What is LLM PII obfuscation?

LLM PII obfuscation is the process of automatically detecting and transforming personally identifiable information (PII) — names, emails, phone numbers, SSNs, account numbers, medical details, and more — before that data is sent to an AI language model. Unlike simple redaction, obfuscation replaces PII with contextually realistic placeholders so the model can still process the query meaningfully.

How is obfuscation different from redaction?

Redaction removes sensitive data and replaces it with a blank or a label like [REDACTED]. This often destroys the context the AI needs to give a useful response. Obfuscation replaces PII with realistic, context-preserving substitutes — for example, replacing a real patient name with a different plausible name — so the model's response remains useful and can be mapped back to the original data after the fact.

Which LLMs does PrivacyLayer work with?

PrivacyLayer is completely LLM-agnostic. It intercepts data at the transport layer — before it reaches any model — so it works with OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, Cohere, or any locally hosted model. No integration work required on the AI provider side.

What is a kernel module PII filter?

A kernel module PII filter operates at the operating system level, intercepting network traffic before it leaves the machine. This provides system-wide coverage regardless of which application or browser is making the AI API call. It's the highest level of protection — no application can accidentally bypass it.

Is PrivacyLayer GDPR and HIPAA compliant?

PrivacyLayer is designed to help organizations meet their obligations under GDPR, HIPAA, and SOC 2 Type II by ensuring that personal data and protected health information is obfuscated before reaching any third-party AI model. It is a technical control, not a compliance certification — organizations should review their specific obligations with legal counsel.

When will PrivacyLayer be available?

PrivacyLayer is currently in active development. Join the early access waitlist at hello@daedalusdynamics.com to be among the first to access the product and shape the roadmap.

In Development · Early Access Open

Stop PII from ever reaching your LLM

PrivacyLayer sits between your applications and the network. PII is intercepted before any packet leaves your machine. The AI still gets a useful query. You never get a breach.

Join Early Access How it works

without privacylayer

John Smith · SSN 234-56-7890 · BCBS policy #99A2

↓ sent directly to LLM

⚠ PII in model context · GDPR risk · data retained by provider

with privacylayer

John Smith · SSN 234-56-7890 · BCBS policy #99A2

↓ intercepted at kernel / extension

Michael Torres · SSN 891-23-4567 · BCBS policy #44C7

↓ obfuscated query sent to LLM

✓ model responds · PII never leaves your machine

↓ response reconstructed

✓ clean answer, real names restored for your app

// the problem

Most AI deployments are one prompt away from a breach

Every time someone pastes a customer record into ChatGPT, that data hits a third-party server. You can't policy your way out of it — you need a technical control.

⚠️ Without PrivacyLayer

PII lands in LLM provider training data or logs
GDPR breach risk on every customer-data prompt
HIPAA exposure for any healthcare workflow
No visibility into what sensitive data was shared
Data retention policies of AI providers are out of your control
AUP violations if employees use consumer AI tools for work

🛡 With PrivacyLayer

PII is intercepted before any network call leaves your machine
Obfuscated data is semantically equivalent — responses are still useful
Works with every LLM, every app, every browser tab
Full audit log of what was obfuscated and when
Context is reconstructed cleanly after the model responds
Deploy system-wide (kernel) or per-tab (extension)

// how it works

Obfuscation, not redaction.
Context preserved.

Redaction destroys the context the AI needs. PrivacyLayer replaces PII with realistic, coherent substitutes — so the model gets a query it can actually answer.

Intercept at the transport layer

PrivacyLayer sits between your apps and the network — kernel module for system-wide coverage, browser extension for per-tab. Every LLM API call is intercepted before the packet leaves your machine.

Detect and classify PII

A local, on-device detection engine identifies PII entities: names, emails, phone numbers, SSNs, account numbers, addresses, medical identifiers, and custom entity types you define.

Obfuscate with context-aware substitutes

Each PII entity is replaced with a realistic, contextually consistent substitute. Names stay culturally plausible, numbers stay structurally valid. The model sees a coherent query.

Reconstruct the response

When the model responds, PrivacyLayer maps obfuscated entities back to their originals. Your app receives a clean, accurate answer — with real data where relevant.

// deployment

Two modes. One decision.

Choose based on how you use AI — both modes provide full protection. You can run both simultaneously.

🖥

Kernel Module

System-wide · OS-level protection

Runs at the OS level, intercepting all outbound LLM API traffic regardless of which app makes the call. No app can bypass it. Ideal for orgs where employees use a mix of AI tools — web apps, desktop clients, CLIs, IDE extensions.

Covers every app and browser on the machine
No per-app configuration required
Works even if users switch to new AI tools
Centrally managed via policy file

🧩

Browser Extension

Per-tab · Web app contexts

Intercepts LLM API calls made by web apps in real time. Ideal for teams using ChatGPT, Claude.ai, Perplexity, or your own internal AI apps — per-tab or per-site control without OS-level access.

Chrome and Firefox support
Per-site allow/block configuration
No OS permissions required
Works in BYOD and contractor environments

// compliance alignment

Built for regulated environments

PrivacyLayer is a technical control, not a policy. It enforces data protection at the transport layer — before sensitive data can leave your machine.

GDPR

Data minimization & purpose limitation

HIPAA

PHI de-identification

SOC 2

Access control & logging

CCPA

Consumer data rights

ISO 27001

Information security

PrivacyLayer is a technical control, not a legal certification. Review your specific obligations with qualified legal counsel.

// faq

Common questions

PrivacyLayer detects a broad set of PII entity types out of the box: full names, email addresses, phone numbers, social security numbers, passport numbers, driver's license numbers, dates of birth, physical addresses, credit card numbers, bank account numbers, IP addresses, medical record identifiers, and more. You can also define custom entity types for domain-specific identifiers.

Entirely locally. The PII detection and obfuscation engine runs on-device — the exact data you're trying to protect never leaves your machine in its original form. Nothing passes through Daedalus Dynamics servers. This is not a proxy service.

Response quality is preserved. Because PrivacyLayer uses contextually realistic obfuscation rather than blunt redaction, the model receives a coherent, meaningful query. If you ask about "John Smith's account balance," the model sees "Michael Torres's account balance" — structurally identical, semantically equivalent, and completely de-identified.

Yes, though if you're running a fully self-hosted LLM with no external network calls, you have inherently stronger data isolation. PrivacyLayer adds an additional layer of protection and audit logging even in self-hosted scenarios — useful for internal compliance and zero-trust architectures.

PrivacyLayer is designed for minimal latency impact. The detection and obfuscation pipeline runs in microseconds for typical prompts. Because LLM API calls have latency measured in hundreds of milliseconds to seconds, the overhead added by PrivacyLayer is imperceptible to end users.

PrivacyLayer is in active development. We are currently accepting early access applications for select organizations who want to help shape the product. Email hello@daedalusdynamics.com with a brief description of your use case and we'll be in touch.