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.

< 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.

PII MCP Server Protect Team ($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. Deterministic AST & Regex 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

Mathematical Guarantee: 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 AI
Execution ArchitectureClient-Side RAM (V8)Cloud REST APICloud REST APIContainer / PythonSaaS Cloud Proxy
Prompt Latency (250 B)< 2 ms~620 ms~580 ms~850 ms~950 ms
Document Latency (45 KB)4.2 ms1,840 ms1,650 ms2,400 ms2,900 ms
LLM Stream Disruption (TTFT)0% (Undetectable)Lag (+650ms)Lag (+600ms)Lag (+900ms)Lag (+1000ms)
Offline & Airplane Mode100% FunctionalFailsFailsFailsFails
Vendor Risk & DPANone (Zero Data Sent)Cloud DPACloud DPASelf-ManagedSaaS DPA
Pricing ModelFlat Rate ($99/mo team)Per-Char MeteredPer-GB MeteredCluster Compute$10k+ Tier

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_Regex_Match < 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.

Regulatory Compliance Without Third-Party 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.

  • HIPAA Safe Harbor (§164.514(b)): Masks all 18 PHI identifiers in client memory before transmitting prompts to medical LLMs.
  • GDPR Article 25 (Privacy by Design): Anonymizes personal data at the keyboard level, eliminating cross-border data transfer liabilities.
  • SOC 2 Type II (CC6.1 & CC6.6): Delivers verifiable cryptographic audit receipts proving records were redacted prior to AI ingestion.
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, ensuring full compliance without lag.
How does the PrivacyScrubber DLP speed test calculate benchmark times?
The benchmark runs our production regex and deterministic AST parsing 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 22+ 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 regex rules and industry profiles?
Yes. In addition to our 22+ built-in industry profiles, enterprise administrators can inject custom regular expressions, internal project codes, employee badge formats, and client account numbering schemes with instant local compilation.