Not Just a Vendor Promise — Architectural Elimination
Most security tools ask you to trust a cloud proxy and a 30-page contract. PrivacyScrubber eliminates the external processor path: sensitive PII is tokenized in 100% local browser RAM before any network request occurs, establishing verifiable Technical Safe Harbor (GDPR Art. 32, HIPAA Safe Harbor, EU AI Act Art. 50) with zero subprocessors.
The Myth of the Cloud AI Guarantee
Most AI vendors say "we don't train on your data." That is a contractual promise — not a technical safeguard. Terms can be renegotiated, and server-side abuse-monitoring logs remain outside your physical control. If their infrastructure is breached, raw payloads are compromised. PrivacyScrubber takes an architectural approach: sensitive identifiers never leave the browser.
Traditional Approach (Cloud Proxy / DPA)
- Vendor promise: "We won't train on data"
- 6–8 week Vendor Risk Assessment cycle
- Raw data still traverses vendor servers
- Breach response requires full forensic investigation
ZTDS™ Approach (Architectural Safe Harbor)
- Zero raw data transmitted — 0 external subprocessors
- 80% of security questionnaire controls = "N/A (Zero Egress)"
- RAM-only tokenization, verifiable via Airplane Mode
- Drastic blast-radius reduction (only surrogate tokens reach AI)
| Threat Vector | Without ZTDS (Cloud Proxy / Raw Prompts) | With ZTDS™ (PrivacyScrubber) |
|---|---|---|
| AI Provider Security Breach | Exfiltration of raw customer prompts, credentials, and business secrets stored in LLM history. | Exfiltrated payloads contain only surrogate tokens (e.g. [NAME_1]). Direct identifier exposure is eliminated. |
| Rogue AI Insider / Support Access | Vendor engineers with database access can view raw employee prompts and internal data. | Only tokenized placeholders are seen by the vendor. Real identities remain in local workstation RAM. |
| GDPR Art. 32 & 34 Safe Harbor | Unredacted data exposure triggers mandatory supervisory authority reporting and data-subject notifications. | Technical Safe Harbor (Art. 32 & 34(3)(a)). Pre-egress pseudonymization in client RAM renders intercepted prompts unintelligible to third parties. |
| Incident Response Timeline | Weeks of forensic audit log tracing, corporate impact assessments, and regulatory inquiry defense. | Auditors verify signed SHA-256 local sanitization receipts proving zero raw PII egress. |
Statutory Risk Minimization & Technical Safe Harbor
When unredacted PII leaks to an external AI provider, organizations face statutory reporting and administrative liability. By enforcing client-side pseudonymization and de-identification prior to transmission, PrivacyScrubber establishes verifiable Technical Safe Harbor under GDPR Articles 25 & 32, HIPAA Safe Harbor (45 CFR § 164.514(b)), and SOC 2 Type II (CC6.1 & CC9.1). Implementing endpoint PII masking with Zero-Trust Data Sanitization (ZTDS™) combines 100% direct identifier zero-egress with contextual entropy reduction across 30 specialized industry profiles.
Hardened Audit Standards
A unified compliance posture for privacy-first AI deployment.
Privacy by design at the engineering layer.
Ensures transparency and data minimisation for high-risk models.
No data persistence on untrusted infrastructure.
Data masking as a core organisational control.
Federal PII minimisation and transparency controls.
Satisfies Safe Harbor de-identification requirements.
Transparency Matters
We provide full architectural transparency for enterprise security audits. Because there is no backend processing server, there is no opaque remote server where your prompt data could be stored or logged. This framework is detailed further in our CISO AI Security Guide, providing the verifiable engineering proof for zero-exfiltration AI workloads.
Active Airplane Mode Verification
Most compliance tools require you to trust their vendor's cloud SOC 2 attestation. We don't. Disconnect your internet right now or click below to execute a live in-RAM diagnostic check via our Zero-Trust Technical Architecture without sending a single byte to any external server.
[NAME_1] · [SSN_1] (0 B Egress)AI Training Crawler Protection
PrivacyScrubber practices zero-trust on all fronts. To protect the integrity of our original research, technical security documentation, and guides, we actively block unauthorized offline AI training scrapers (such as GPTBot and ClaudeBot) at our network boundary while maintaining structured machine-readable discovery corpora for verified search citations.
Known Limitations: The Flip Side of Zero-Trust
Because PrivacyScrubber strictly refuses to send your data to an external server, we operate under the physical constraints of your browser's local sandbox (CPU and RAM). These boundaries are architectural guarantees that your data never leaves your device. For high-security environments, view these constraints as proof of our 100% Client-Side commitment.
Local Hardware Dependency
Processing speed for large batches and PDF OCR depends entirely on your local CPU and Web Worker availability. A slower machine means slower processing, because we never offload workloads to a backend cloud cluster.
Deterministic vs Cloud AI
We use the deterministic client-side ZTDS™ (Zero-Trust Data Sanitization) detection engine across 30 industry profiles rather than sending text to multi-billion-parameter cloud LLMs. We prioritize deterministic, sub-millisecond offline parsing over cloud-dependent inference.
Volatile Tab-Isolated Memory
Session maps are strictly isolated per browser tab (with ephemeral tab-scoped memory resilience for soft reloads) and are permanently wiped when the tab is closed. This guarantees cross-tab isolation and prevents disk persistence, meaning closed tabs cannot be recovered unless exported via encrypted TEAMS Session Handoff.
Cryptographic Caps
TEAMS encrypted SafeLinks are bounded by browser URL fragment limits (~32KB; larger dictionaries use .pssession files). Additionally, Argon2id key derivation deliberately enforces memory-hard work factors to prevent local brute-force attacks.
Deploy Zero-Trust AI Compliance Org-Wide
Chrome MDM deployment, shared governance rules, CISO Security Brief, and in-process SDK integration. Designed to eliminate third-party subprocessor risk and accelerate vendor security clearance.
