Give Employees AI Tools Without Sending a Single Real Name to LLM Cloud Servers
Give Employees AI Tools Without Server Risk.
Traditional network firewalls can't inspect encrypted LLM prompts. PrivacyScrubber isolates and replaces corporate PII locally at the keyboard level in under 1ms — long before any data reaches OpenAI, Anthropic, or internal LLM gateways.
Video Brief: Securing the LLM Layer
Shadow AI Governance: The "Yes-to-AI" Framework
Legacy Approach: Prohibition
Blocking AI sites forces employees into unmanaged "Shadow AI" loops using personal devices and unencrypted tunnels.
The ZTDS Paradigm: Empowerment
Empower teams to use any LLM by stripping PII at the keyboard level. The CISO maintains audit integrity without ever seeing or storing raw data.
Architecture Layer Cake: Zero-Server Orchestration
CLIENT-SIDE INGESTION
Data remains strictly local; processed in-RAM using WebAssembly. No packet leaves the device baseline.
PSEUDONYMIZATION & ZTDS
Deterministic entity mapping via AES-GCM local seed. Labels applied instantly at the semantic boundary.
FOUNDATIONAL AI MODEL
Receives only sanitized "safe" payloads. Zero PII recall risks during future model re-training cycles.
Strategic Implementation Roadmap
Audit & Baseline
Map high-risk LLM ingestion points across departments (Legal, HR, Dev).
Shadow AI Amnesty
Deploy PrivacyScrubber to provide a "safe path" for existing unmanaged AI usage.
Policy Enforcement
Hardcode ZTDS as a prerequisite for all enterprise GenAI workflows.
Continuous Audit
Automate compliance reporting using local-only audit logs.
Standardized Compliance & Control Mappings
OWASP Top 10 for LLMs
Edge-level sanitization addresses three critical OWASP LLM vulnerabilities at the semantic layer:
- LLM01: Prompt Injection: Intercepts prompts at the DOM level, neutralizing structural exploits before model inference.
- LLM02: Insecure Output Handling: Performs detokenization (Reveal) 100% client-side, preventing leaks over insecure networks.
- LLM06: Sensitive Info Disclosure: Edge-filtering of credentials, API tokens, and customer identifiers.
NIST AI Risk Management
Direct alignment with the NIST AI RMF 1.0 core sub-functions:
- GOVERN-1.2: Enforces organizational policies for acceptable generative AI use.
- MAP-1.5: Tracks and isolates data leakage vectors before network egress.
- MEASURE-2.6: Quantifies masked entity counts via browser-local telemetry.
- MANAGE-1.5: Actively mitigates exfiltration risks at the prompt perimeter.
Regulatory Data Masking
Enforces technical controls required by global GRC standards:
- ISO 27001 A.8.11: Automates data masking compliance at the client endpoint.
- GDPR Article 25: Implements privacy-by-design through local de-identification.
- HIPAA Safe Harbor: De-identifies 18 PHI elements locally before cloud routing.
- DORA / NIS2: Strengthens operational resilience against supply chain exfiltration.
AI Governance Board Recommendations
When forming your company's GenAI Committee, legacy blocklists (MDM-level blocks of OpenAI) fail because they create massive shadow AI backchannels. Governance Boards should follow these three core guidelines:
- Adopt a "Yes-to-AI" Policy: Provide employees with approved productivity tools paired with edge-level sanitization.
- Enforce Local Pre-Sanitization: Mandate that all prompt inputs pass through a local ZTDS filter before server exfiltration.
- Standardize Custom Rules: Set up unified regex profiles for proprietary project names, API keys, and corporate identifiers.
TPRM & Vendor Risk Assessment Bypass
Standard SaaS procurements are bottle-necked for 3–6 months during Third-Party Risk Management (TPRM) audits because vendors act as Data Processors. ZTDS eliminates this friction:
- No Processor Status: Because no data is sent to our servers, we never act as a Data Processor or Sub-processor.
- Pre-approved CAIQ: 80% of standard Consensus Assessments Initiative Questionnaire answers are immediately rated "N/A".
- Instant Deploy: Legal teams approve PrivacyScrubber as an on-premise local utility, bypassing the DPA backlog.
Deployment Economics: Time-to-Value
Legacy Cloud DLP Hub
Requires Vendor Risk Assessment (VRA), Data Processing Addendum (DPA) revisions, and network-wide proxy certification.
ZTDS Native Deployment
Zero data transit means zero regulatory friction. Bypasses DPA requirements and VDI constraints entirely.
CISO Objection Handling Matrix
"Won't masking PII break the AI's contextual understanding?"
"ZTDS uses semantic placeholders (e.g., [NAME_1]) which preserve the grammatical structure and relationship context. The model understands the intent without ever seeing the identity."
"How do we know the engine itself isn't a supply chain risk?"
"PrivacyScrubber is 100% auditable via 'Network-Zero' verification. It has no backend, no telemetry, and operates in a hardened browser sandbox, preventing outbound data exfiltration."
"If a token leaks into an AI model, can't we just scrub the Git history later?"
"No. Once an LLM ingests a secret, it is baked into the neural weights. You cannot 'BFG' a compiled multi-billion parameter model. ZTDS at the edge is the only mitigation."
The 5-Step Audit Procedure for Zero-Trust AI Tools
Do not take any vendor's privacy claims on faith. CISOs and GRC auditors can independently verify PrivacyScrubber's zero-exfiltration architecture in under 60 seconds by executing this standard verification playbook:
Open Developer Console
Right-click anywhere on the PrivacyScrubber interface and select Inspect to open Chrome DevTools.
Monitor Network Tab
Select the Network tab inside the DevTools panel. Clear existing logs to start with a blank stream.
Disconnect Wi-Fi
Enable physical Airplane Mode, or simulate it by selecting Offline from the throttling menu in the Network tab.
Execute Redaction
Paste a sample prompt containing sensitive data (e.g. name, email, credentials) and click Scrub PII.
Verify Zero Requests
Observe the Network panel. Confirm that zero outbound requests were made. This verifies 100% client-side operations.
Download the Printable CISO AI Security Guide
Join 3,000+ security engineering leaders. Instantly access the mathematical proofs, local execution benchmarks, and our SOC 2 compliance checklist.
- VDI & Air-gapped Deployment Protocol
- Pre-filled Vendor Risk Questionnaire (VRQ)
Technical Governance FAQ
How does PrivacyScrubber prevent "Model Learning" from sensitive data?
By removing PII at the semantic boundary (the prompt), the foundational model never receives the original data. This prevents sensitive information from being stored in the model's training weights or short-term context window.
Is ZTDS auditable for SOC 2 Type II compliance?
Yes. Because the sanitization is auditable in the browser's Network tab, CISOs can provide definitive proof that PII never leaves the endpoint. This dramatically simplifies the "Privacy" trust service criteria in your audit.
Does this work for offline air-gapped environments?
PrivacyScrubber functions 100% offline once initial engine components are loaded. We offer a VDI-optimized build for enterprises requiring hard-isolation deployments.