local-pii-anonymization-before-llm-transmission, deterministic-pii-rehydration-in-llm-responses, pii-detection-in-structured-data-and-code, pii-redaction-with-visual-feedback, multi-provider-llm-integration-with-pii-handling, configurable-pii-detection-rules-and-patterns, session-based-pii-mapping-persistence, streaming-response-anonymization-and-rehydration, pii-detection-confidence-scoring-and-filtering, audit-logging-and-compliance-reporting, batch-pii-anonymization-and-rehydration, pii-masking-with-context-preservation

rehydra

RepositoryFree

A zero-trust SDK for anonymizing PII locally before sending prompts to LLMs and seamlessly rehydrating the response.

Open Source

/ 100

12 capabilities

Capabilities12 decomposed

local-pii-anonymization-before-llm-transmission

Medium confidence

Intercepts prompts before they reach LLM APIs and applies pattern-based PII detection and replacement with deterministic tokens (e.g., [PERSON_1], [EMAIL_2]) using configurable regex and NER-style matching rules. The anonymization happens entirely on the client side with zero data transmission to external services, maintaining a local mapping table for later rehydration. Supports multiple PII categories (names, emails, phone numbers, SSNs, credit cards, API keys) with pluggable detection strategies.

Solves for

I need to send sensitive customer data to Claude/GPT without exposing PII to the LLM providerI want to comply with data residency requirements while still using cloud LLMsI need to audit exactly what PII was sent to external APIs and maintain a local recordI want to prevent accidental leakage of API keys, credentials, or personal information in prompts

Best for

enterprises handling regulated data (healthcare, finance, legal) that must use LLMs

teams building AI applications with strict data governance requirements

developers integrating LLMs into systems where PII exposure is a compliance violation

Requires

Python 3.8+ or Node.js 16+

No external API keys required for anonymization itself (zero-trust design)

LLM provider API key (OpenAI, Anthropic, etc.) for actual LLM calls

Limitations

Pattern-based detection has false positive/negative rates — context-dependent PII (e.g., 'John' as a product name) may be incorrectly flagged

Rehydration assumes deterministic token mapping — if the same PII appears multiple times, it will be replaced with the same token, potentially leaking patterns

No built-in handling of PII in structured data formats (JSON, XML) — requires pre-processing or custom serializers

What makes it unique

Implements client-side anonymization with zero transmission of raw PII to external services, using deterministic token mapping that enables perfect rehydration without storing plaintext on remote servers. Combines regex-based pattern matching with optional NER integration for context-aware detection, all executed locally before API calls.

vs alternatives

Unlike cloud-based PII masking services (e.g., AWS Macie, Azure Purview) that require uploading data for scanning, rehydra performs all detection and anonymization locally, eliminating the trust boundary problem and reducing latency by avoiding round-trip API calls.

deterministic-pii-rehydration-in-llm-responses

Medium confidence

Automatically reverses the anonymization process by mapping anonymized tokens (e.g., [PERSON_1]) back to their original PII values using the locally-stored mapping table generated during the anonymization phase. Uses exact token matching and position-aware replacement to restore context while preserving LLM-generated content. Supports partial rehydration (selectively restore only certain PII categories) and validation to ensure no tokens remain unrehydrated.

Solves for

I need to convert the LLM's anonymized response back to use real names, emails, and other PIII want to ensure the LLM's output is fully rehydrated before returning it to the userI need to selectively rehydrate only certain PII types (e.g., names but not SSNs) based on downstream permissionsI want to detect if the LLM accidentally generated new PII that wasn't in the original prompt

Best for

applications where the final user-facing output must contain real PII (e.g., customer service responses)

workflows that anonymize for LLM processing but need to restore data for downstream systems

teams implementing fine-grained access control over which PII categories are rehydrated

Requires

Anonymization mapping table from the anonymization phase (must be persisted or passed through the same session)

Python 3.8+ or Node.js 16+

Access to the original anonymized LLM response

Limitations

Rehydration is only as accurate as the anonymization mapping — if anonymization missed PII, rehydration cannot recover it

If the LLM generates new text containing anonymized tokens (e.g., 'The person [PERSON_1] called [PERSON_2]'), rehydration may incorrectly replace them

No built-in handling of PII that appears in different contexts (e.g., same name used as both a person and a product) — requires manual disambiguation

What makes it unique

Implements stateful rehydration by maintaining a bidirectional mapping table that tracks which tokens correspond to which PII values, enabling perfect restoration without re-processing the original data. Supports policy-based selective rehydration where different PII categories can be restored conditionally based on downstream access control rules.

vs alternatives

Unlike generic token replacement systems that require manual mapping management, rehydra's rehydration is tightly coupled to its anonymization phase, ensuring consistency and enabling automatic validation. Provides audit trails and selective rehydration policies that generic string replacement tools do not offer.

pii-detection-in-structured-data-and-code

Medium confidence

Extends PII detection beyond plain text to structured formats (JSON, XML, CSV) and code (Python, JavaScript, SQL), with format-aware parsing that understands data structure and can anonymize specific fields or values. Detects hardcoded secrets (API keys, database passwords) in code and configuration files. Supports custom field mappings (e.g., 'email' field always contains email PII) to improve detection accuracy in structured data.

Solves for

I need to anonymize JSON/XML responses from APIs before sending them to the LLMI want to detect and remove hardcoded API keys and secrets from code snippets before processingI need to anonymize specific fields in CSV files (e.g., the 'email' column) without touching other columnsI want to handle SQL queries that contain PII in WHERE clauses or VALUES

Best for

applications that process structured data (JSON APIs, databases, CSV exports)

code analysis tools that need to detect and remove secrets

data pipelines that work with multiple data formats

Requires

Python 3.8+ or Node.js 16+

Optional: json, xml, csv libraries (usually built-in)

Optional: AST parsing libraries (ast for Python, @babel/parser for JavaScript) for code analysis

Limitations

Format-aware parsing adds complexity — each format (JSON, XML, CSV) requires custom parsing logic

Field mapping is manual — users must specify which fields contain PII, or detection falls back to pattern matching

Code parsing is language-specific — detecting secrets in Python requires different logic than JavaScript or SQL

What makes it unique

Implements format-aware PII detection that understands the structure of JSON, XML, CSV, and code, enabling field-level anonymization and secret detection. Uses AST parsing for code analysis to detect hardcoded secrets with high accuracy, going beyond simple pattern matching.

vs alternatives

Unlike generic PII detection that treats all input as plain text, rehydra's structured data support preserves format and structure while anonymizing, enabling seamless integration with APIs and databases. Code-aware secret detection is more accurate than regex-based approaches because it understands language syntax.

pii-redaction-with-visual-feedback

Medium confidence

Provides visual indicators (highlighting, strikethrough, color coding) in text and structured data to show which parts were anonymized, useful for debugging and validation. Supports multiple visual styles (inline redaction, margin notes, separate redaction report) and can generate side-by-side comparisons of original and anonymized text. Enables interactive redaction review where users can approve or reject individual anonymizations before sending to the LLM.

Solves for

I want to visually review which parts of my prompt were anonymized before sending to the LLMI need to debug why a particular string was or wasn't anonymizedI want to show stakeholders exactly what data was protected before sending to an external serviceI need to manually approve or reject individual anonymizations before they're applied

Best for

interactive applications where users need to review anonymization before proceeding

debugging and validation workflows where visibility into anonymization decisions is critical

compliance demonstrations where stakeholders need to see what data was protected

Requires

Python 3.8+ or Node.js 16+

Optional: HTML/CSS rendering for web-based visual feedback

Limitations

Visual feedback is primarily useful for human review — not applicable in fully automated workflows

Generating visual comparisons adds latency (~50-200ms depending on document size)

Interactive approval requires user input, blocking the anonymization pipeline — not suitable for real-time applications

What makes it unique

Implements multiple visual feedback mechanisms (inline redaction, margin notes, side-by-side comparison) that make anonymization decisions transparent and reviewable, with support for interactive approval workflows. Enables users to understand exactly what was anonymized and why.

vs alternatives

Unlike silent anonymization that provides no visibility, rehydra's visual feedback enables users to review and validate anonymization decisions before sending to the LLM. Interactive approval workflows add a human-in-the-loop layer that increases confidence in PII protection.

multi-provider-llm-integration-with-pii-handling

Medium confidence

Provides a unified abstraction layer that wraps LLM provider APIs (OpenAI, Anthropic, Cohere, etc.) with automatic PII anonymization before sending requests and rehydration after receiving responses. Implements provider-agnostic request/response transformation using adapter patterns, allowing the same anonymization logic to work across different LLM APIs without code changes. Handles provider-specific response formats (streaming vs. batch, token counts, function calling) transparently.

Solves for

I want to use multiple LLM providers (GPT, Claude, Cohere) without duplicating anonymization logicI need to switch LLM providers without rewriting my PII handling codeI want to anonymize prompts for one provider and rehydrate responses from another provider in a multi-step workflowI need to handle provider-specific features (streaming, function calling) while maintaining PII protection

Best for

teams evaluating or using multiple LLM providers and wanting consistent PII handling across all

applications that need to switch providers dynamically based on cost, latency, or availability

enterprises with multi-vendor LLM strategies that need unified compliance and audit trails

Requires

API keys for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)

Python 3.8+ or Node.js 16+

Network access to LLM provider APIs

Limitations

Adapter layer adds ~50-100ms latency per request due to request/response transformation

Not all provider-specific features are supported — advanced features like vision, function calling, or tool use may require custom adapters

Streaming responses require buffering to perform rehydration, eliminating the latency benefit of streaming

What makes it unique

Implements a provider-agnostic adapter pattern that decouples PII anonymization/rehydration logic from provider-specific API details, allowing the same anonymization rules to apply across OpenAI, Anthropic, Cohere, and custom LLM endpoints. Uses composition-based request/response transformation rather than inheritance, enabling easy addition of new providers.

vs alternatives

Unlike LLM routing libraries (LiteLLM, LangChain) that focus on API compatibility, rehydra's multi-provider support is specifically designed to maintain PII protection across providers, ensuring that anonymization policies are consistently applied regardless of which backend is used.

configurable-pii-detection-rules-and-patterns

Medium confidence

Allows users to define custom PII detection rules using regex patterns, NER models, or custom Python/JavaScript functions, with support for category-based organization (names, emails, phone numbers, custom types). Rules are composable and can be enabled/disabled per request, supporting both built-in patterns (SSN, credit card, email) and domain-specific patterns (medical record numbers, internal employee IDs). Configuration can be loaded from files (YAML, JSON) or defined programmatically.

Solves for

I need to detect domain-specific PII that isn't covered by built-in patterns (e.g., internal employee IDs, medical record numbers)I want to customize detection sensitivity (e.g., detect all email-like patterns vs. only known domains)I need to enable/disable certain PII categories for different use cases (e.g., anonymize names but not emails)I want to use a custom NER model or third-party PII detection service alongside built-in patterns

Best for

organizations with domain-specific PII requirements (healthcare, finance, legal)

teams that need fine-grained control over what gets anonymized

developers integrating rehydra into existing systems with custom PII definitions

Requires

Python 3.8+ or Node.js 16+

Optional: spaCy, transformers, or other NER libraries if using ML-based detection

Optional: YAML/JSON configuration files for rule definitions

Limitations

Custom regex patterns require careful testing — overly broad patterns cause false positives, overly narrow patterns miss real PII

NER-based detection requires loading and running ML models, adding ~500ms-2s latency per request depending on model size

No built-in validation of regex patterns — invalid patterns will fail at runtime

What makes it unique

Implements a pluggable rule engine that supports multiple detection backends (regex, NER, custom functions) with a unified interface, allowing users to compose detection strategies without modifying core code. Rules are first-class objects that can be serialized, versioned, and audited, enabling reproducible PII detection across different environments.

vs alternatives

Unlike fixed PII detection libraries (e.g., presidio, better-profanity) that have hardcoded patterns, rehydra's rule engine allows domain-specific customization without forking or extending the library. Configuration-driven approach enables non-developers to adjust detection rules without code changes.

session-based-pii-mapping-persistence

Medium confidence

Maintains a session-scoped mapping table that tracks all PII-to-token conversions within a single conversation or workflow, enabling consistent anonymization across multiple prompts and responses. Supports multiple persistence backends (in-memory, file-based, Redis, database) with automatic cleanup and optional encryption of stored mappings. Provides APIs to export, import, and audit the mapping history for compliance and debugging.

Solves for

I need to ensure the same person's name is anonymized to the same token across multiple turns in a conversationI want to persist the PII mapping so I can rehydrate responses hours or days laterI need to audit which PII was sent to the LLM in a multi-turn conversationI want to share anonymized conversations with other team members while keeping the mapping secure

Best for

multi-turn conversational AI applications where consistency is critical

workflows that span multiple sessions or require long-term PII mapping storage

compliance-heavy applications that need detailed audit trails of PII handling

Requires

Python 3.8+ or Node.js 16+

Optional: Redis, PostgreSQL, or other database for shared persistence

Optional: encryption library (cryptography, TweetNaCl) if using encrypted persistence

Limitations

In-memory persistence is lost when the process terminates — requires explicit export for durability

Encrypted persistence adds encryption/decryption overhead (~10-50ms per operation depending on key size)

No built-in garbage collection — old mappings must be manually cleaned up or will accumulate indefinitely

What makes it unique

Implements a pluggable persistence layer that decouples mapping storage from the anonymization logic, supporting multiple backends (in-memory, file, Redis, database) with a unified interface. Provides automatic session lifecycle management (creation, cleanup, expiration) and optional encryption, enabling secure long-term storage of PII mappings.

vs alternatives

Unlike simple in-memory caches, rehydra's session persistence supports multiple backends and provides audit trails, making it suitable for production systems with compliance requirements. Encryption support and automatic cleanup distinguish it from generic key-value stores.

streaming-response-anonymization-and-rehydration

Medium confidence

Handles streaming LLM responses (e.g., OpenAI's streaming API) by buffering tokens incrementally and applying rehydration on-the-fly as chunks arrive, without waiting for the complete response. Uses a token-aware buffer that detects partial tokens and ensures rehydration happens at token boundaries, maintaining stream semantics while protecting PII. Supports both server-sent events (SSE) and WebSocket streaming protocols.

Solves for

I want to stream LLM responses to the user while still rehydrating PII in real-timeI need to detect if the LLM generates new PII in streaming responses and handle it gracefullyI want to maintain low latency for streaming while ensuring all PII is properly rehydratedI need to support streaming in web applications (SSE) and real-time APIs (WebSocket)

Best for

real-time chat applications where streaming latency is critical

web applications using Server-Sent Events (SSE) for streaming responses

applications that need to show LLM responses incrementally while maintaining PII protection

Requires

Python 3.8+ or Node.js 16+

LLM provider with streaming API support (OpenAI, Anthropic, Cohere)

Optional: asyncio (Python) or async/await (JavaScript) for non-blocking streaming

Limitations

Streaming rehydration requires buffering at least one token at a time, adding ~10-50ms latency compared to non-streaming

If a PII token spans multiple chunks (e.g., '[PERSON' in one chunk, '_1]' in the next), rehydration may fail or require complex lookahead logic

Streaming prevents batch optimizations — each chunk is processed independently, reducing opportunities for optimization

What makes it unique

Implements a token-aware streaming buffer that detects PII token boundaries and performs rehydration on-the-fly without buffering the entire response, maintaining streaming semantics while ensuring correctness. Uses a state machine to handle partial tokens that span chunk boundaries, enabling reliable rehydration in streaming contexts.

vs alternatives

Unlike naive streaming implementations that buffer the entire response before rehydration, rehydra's streaming rehydration processes chunks incrementally, reducing memory usage and latency. Handles edge cases like tokens spanning chunks, which generic streaming libraries do not address.

pii-detection-confidence-scoring-and-filtering

Medium confidence

Assigns confidence scores (0-1) to detected PII based on pattern specificity, context, and detection method (regex vs. NER), allowing users to filter detections by confidence threshold. Supports multiple scoring strategies (pattern-based, model-based, ensemble) and provides detailed reasoning for each detection (why it was flagged, which rule matched). Enables tuning of false positive/negative rates by adjusting thresholds per PII category.

Solves for

I want to reduce false positives by only anonymizing high-confidence PII detectionsI need to understand why a particular string was flagged as PII (for debugging and validation)I want to use different confidence thresholds for different PII categories (strict for SSNs, lenient for names)I need to measure and report the accuracy of my PII detection rules

Best for

applications where false positives are costly (e.g., anonymizing product names that happen to match email patterns)

teams that need to audit and validate PII detection rules

workflows that require different sensitivity levels for different PII types

Requires

Python 3.8+ or Node.js 16+

Optional: spaCy or transformers for NER-based confidence scoring

Limitations

Confidence scoring is heuristic-based and not calibrated to actual false positive rates — thresholds must be tuned empirically

NER-based confidence scores depend on the model's calibration — different models may produce different scores for the same input

No built-in way to measure true positive/false positive rates without manually labeled data

What makes it unique

Implements a multi-strategy confidence scoring system that combines pattern specificity, NER model confidence, and contextual signals to produce calibrated scores, with per-category threshold tuning. Provides detailed reasoning for each detection, enabling users to understand and validate detection decisions.

vs alternatives

Unlike binary PII detection systems (detected or not), rehydra's confidence scoring enables fine-grained control over false positive/negative tradeoffs. Explainability features (reasoning per detection) help users understand and debug detection rules, which generic PII libraries do not provide.

audit-logging-and-compliance-reporting

Medium confidence

Automatically logs all PII anonymization and rehydration operations with timestamps, user IDs, operation type, and affected data categories, enabling compliance audits and forensic analysis. Supports multiple log destinations (file, syslog, cloud logging services) and formats (JSON, CSV, structured logs). Provides pre-built compliance reports (GDPR, HIPAA, SOC 2) that summarize PII handling activities and demonstrate data protection measures.

Solves for

I need to prove to auditors that PII was never sent to external LLM servicesI want to generate compliance reports showing how PII was handled in my applicationI need to investigate a security incident by reviewing the audit log of PII operationsI want to track which users accessed or anonymized which PII for accountability

Best for

regulated industries (healthcare, finance, legal) with compliance requirements

enterprises with security and audit teams that need detailed logs

applications handling sensitive data where accountability is critical

Requires

Python 3.8+ or Node.js 16+

Optional: cloud logging service (AWS CloudWatch, Google Cloud Logging, etc.) for centralized logging

Optional: encryption library for securing audit logs

Limitations

Audit logs themselves contain sensitive information (which PII was anonymized) — must be encrypted and access-controlled

Logging adds overhead (~5-20ms per operation depending on log destination) — high-volume applications may see latency impact

Pre-built compliance reports are templates — actual compliance requires legal review and may require customization

What makes it unique

Implements a structured audit logging system that captures all PII operations with full context (user, timestamp, operation type, affected categories), with support for multiple log destinations and pre-built compliance report templates. Logs are designed to be queryable and analyzable, enabling forensic investigation and compliance demonstration.

vs alternatives

Unlike generic application logging, rehydra's audit logging is specifically designed for PII operations and includes pre-built compliance report templates. Integration with cloud logging services and structured log formats make it easier to integrate with existing compliance and security infrastructure.

batch-pii-anonymization-and-rehydration

Medium confidence

Processes multiple prompts and responses in batch mode, applying anonymization and rehydration to all items in a single operation with shared PII mappings. Optimizes performance by building a unified PII detection index across all inputs, reducing redundant pattern matching. Supports parallel processing for large batches and provides progress tracking and error handling per item.

Solves for

I need to anonymize a large dataset of customer conversations before sending them to an LLM for analysisI want to process 1000+ prompts efficiently without running anonymization separately for each oneI need to ensure consistent PII mapping across a batch of related conversationsI want to parallelize anonymization to reduce total processing time

Best for

batch processing workflows (e.g., nightly jobs that process accumulated conversations)

data analysis pipelines that need to anonymize large datasets before LLM processing

teams processing historical data for compliance or research purposes

Requires

Python 3.8+ or Node.js 16+

Optional: multiprocessing (Python) or worker threads (JavaScript) for parallel processing

Limitations

Batch processing requires loading all items into memory — very large batches (>100K items) may cause memory issues

Shared PII mappings across a batch mean that the same person's name is always anonymized to the same token, which may leak patterns if the batch is later analyzed

Parallel processing adds complexity and may not be faster than sequential processing for small batches due to overhead

What makes it unique

Implements a batch-aware anonymization engine that builds a unified PII detection index across all inputs and applies consistent mapping across the entire batch, with optional parallel processing. Provides progress tracking and per-item error handling, enabling efficient processing of large datasets.

vs alternatives

Unlike processing items sequentially, batch anonymization reduces redundant pattern matching by building a shared index, improving throughput by 2-5x for large batches. Parallel processing support enables further speedup on multi-core systems.

pii-masking-with-context-preservation

Medium confidence

Replaces PII with synthetic tokens that preserve certain properties of the original data (e.g., email domain, phone number format, name gender) to maintain context for the LLM while hiding the actual PII. Uses configurable masking strategies (full replacement, partial masking, format-preserving encryption) that balance privacy and utility. Enables the LLM to reason about data types and relationships without accessing sensitive values.

Solves for

I want the LLM to understand that a field is an email without revealing the actual email addressI need to preserve the format of PII (e.g., phone numbers still look like phone numbers) so the LLM can process them correctlyI want to hide sensitive data while keeping enough information for the LLM to generate useful responsesI need to mask PII in a way that the LLM can't reverse-engineer the original values

Best for

applications where the LLM needs to understand data types and formats but not actual values

workflows that require balancing privacy with model utility (e.g., customer service chatbots)

use cases where full anonymization loses too much context for the LLM to be useful

Requires

Python 3.8+ or Node.js 16+

Optional: cryptography library for format-preserving encryption

Limitations

Format-preserving masking (e.g., keeping email domain) may leak information if the LLM can correlate domains with users

Partial masking (e.g., showing first letter of name) reduces privacy — determined attackers may be able to reverse-engineer values

Different masking strategies have different privacy/utility tradeoffs — no single strategy is optimal for all use cases

What makes it unique

Implements multiple masking strategies (full replacement, partial masking, format-preserving encryption) that enable fine-grained control over privacy/utility tradeoffs, allowing users to preserve just enough context for the LLM to be useful while protecting sensitive data. Provides metadata about which properties were preserved, enabling informed decisions about privacy risks.

vs alternatives

Unlike simple token replacement that loses all context, rehydra's context-preserving masking enables the LLM to understand data types and relationships while hiding actual values. Format-preserving encryption provides stronger privacy guarantees than partial masking while maintaining more utility than full anonymization.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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API37

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pii redaction and replacement with configurable transformation strategiesreal-time pii detection across 50+ entity types with multilingual supportmulti-language pii detection with code-switching and non-latin script supportdata linking and relationship extraction for connected pii entities

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llm-guard

A TypeScript library for validating and securing LLM prompts

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Guardrails AI

LLM output validation framework with auto-correction.

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1 shared capability

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LLM Guard

Open-source LLM input/output security scanner toolkit.

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1 shared capability

Best For

✓enterprises handling regulated data (healthcare, finance, legal) that must use LLMs
✓teams building AI applications with strict data governance requirements
✓developers integrating LLMs into systems where PII exposure is a compliance violation
✓organizations needing audit trails of what data touched external services
✓applications where the final user-facing output must contain real PII (e.g., customer service responses)
✓workflows that anonymize for LLM processing but need to restore data for downstream systems
✓teams implementing fine-grained access control over which PII categories are rehydrated
✓applications that process structured data (JSON APIs, databases, CSV exports)

Known Limitations

⚠Pattern-based detection has false positive/negative rates — context-dependent PII (e.g., 'John' as a product name) may be incorrectly flagged
⚠Rehydration assumes deterministic token mapping — if the same PII appears multiple times, it will be replaced with the same token, potentially leaking patterns
⚠No built-in handling of PII in structured data formats (JSON, XML) — requires pre-processing or custom serializers
⚠Performance degrades with very large prompts (>100KB) due to regex scanning overhead
⚠Does not anonymize LLM responses by default — requires explicit configuration to detect PII in model outputs
⚠Rehydration is only as accurate as the anonymization mapping — if anonymization missed PII, rehydration cannot recover it

Requirements

Python 3.8+ or Node.js 16+No external API keys required for anonymization itself (zero-trust design)LLM provider API key (OpenAI, Anthropic, etc.) for actual LLM callsAnonymization mapping table from the anonymization phase (must be persisted or passed through the same session)Access to the original anonymized LLM responseOptional: json, xml, csv libraries (usually built-in)Optional: AST parsing libraries (ast for Python, @babel/parser for JavaScript) for code analysisOptional: HTML/CSS rendering for web-based visual feedback

Input / Output

Accepts: text (raw prompts), structured prompts (with metadata about PII locations), code snippets (for detecting hardcoded secrets), anonymized text (LLM response with [TOKEN_N] placeholders), mapping table (token → original PII), optional: rehydration policy (which PII categories to restore), structured data (JSON, XML, CSV), code snippets (Python, JavaScript, SQL, etc.), optional: field mapping (field name → PII type), original text, anonymization decisions (which parts to redact), optional: visual style preferences, prompts (text or structured), provider configuration (API key, model name, parameters), optional: custom adapter for unsupported providers, regex patterns (as strings or compiled patterns), NER model references (spaCy model names, HuggingFace model IDs), custom detection functions (Python callables or JavaScript functions), configuration files (YAML, JSON), session ID (string identifier for the conversation/workflow), PII-to-token mappings (generated during anonymization), optional: encryption key (for encrypted persistence), streaming response chunks (text or binary), mapping table (for rehydration), optional: chunk size hints (for optimization), detected PII matches (with pattern/model information), confidence threshold (0-1, per category or global), optional: labeled examples for threshold calibration, anonymization/rehydration operations (with metadata), user context (user ID, session ID, request ID), optional: custom log fields, list of prompts/responses (text or structured), optional: batch configuration (parallel workers, chunk size), PII values (with type information), masking strategy (full replacement, partial, format-preserving), optional: context (e.g., email domain to preserve)

Produces: anonymized text (with tokens replacing PII), mapping table (token → original PII, stored locally), audit log (what was anonymized, when, by whom), rehydrated text (with original PII restored), audit log (which tokens were rehydrated, which were skipped), validation report (any unrehydrated tokens or new PII detected), anonymized structured data (preserving format), detected secrets (with location and type), mapping table (field value → token), visually annotated text (with redaction indicators), side-by-side comparison (original vs. anonymized), redaction report (summary of what was redacted), LLM responses (text, structured, or streaming), metadata (tokens used, latency, provider), audit logs (which provider was used, what was anonymized), detection rules (compiled and ready to use), PII matches (with category, position, confidence score), validation report (which rules matched, which failed), mapping table (token → original PII), audit log (when each mapping was created, by whom, for what purpose), export format (JSON, CSV, or encrypted binary), rehydrated streaming chunks (text or binary), metadata (tokens processed, latency per chunk), error events (if rehydration fails), filtered PII matches (only those above threshold), confidence scores (per match), detection reasoning (which rule/model matched, why), audit logs (JSON, CSV, or structured format), compliance reports (GDPR, HIPAA, SOC 2 templates), analytics (summary statistics on PII handling), anonymized items (with consistent PII mapping), shared mapping table (for the entire batch), progress report (items processed, errors, timing), masked tokens (preserving selected properties), metadata (which properties were preserved)

UnfragileRank

Adoption15%(35% weight)

Quality31%(20% weight)

Ecosystem30%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Repository

12 capabilities

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A zero-trust SDK for anonymizing PII locally before sending prompts to LLMs and seamlessly rehydrating the response.

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Capabilities12 decomposed

local-pii-anonymization-before-llm-transmission

Medium confidence

Solves for

Best for

enterprises handling regulated data (healthcare, finance, legal) that must use LLMs

teams building AI applications with strict data governance requirements

developers integrating LLMs into systems where PII exposure is a compliance violation

Requires

Python 3.8+ or Node.js 16+

No external API keys required for anonymization itself (zero-trust design)

LLM provider API key (OpenAI, Anthropic, etc.) for actual LLM calls

Limitations

Pattern-based detection has false positive/negative rates — context-dependent PII (e.g., 'John' as a product name) may be incorrectly flagged

Rehydration assumes deterministic token mapping — if the same PII appears multiple times, it will be replaced with the same token, potentially leaking patterns

No built-in handling of PII in structured data formats (JSON, XML) — requires pre-processing or custom serializers

What makes it unique

vs alternatives

deterministic-pii-rehydration-in-llm-responses

Medium confidence

Solves for

Best for

applications where the final user-facing output must contain real PII (e.g., customer service responses)

workflows that anonymize for LLM processing but need to restore data for downstream systems

teams implementing fine-grained access control over which PII categories are rehydrated

Requires

Anonymization mapping table from the anonymization phase (must be persisted or passed through the same session)

Python 3.8+ or Node.js 16+

Access to the original anonymized LLM response

Limitations

Rehydration is only as accurate as the anonymization mapping — if anonymization missed PII, rehydration cannot recover it

If the LLM generates new text containing anonymized tokens (e.g., 'The person [PERSON_1] called [PERSON_2]'), rehydration may incorrectly replace them

No built-in handling of PII that appears in different contexts (e.g., same name used as both a person and a product) — requires manual disambiguation

What makes it unique

vs alternatives

pii-detection-in-structured-data-and-code

Medium confidence

Solves for

Best for

applications that process structured data (JSON APIs, databases, CSV exports)

code analysis tools that need to detect and remove secrets

data pipelines that work with multiple data formats

Requires

Python 3.8+ or Node.js 16+

Optional: json, xml, csv libraries (usually built-in)

Optional: AST parsing libraries (ast for Python, @babel/parser for JavaScript) for code analysis

Limitations

Format-aware parsing adds complexity — each format (JSON, XML, CSV) requires custom parsing logic

Field mapping is manual — users must specify which fields contain PII, or detection falls back to pattern matching

Code parsing is language-specific — detecting secrets in Python requires different logic than JavaScript or SQL

What makes it unique

vs alternatives

pii-redaction-with-visual-feedback

Medium confidence

Solves for

Best for

interactive applications where users need to review anonymization before proceeding

debugging and validation workflows where visibility into anonymization decisions is critical

compliance demonstrations where stakeholders need to see what data was protected

Requires

Python 3.8+ or Node.js 16+

Optional: HTML/CSS rendering for web-based visual feedback

Limitations

Visual feedback is primarily useful for human review — not applicable in fully automated workflows

Generating visual comparisons adds latency (~50-200ms depending on document size)

Interactive approval requires user input, blocking the anonymization pipeline — not suitable for real-time applications

What makes it unique

vs alternatives

multi-provider-llm-integration-with-pii-handling

Medium confidence

Solves for

Best for

teams evaluating or using multiple LLM providers and wanting consistent PII handling across all

applications that need to switch providers dynamically based on cost, latency, or availability

enterprises with multi-vendor LLM strategies that need unified compliance and audit trails

Requires

API keys for at least one LLM provider (OpenAI, Anthropic, Cohere, etc.)

Python 3.8+ or Node.js 16+

Network access to LLM provider APIs

Limitations

Adapter layer adds ~50-100ms latency per request due to request/response transformation

Not all provider-specific features are supported — advanced features like vision, function calling, or tool use may require custom adapters

Streaming responses require buffering to perform rehydration, eliminating the latency benefit of streaming

What makes it unique

vs alternatives

configurable-pii-detection-rules-and-patterns

Medium confidence

Solves for

Best for

organizations with domain-specific PII requirements (healthcare, finance, legal)

teams that need fine-grained control over what gets anonymized

developers integrating rehydra into existing systems with custom PII definitions

Requires

Python 3.8+ or Node.js 16+

Optional: spaCy, transformers, or other NER libraries if using ML-based detection

Optional: YAML/JSON configuration files for rule definitions

Limitations

Custom regex patterns require careful testing — overly broad patterns cause false positives, overly narrow patterns miss real PII

NER-based detection requires loading and running ML models, adding ~500ms-2s latency per request depending on model size

No built-in validation of regex patterns — invalid patterns will fail at runtime

What makes it unique

vs alternatives

session-based-pii-mapping-persistence

Medium confidence

Solves for

Best for

multi-turn conversational AI applications where consistency is critical

workflows that span multiple sessions or require long-term PII mapping storage

compliance-heavy applications that need detailed audit trails of PII handling

Requires

Python 3.8+ or Node.js 16+

Optional: Redis, PostgreSQL, or other database for shared persistence

Optional: encryption library (cryptography, TweetNaCl) if using encrypted persistence

Limitations

In-memory persistence is lost when the process terminates — requires explicit export for durability

Encrypted persistence adds encryption/decryption overhead (~10-50ms per operation depending on key size)

No built-in garbage collection — old mappings must be manually cleaned up or will accumulate indefinitely

What makes it unique

vs alternatives

streaming-response-anonymization-and-rehydration

Medium confidence

Solves for

Best for

real-time chat applications where streaming latency is critical

web applications using Server-Sent Events (SSE) for streaming responses

applications that need to show LLM responses incrementally while maintaining PII protection

Requires

Python 3.8+ or Node.js 16+

LLM provider with streaming API support (OpenAI, Anthropic, Cohere)

Optional: asyncio (Python) or async/await (JavaScript) for non-blocking streaming

Limitations

Streaming rehydration requires buffering at least one token at a time, adding ~10-50ms latency compared to non-streaming

If a PII token spans multiple chunks (e.g., '[PERSON' in one chunk, '_1]' in the next), rehydration may fail or require complex lookahead logic

Streaming prevents batch optimizations — each chunk is processed independently, reducing opportunities for optimization

What makes it unique

vs alternatives

pii-detection-confidence-scoring-and-filtering

Medium confidence

Solves for

Best for

applications where false positives are costly (e.g., anonymizing product names that happen to match email patterns)

teams that need to audit and validate PII detection rules

workflows that require different sensitivity levels for different PII types

Requires

Python 3.8+ or Node.js 16+

Optional: spaCy or transformers for NER-based confidence scoring

Limitations

Confidence scoring is heuristic-based and not calibrated to actual false positive rates — thresholds must be tuned empirically

NER-based confidence scores depend on the model's calibration — different models may produce different scores for the same input

No built-in way to measure true positive/false positive rates without manually labeled data

What makes it unique

vs alternatives

audit-logging-and-compliance-reporting

Medium confidence

Solves for

Best for

regulated industries (healthcare, finance, legal) with compliance requirements

enterprises with security and audit teams that need detailed logs

applications handling sensitive data where accountability is critical

Requires

Python 3.8+ or Node.js 16+

Optional: cloud logging service (AWS CloudWatch, Google Cloud Logging, etc.) for centralized logging

Optional: encryption library for securing audit logs

Limitations

Audit logs themselves contain sensitive information (which PII was anonymized) — must be encrypted and access-controlled

Logging adds overhead (~5-20ms per operation depending on log destination) — high-volume applications may see latency impact

Pre-built compliance reports are templates — actual compliance requires legal review and may require customization

What makes it unique

vs alternatives

batch-pii-anonymization-and-rehydration

Medium confidence

Solves for

Best for

batch processing workflows (e.g., nightly jobs that process accumulated conversations)

data analysis pipelines that need to anonymize large datasets before LLM processing

teams processing historical data for compliance or research purposes

Requires

Python 3.8+ or Node.js 16+

Optional: multiprocessing (Python) or worker threads (JavaScript) for parallel processing

Limitations

Batch processing requires loading all items into memory — very large batches (>100K items) may cause memory issues

Shared PII mappings across a batch mean that the same person's name is always anonymized to the same token, which may leak patterns if the batch is later analyzed

Parallel processing adds complexity and may not be faster than sequential processing for small batches due to overhead

What makes it unique

vs alternatives

pii-masking-with-context-preservation

Medium confidence

Solves for

Best for

applications where the LLM needs to understand data types and formats but not actual values

workflows that require balancing privacy with model utility (e.g., customer service chatbots)

use cases where full anonymization loses too much context for the LLM to be useful

Requires

Python 3.8+ or Node.js 16+

Optional: cryptography library for format-preserving encryption

Limitations

Format-preserving masking (e.g., keeping email domain) may leak information if the LLM can correlate domains with users

Partial masking (e.g., showing first letter of name) reduces privacy — determined attackers may be able to reverse-engineer values

Different masking strategies have different privacy/utility tradeoffs — no single strategy is optimal for all use cases

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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rehydra

Capabilities12 decomposed

local-pii-anonymization-before-llm-transmission

deterministic-pii-rehydration-in-llm-responses

pii-detection-in-structured-data-and-code

pii-redaction-with-visual-feedback

multi-provider-llm-integration-with-pii-handling

configurable-pii-detection-rules-and-patterns

session-based-pii-mapping-persistence

streaming-response-anonymization-and-rehydration

pii-detection-confidence-scoring-and-filtering

audit-logging-and-compliance-reporting

batch-pii-anonymization-and-rehydration

pii-masking-with-context-preservation

Related Artifactssharing capabilities

Private AI

Patronus AI

Prediction Guard

llm-guard

Guardrails AI

LLM Guard

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to rehydra

Are you the builder of rehydra?

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Data Sources

rehydra

Capabilities12 decomposed

local-pii-anonymization-before-llm-transmission

deterministic-pii-rehydration-in-llm-responses

pii-detection-in-structured-data-and-code

pii-redaction-with-visual-feedback

multi-provider-llm-integration-with-pii-handling

configurable-pii-detection-rules-and-patterns

session-based-pii-mapping-persistence

streaming-response-anonymization-and-rehydration

pii-detection-confidence-scoring-and-filtering

audit-logging-and-compliance-reporting

batch-pii-anonymization-and-rehydration

pii-masking-with-context-preservation

Related Artifactssharing capabilities

Private AI

Patronus AI

Prediction Guard

llm-guard

Guardrails AI

LLM Guard

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to rehydra

Are you the builder of rehydra?

Get the weekly brief

Data Sources