WalledAI Way
Granular, policy-driven access at every level. The right people get the right AI capabilities with the right guardrails. Everyone is productive, everything is governed.
Access Control Hierarchy
WalledAI's RBAC system mirrors your organizational structure. Policies cascade from org to department to team to individual - with each level able to tighten (but never loosen) the controls set above it.
Each policy level pairs permissions with real-time redaction controls, and every enforcement decision is reported in the AI governance dashboard.
Organization Admin
Full platform control. Define global policies, manage departments, approve LLM providers, set data sensitivity ceilings, and configure deployment settings. Typically: CISO, CTO, or Head of AI Governance.
Example: Sets a global policy: No employee can share Restricted-level data with any external LLM, regardless of department.
Department Admin
Manage LLM access, data policies, and guardrails for their department. Cannot override org-level policies. Can add department-specific restrictions.
Example: Finance department admin restricts AI usage to GPT-4 and Claude only (no open-source models) and adds a custom guardrail blocking financial projection queries.
Team Lead
Configure team-specific guardrails and monitor usage within defined boundaries. Cannot change department policies. Can add team-specific restrictions.
Example: A trading desk lead enables real-time market data queries via AI but blocks any queries about competitor trading strategies.
End User
Use approved LLMs within configured guardrails. All interactions are governed by layered policies from org, department, and team levels. Full transparency into what policies apply.
Example: A junior analyst can use AI for data analysis and report drafting, but all queries are subject to data masking, off-topic filtering, and hallucination detection.
Control who uses AI - and how
Map AI permissions to your existing role hierarchy. Department-level isolation. Custom risk profiles per team.
Enterprise AI RBAC and Redaction Controls
WalledAI combines role-based access control with real-time redaction so enterprise teams can use AI according to their permissions, data sensitivity, and policy requirements. Admins can control who uses specific AI tools and what sensitive information must be masked before it reaches a model.
What You Can Control
LLM Access Policies
- Which LLMs each team can use
- Token/usage limits per role
- Model-specific guardrail configs
- Approved use cases per department
- Cost allocation and budget caps
- API key management per team
Data Policies
- Data sensitivity access levels
- Department-based data boundaries
- Cross-department sharing rules
- Data classification enforcement
- Geo-fencing and data residency
- Temporal access controls (time-based)
Governance Rules
- Custom guardrails per team
- Escalation workflows and approval chains
- Audit requirements per role
- Compliance policy mapping
- Shadow AI detection and blocking
- Usage analytics and reporting
RBAC in Action
Here's how a 5,000-person financial institution uses WalledAI's RBAC to manage AI access across the organization.
The same department model underpins legal AI access controls for matter teams and manufacturing AI access controls for engineering and plant operations.
Wealth Management
500 relationship managersGPT-4 and Claude for client correspondence and portfolio analysis. All client PII masked via Walled Redact. No access to competitor analysis queries.
Client names, account numbers, and portfolio values never reach external LLMs.
Compliance & Risk
50 compliance officersFull LLM access for regulatory research. Walled Correct at 98% threshold. All queries logged with full audit trail. Custom guardrails blocking queries about specific ongoing investigations.
Compliance team gets the most capable AI tools with the strictest accuracy requirements.
IT & Engineering
300 developersAccess to code-focused LLMs (Copilot, Claude). Source code is masked for proprietary algorithms. Open-source model access for internal tools. No access to customer or financial data.
Developers can use AI freely for code - but proprietary algorithms stay protected.
Marketing
100 marketersAll LLMs for content creation and campaign analysis. Customer segment data accessible but individual PII masked. Brand safety guardrails ensure all AI-generated content meets brand guidelines.
Creative freedom with data protection - marketers create, guardrails protect.
Customer Story
Multinational Conglomerate Deploys AI to 10,000 Employees with Zero Policy Violations
Challenge
A diversified conglomerate spanning financial services, real estate, and hospitality wanted to provide AI access to all 10,000 employees. Each business unit had different regulatory requirements, data sensitivity levels, and AI maturity. A one-size-fits-all approach was impossible.
Solution
Deployed WalledAI's Enterprise RBAC with a four-tier hierarchy - organization, business unit, department, and team. Each business unit admin configured their own policies within the boundaries set by the CISO at the org level. 47 different role configurations were created.
Results
All 10,000 employees gained AI access within 12 weeks. Zero cross-department data breaches. Zero policy violations escalated to the CISO. Each business unit maintained regulatory compliance with their specific requirements. Shadow AI usage dropped to near zero.
Employees Enabled
Role Configurations
Policy Violations
Full Deployment
Related Resources
The CISO's Guide to AI Access Control
How to design an AI access control strategy that balances productivity with security - without becoming a bottleneck.
Read more about The CISO's Guide to AI Access ControlRead moreShadow AI: The Hidden Risk
When employees can't use AI officially, they use it unofficially. Why restrictive policies create more risk than governance frameworks.
Read more about Shadow AI: The Hidden RiskRead moreRBAC for Regulated Industries
How financial services, healthcare, and government organizations design AI access controls that satisfy both regulators and employees.
Read more about RBAC for Regulated IndustriesRead moreDefine who uses AI and how
See how WalledAI's RBAC gives every employee the right AI access with the right guardrails.
Frequently Asked Questions
Authoritative references
Primary sources behind the standards, regulations and research referenced on this page.
- NIST Role-Based Access Control projectOrigin and reference model for RBAC.
- ISO/IEC 27001 information security managementInternational standard for information security controls.
- NIST SP 800-63 Digital Identity GuidelinesAuthentication and identity assurance levels that complement RBAC.
- NIST SP 800-53 Rev. 5 security and privacy controlsControl catalogue referenced by most enterprise compliance programmes.