Zero-Trust DLP Latency & Throughput Benchmark

Client-Side ZTDS vs Cloud DLP Speed Test

Test and benchmark real-time PII redaction latency directly in your browser. Compare zero-trust client-side DLP speeds (< 3ms) against legacy cloud APIs (1,500ms+). 100% local RAM execution, 0ms network overhead.

ZTDS™ Level-1 Certified
< 3 ms
Local RAM Latency
0 Hops
Network Transit (0ms)
> 120 MB/s
V8 Engine Speed
0 Bytes
External Data Egress
Local ZTDS
1.8 ms(0ms transit)
Cloud DLP API Proxy
645 ms(7 hops)
Performance Delta
358x Faster
Throughput: ~94 MB/s | Zero Egress

Raw Input

0 characters|0 KB

Sanitized Output

0 tokens redacted
Sanitized output and highlighted tokens will appear here instantly...
Tap tokens to inspect.

Need sub-5ms DLP in Cursor, Claude Desktop, or CI/CD?

PrivacyScrubber runs as a native Model Context Protocol (MCP) server or SDK with zero remote dependencies.

Developer SDK ($299/mo) Deploy Teams ($99/mo)
Architectural Latency Breakdown

The 7 Hops of Cloud DLP vs 0 Hops of ZTDS

Why is cloud-based DLP fundamentally incompatible with real-time AI and autonomous agents? Every external request incurs physical transit, queuing, and serialization bottlenecks.

Legacy Architecture

Traditional Cloud DLP APIs

1. Public DNS Query & Resolution~40 ms
2. TLS 1.3 Cryptographic Handshake~75 ms
3. Internet Backbone RTT (Client to Cloud)~180 ms
4. WAF & API Gateway Request Ingress~50 ms
5. Multi-Tenant ML Inference Queue~850 ms
6. JSON Serialization & Return Egress~160 ms
Total Latency Penalty:1,355 ms - 2,500 ms

Compliance Risk: Sensitive plaintext leaves your corporate firewall, exposing employee prompts to sub-processors and subpoena discovery.

Zero-Trust Winner
PrivacyScrubber Engine

Client-Side ZTDS Pipeline

1. Local V8 JavaScript Context Allocation0.1 ms
2. ZTDS™ In-Memory Execution1.2 ms
3. Ephemeral In-Memory Token Vaulting0.4 ms
4. Network Transmission (Hops = 0)0.0 ms
5. Third-Party Sub-Processor Ingress0.0 ms
6. Sanitized Prompt Ready for LLM0.1 ms
Total Latency:1.8 ms - 4.5 ms

Architectural Safeguard: Zero external requests, air-gapped memory isolation, and 1-click token reveal without sending raw data to servers.

Vendor Comparison Matrix

Enterprise DLP Benchmark Comparison

Empirical comparison between PrivacyScrubber's client-side ZTDS architecture and industry cloud DLP vendors.

MetricPrivacyScrubber (ZTDS)AWS ComprehendGoogle Cloud DLPMicrosoft PresidioNightfall AIHomomorphic (FHE)
Execution ArchitectureClient-Side RAM (V8)Cloud REST APICloud REST APIContainer / PythonSaaS Cloud ProxyLattice Cipher Circuit
Prompt Latency (250 B)< 2 ms~620 ms~580 ms~850 ms~950 ms10,000+ ms (10⁴×)
Document Latency (45 KB)4.2 ms1,840 ms1,650 ms2,400 ms2,900 ms120,000+ ms
Commercial Cloud LLM Support100% Native (All LLMs)Via API ProxyVia API ProxyVia API ProxyVia SaaS ProxyIncompatible
LLM Stream Disruption (TTFT)0% (Undetectable)Lag (+650ms)Lag (+600ms)Lag (+900ms)Lag (+1000ms)Blocks Streaming
Offline & Airplane Mode100% FunctionalFailsFailsFailsFailsRequires GPU Cluster
Vendor Risk & DPANone (Zero Data Sent)Cloud DPACloud DPASelf-ManagedSaaS DPACustom Server Stack
Pricing ModelFlat Rate ($99/mo team)Per-Char MeteredPer-GB MeteredCluster Compute$10k+ Tier$50k+ Enterprise

The Physics of Latency: Why Edge Sanitization Beats Cloud DLPs

In computer networking, total cloud request latency is bound by physical fiber transmission and multi-tenant queuing:

T_cloud = T_DNS + T_TLS_Handshake + (RTT_backbone × N_hops) + T_Ingress_Queue + T_ML_Inference + T_Egress_Payload

Even on fast connections, network round-trip time ($RTT$) to cloud data centers combined with TLS handshakes and Python NLP models causes response times to exceed 600ms to 2,000ms. In contrast, PrivacyScrubber executes deterministically in local V8 memory:

T_ZTDS = T_V8_Context_Execution + T_Local_Parse < 3.0 ms (where Network Hops = 0)

Time-To-First-Token (TTFT) & The Autonomous AI Agent Loop

Modern generative AI tools rely on real-time token streaming. Adding a 1-second cloud DLP filter creates a perceptible pause that ruins developer ergonomics and conversational flow.

In autonomous multi-agent frameworks (such as CrewAI, AutoGen, and Claude Code), agents perform continuous iterative tool calls. If an agent executes 12 recursive steps, a 1,200ms cloud DLP proxy introduces over 14.4 seconds of pure idle latency. With PrivacyScrubber's 2ms ZTDS engine, those same 12 steps consume less than 25ms total.

Statutory Risk Minimization & Technical Safe Harbor Without Vendor Risk

When enterprise security teams adopt cloud DLP gateways, they introduce a new high-value attack surface: the DLP provider itself. If an attacker breaches the cloud proxy, all intercepted employee prompts are compromised. Client-side ZTDS establishes a verifiable Technical Safe Harbor (Defense-in-Depth):

  • GDPR Articles 25 & 32 (Recital 26), CCPA/CPRA & SOC 2 Type II: Enforces 100% direct PII zero-egress in client RAM and contextual entropy reduction across 30 specialized industry profiles, shielding DPOs and officers from statutory administrative liability.
  • HIPAA Safe Harbor (45 CFR §164.514(b)): Masks all 18 PHI identifiers in client memory before transmitting prompts to medical LLMs.
  • SOC 2 Type II (CC6.1 & CC6.6): Delivers verifiable cryptographic audit receipts proving records were redacted prior to AI ingestion.

Why Homomorphic Encryption (FHE) & PGP Cannot Power Interactive Cloud LLMs

Enterprise cryptographers frequently evaluate Fully Homomorphic Encryption (FHE) and OpenPGP alongside client-side sanitization. While FHE permits mathematical operations on lattice-encrypted ciphertexts, it incurs a 10,000× to 1,000,000× compute penalty (10s–120s+ per query) and requires custom homomorphic evaluation circuits on the server. Commercial frontier LLMs (OpenAI GPT-4o/o3, Anthropic Claude 3.7, Google Gemini) execute standard floating-point Transformer weights and do not accept FHE ciphertexts.

Similarly, legacy PGP encrypts prompts into opaque ciphertext blocks that blind the LLM to natural language syntax. Client-Side ZTDS™ achieves zero raw PII egress in < 2ms on commodity client CPUs with zero cooperation required from the AI vendor, and pairs with Protocol V5 Dual-Plane Session Handoff so colleagues or external counsel can restore real names in local RAM in 0.4 seconds for free.

Knowledge Base

Frequently Asked Questions

Technical details on our DLP speed test, ZTDS architecture, and methodology.

Why is DLP latency so critical for Generative AI and streaming chatbots?
Modern Generative AI applications (ChatGPT, Claude, Cursor, enterprise RAG) stream tokens incrementally. A 1-second network delay for cloud DLP scrubbing blocks the initial stream buffer, causing severe UX degradation. Client-side Zero-Trust Data Sanitization executes in under 3 milliseconds, supporting statutory compliance safeguards without lag.
How does the PrivacyScrubber DLP speed test calculate benchmark times?
The benchmark runs our production ZTDS™ local sanitization engine directly in your browser using high-precision JavaScript performance.now() timers. The comparative cloud API latency is modeled based on empirical measurements of standard cloud DLP round-trips (DNS lookup + TLS 1.3 handshake + network transit + ML inference queuing).
Is client-side deterministic PII redaction as accurate as cloud AI models?
Yes, for structured, semi-structured, and domain-specific corporate data. While cloud NLP models frequently suffer from hallucinations and non-deterministic false positives, PrivacyScrubber utilizes hyper-optimized deterministic rulesets tailored across 30 specialized industry profiles, delivering 99.9% precision without heavy compute overhead.
Does this benchmark work in air-gapped environments and offline mode?
Yes. Once the page is loaded, you can disconnect your Wi-Fi or enable Airplane Mode. All parsing, token vaulting, and latency calculations happen 100% locally inside the browser's JavaScript engine without making any network requests.
How does PrivacyScrubber integrate with developer IDEs like Cursor and VS Code?
We provide a native Model Context Protocol (MCP) server (pii-mcp) that hooks directly into Cursor, Claude Desktop, and VS Code. When your AI assistant reads files or executes tool calls, pii-mcp scrubs secrets and personal data locally before transmitting tokens to the LLM backend.
Can enterprise teams define custom proprietary detection rules and industry profiles?
Yes. In addition to our 30 built-in industry profiles, enterprise administrators can inject custom detection patterns, internal project codes, employee badge formats, and client account numbering schemes with instant local compilation.
How does Client-Side ZTDS™ compare to Homomorphic Encryption (FHE) and PGP for LLM latency?
Fully Homomorphic Encryption (FHE) introduces a 10,000× to 1,000,000× computational overhead (10,000ms–120,000ms+ per query) and requires custom homomorphic evaluation circuits that commercial cloud LLMs (OpenAI, Anthropic, Google) do not support. Legacy PGP encrypts text into opaque ciphertext that blinds the AI to natural language syntax. Client-Side ZTDS™ executes in under 2 milliseconds in local browser or Node.js RAM with zero server cooperation required, preserving 100% syntax for native LLM reasoning while enabling 0.4-second free recipient restoration via Protocol V5 Session Handoff.
Production-Grade Performance

Eliminate Cloud DLP Latency in Your Enterprise Stacks

Choose between in-memory headless developer SDK for microservices and RAG pipelines, or zero-friction departmental Teams deployment. 100% RAM execution, 0 server hops, 0-byte egress.

Developer SDK $299/mo · $2,990/yr

Headless In-Memory Engine

Node.js & WASM library for ETL data pipelines, backend microservices, and RAG vector search. Sub-millisecond latency in device RAM.

  • Zero network transit (0ms cloud latency)
  • All 30 specialized industry profiles included
  • 1-line wrapper for OpenAI, Anthropic & LangChain
Explore Developer SDK →
Teams Deployment $99/mo Flat

Enterprise Team Protection

Unlimited seats, instant browser extension deployment for ChatGPT, Claude & Gemini, and encrypted session handoff.

  • Unlimited team seats with zero per-user fees
  • XChaCha20-Poly1305 encrypted session handoff
  • Zero-Trust CISO compliance receipts & audit trails
Deploy Teams ($99/mo) →
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