Enterprise RBACNext-Gen Role-Based Access Control for Enterprise AI
Intercept to Authorise
Authorise every AI interaction with granular access controls. Control who can use which LLMs, what data they share, and what governance policies apply.
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AI Access Problem is Only Getting Worse
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.
of enterprises lack role-based AI access controls (Gartner, 2024)
And Existing Approaches fall Seriously Short
Organizations typically oscillate between two flawed extremes. Neither approach delivers secure, productive AI access at scale.
Too Open
Massive security & compliance risk
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.
Security Breaches
Sensitive data flows to external LLMs with no controls. Trade secrets, PII, and confidential strategies exposed.
Compliance Violations
GDPR, HIPAA, SOX, and industry regulations ignored. Fines and legal liability accumulate quickly.
Data Leakage
Customer data, financial records, and intellectual property leak through unfiltered AI queries.
Zero Auditability
No way to track who accessed what, when, or why. Regulatory audits become a nightmare.
Too Restrictive
Kills productivity & breeds shadow AI
IT blocks all AI tools, pushing employees to use shadow AI on personal devices. Zero governance, zero visibility, maximum risk - and frustrated employees.
Shadow AI Explosion
Employees use personal ChatGPT, Claude, or unauthorized tools. You have no visibility, no control, no audit trail.
Productivity Collapse
Teams can't leverage AI for routine tasks. Competitive advantage evaporates. Manual work increases.
Talent Exodus
Top engineers, analysts, and creatives leave for competitors who let them use modern tools.
Hidden Risk Exposure
Unauthorized AI use means data ends up on public cloud services. Worse security than controlled access.
Meet RBAC Designed for Rapidly Evolving AI
Walled AI s RBAC sits between your network endpoints and third-party LLM systems ensuring complete independence, control, and separation of concern.
Role policies control what LLMs users can access and what data they have access to. No matter how rapidly third-party AI evolves, ensure complete and continuous RBAC with Walled AI's simple 3-step enforcement.
Intercept
Every request is caught at the network edge before reaching any external LLM system. Browser, desktop app, or API—no request bypasses this point.
Evaluate
Real-time policy check: Is this user's role authorized? Is this LLM in the allowed list? Does the request contain sensitive data that needs protection?
Enforce
Based on evaluation results: Allow the full request through, deny access entirely, or redact sensitive values while allowing the request to proceed.
Outcome
Request reaches LLM, is blocked, or is redacted. Every decision is logged with full context: who, what, when, which policy, and what action was taken.
Intercept
Every request is caught at the network edge before reaching any external LLM system. Browser, desktop app, or API—no request bypasses this point.
Evaluate
Real-time policy check: Is this user's role authorized? Is this LLM in the allowed list? Does the request contain sensitive data that needs protection?
Enforce
Based on evaluation results: Allow the full request through, deny access entirely, or redact sensitive values while allowing the request to proceed.
Outcome
Request reaches LLM, is blocked, or is redacted. Every decision is logged with full context: who, what, when, which policy, and what action was taken.
Control Every Aspect of AI Interactions
Enterprise RBAC goes beyond simple allow/deny decisions. Control LLM access, data policies, and governance rules with granular precision—ensuring every AI interaction aligns with your security, compliance, and operational requirements.
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
Hierarchial Access Controls that Offer Complete Control
WalledAI's RBAC system mirrors your organizational structure. Policies cascade from organisational level to department to team and individual levels- with each level able to tighten (but never loosen) the controls set above it.
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.
Role-based Redaction for a Real Way Forward
Traditional RBAC offers the restrictive choice of either blocking a request or letting it through.
Walled AI offers a better way forward by combining role-based access control with real-time data redaction
With Walled Redact, enterprises can redact data, depending on user roles, before sharing it with third-party AI or LLMs. Each role policy pairs permissions with real-time redaction controls enabling enterprises to use AI according to their permissions, data sensitivity, and policy requirements.
Traditional RBAC
Limited and inflexible
- ✕Binary choice: block or allow entire request
- ✕No granular data protection controls
- ✕Sensitive data exposed to external LLMs
- ✕Limited visibility into AI interactions
- ✕Difficult to enforce compliance at scale
Walled AI RBAC (with WalledRedact)
Flexible and powerful
- ✓Flexible control: allow, block, or redact in real-time
- ✓Role-based data masking and redaction
- ✓Protect sensitive data before sharing with LLMs
- ✓Complete audit trail of all decisions and redactions
- ✓Effortless compliance at enterprise scale
And Extensive Audit Logs for Complete Compliance
Every interaction is logged with Walled AI s RBAC ensuring audit readiness at any moment. Complete visibility into who accessed what, when, why, and what data was protected—giving you the confidence to meet any compliance requirement.
GDPR
General Data Protection Regulation
HIPAA
Health Insurance Portability and Accountability Act
CCPA / CPRA
California Consumer Privacy Act & Privacy Rights Act
GLBA
Gramm-Leach-Bliley Act (financial services)
SOX
Sarbanes-Oxley Act (audit logging)
EU AI Act
Data governance & transparency obligations
PDPA
Singapore Personal Data Protection Act
DPDP Act
India's Digital Personal Data Protection Act
HIPAA
Compliance and audit trail requirements
RBAC for Every Role in Your Organisation
Different roles have different needs. Finance needs data protection. Engineering needs code privacy. Legal needs confidentiality. With Walled AI's Enterprise RBAC, every department gets tailored access controls—the right LLMs, the right data protections, and the right guardrails for their role.
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Experience RBAC Designed for Enterprise AI
See how WalledAI's RBAC gives every employee the right AI access with the right guardrails.
Enterprise RBAC Success Stories
Financial Services Firm Enables 5,000 Employees with GDPR Compliance
Healthcare Organization Deploys AI Without HIPAA Risk
Tech Company Controls Shadow AI with Role-Based Governance
Learn More about Enterprise RBAC
The Blueprint for AI Access Control
A step-by-step guide to implementing role-based access controls that balance security, compliance, and employee productivity without becoming a bottleneck.
ROI of Enterprise AI Governance
Why investing in RBAC infrastructure delivers 10x returns compared to unrestricted AI adoption or complete lockdown approaches.
RBAC in Regulated Industries
How financial services, healthcare, and government organizations implement RBAC to satisfy regulatory requirements while enabling AI innovation.
You May Also Like
Walled Redact
→Multi-modal PII masking for AI prompts. Protect all sensitive data across 50+ entity types.
Walled Protect
→Stop prompt injection, jailbreaks, and unauthorized AI use with 99.7% blocking accuracy.
Walled Correct
→Validate AI responses against ground truth, reduce hallucination rates from 12% to 0.3%.
Data Classification
→Automatically classify data sensitivity across all AI interactions.
Governance Dashboard
→Real-time monitoring and compliance reporting across every LLM.
Shadow AI Detection
→Discover and block unauthorized AI usage across browsers, networks, and SaaS.
Frequently Asked Questions
How granular are WalledAI's access controls?
WalledAI's Enterprise RBAC supports department-level, team-level, and individual-level controls. You can define which LLMs each group can access, what data categories they can share, what sensitivity levels are permitted, and what output validation rules apply.
What is the difference between redaction and blocking?
Blocking denies the entire request—the user can't send data to the LLM at all. Redaction allows the request to proceed but removes sensitive values before the data reaches the LLM, so users get AI assistance without exposing protected information. Redaction enables productivity while protecting data; blocking is a complete access denial. With Enterprise RBAC and Walled Redact, you can apply either action based on role, data sensitivity, and policy.