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Alternatives
Software like PII Eraser
What else does this job. Matched on what each project does, not on who links to whom.
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- PII Anomalyzerpiianomalyzer.aiPII Anomalyzer is a desktop tool for detecting, anonymizing, and redacting personally identifiable information in documents while keeping processing entirely offline. It is built for handling files on the user’s machine rather than in the cloud, and its interface and messaging focus on reducing data transmission during PII review. It imports PDF, Word documents, and spreadsheets, and supports PDF, DOCX, and XLSX files. The core workflow combines two independent AI models with context-aware detection to find names, Social Security numbers, credit cards, medical records, passport numbers, and other entities. The tool offers four de-identification methods: Redact, Replace, Highlight, and Mask. A manual draw-to-redact toolbar is also included for cases that need direct control over what is removed or obscured. The product states that detection continues to work even when the computer is disconnected from the internet. The software is available on Windows and macOS. It is described as using no uploads, no cloud, and no third-party AI, and it says that no telemetry or analytics are sent. The page also mentions a free 7-day trial and links to pricing and download pages. It says the tool supports compliance workflows for HIPAA, GDPR, the EU AI Act, CCPA, FERPA, and GLBA.
- PII Firewallpii-firewall.comPII Firewall is an open-source privacy firewall for LLM applications. It intercepts personal data before it reaches OpenAI, Anthropic, or other LLMs, then rehydrates it in the response. The tool is described as a transparent privacy layer between an application and any LLM, with zero changes to existing prompt logic. Its workflow is organized around detection, anonymization, and rehydration. The page shows that one or more backends, including regex, Presidio, GLiNER, and Transformers, detect entities, and domain rules decide what to keep. Entities can be handled by disposition as keep, pseudonymize, redact, generalize, mask, or hash. The examples on the page show healthcare usage that keeps diagnoses and other clinical context while stripping identifiers such as names, national IDs, email addresses, ages, and account data. The tool also advertises 55+ languages and domain-aware profiles, with built-in presets for healthcare, finance, legal, and generic or custom use. The documentation presents it as usable in a few lines of code. The quick start shows a Python import, create_firewall(), and a firewall.process() call, and the text states that the sanitized prompt can be forwarded to OpenAI, Anthropic, Mistral, or local models. The backend section describes several detection options: regex for structured IDs and fast pipelines, Presidio for general-purpose NER, GLiNER for zero-shot entity types, Transformers for domain-specific accuracy, and an OpenAI Privacy Filter. Each backend is shown with an install command using pip and a firewall call. PII Firewall is labeled Open Source and Apache 2.0. The page does not identify a company maker, but it does link to GitHub and documentation. It is a Python-based privacy layer for LLM apps rather than a general-purpose AI model or chat product.
- NoPIInopii.coNoPII is a reverse proxy for LLM APIs that detects and tokenizes sensitive data before it leaves an application’s environment, then detokenizes the response on the way back. It is described as a PII protection layer for prompts sent to external AI providers, with examples that include names, SSNs, email addresses, and addresses. Its request flow is presented in three steps. Incoming requests are scanned for PII and each value is replaced with a deterministic, format-preserving token before the request is forwarded. The sanitized payload is then sent to supported LLM providers using the caller’s own API key. When the model response returns, the proxy maps tokens back to the original values and returns the restored response to the application. The product also says it preserves entity relationships across messages, includes context phrase neutralization to prevent LLM safety refusals on tokenized data, keeps a full audit trail for every request, and blocks a request if the tokenization service is unreachable rather than sending PII through. The site describes one-line integration by changing the base_url to https://api.nopii.co, with no SDK, middleware rewrite, or architecture changes. It states that the service works with OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, Groq, Together, and Fireworks, and that it supports native SSE streaming for OpenAI and Anthropic. The examples and use cases shown are healthcare, financial services, legal, customer support, and HR and people operations. NoPII is offered as a managed service and is sign-up free. It is described as built by Enigma Vault, and the infrastructure is stated to be PCI Level 1 and SOC 2 Type II certified, with compliance references to HIPAA, GDPR, SOX, and PCI-DSS.
- Local PII Redactionpii-redaction.comPII Redaction Pro is a Windows desktop application that identifies and masks personally identifiable information in PDF, CSV, XLSX, TXT, and DOCX files. It processes data locally, supports English and German documents, and offers bulk redaction for privacy-conscious and regulated organizations.
- PII Blackoutpiiblackout.comPII Blackout is a desktop application for Windows and macOS that runs 100% offline to scan folders of PDFs, detect over 70 types of sensitive data using models like Microsoft Presidio and GLiNER, and permanently black them out. It produces audit reports and is designed for attorneys and businesses handling confidential client files. A free trial is available and an online demo allows testing before downloading the full 1.2GB application.
- PEyeEye AIpeyeeye.aiPEyeEye AI is a platform designed to protect sensitive customer data when integrating large language models (LLMs) into applications. It addresses the challenge of preventing personally identifiable information (PII) from being exposed to third-party AI models by redacting PII from prompts before they are sent to an LLM and rehydrating the redacted data in responses, all within a single round-trip. The system uses deterministic tokens to replace sensitive information, ensuring the mapping remains consistent within a session while remaining random across sessions. Zero data retention is enforced by default, with stateless AEAD-sealed sessions and AES-256-GCM encryption at rest, so no sensitive data is stored during processing. The platform detects 62 types of PII entities out of the box, including names, emails, phone numbers, credit cards, SSNs, addresses, bank accounts, medical record numbers, IP addresses, license plates, passport numbers, geo coordinates, company names, order IDs, currency amounts, API keys, and tax IDs. Users can also define custom detectors by providing positive and negative examples, allowing the tool to recognize domain-specific PII without the need for custom model training or data science intervention. Detection is performed using a hybrid approach that combines regular expressions and machine learning-based named entity recognition, supporting a wide range of use cases such as support tickets, medical notes, and financial transactions. PEyeEye AI is delivered as an API with two primary endpoints for redaction and rehydration, and offers SDKs for multiple programming languages including Python, TypeScript, and Go. shield() call, or developers can interact directly with the API. The service supports streaming with server-sent events and partial-token buffering, making it suitable for real-time applications. The platform emphasizes composability, allowing users to customize detection policies and entity types as needed. 3 for data in transit and opinionated defaults for privacy protection. Designed for developers and organizations deploying AI features that handle sensitive user data, PEyeEye AI provides a stateless, privacy-preserving solution to safely leverage LLM capabilities without leaking confidential information.
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