| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Detects personally identifiable information, protected health information, payment card data, and confidential company information across 50+ entity types by analyzing semantic context rather than pattern matching alone. Unlike regex-based approaches, the system reads contextual relationships between tokens to distinguish legitimate uses of PII-like strings (e.g., 'John' as a common noun vs. a person's name) and handles real-world data quality issues including ASR errors, OCR mistakes, handwritten forms, and conversational disfluencies. Supports 52 languages including code-switching scenarios.
Unique: Uses contextual semantic analysis ('reads context' per product claims) rather than pattern matching to detect PII, enabling accurate identification even with ASR errors, OCR mistakes, and conversational disfluencies where regex-based tools fail. Handles code-switching and 52 languages natively.
vs alternatives: Achieves 99.5% accuracy on physician conversations (Providence Health case study) vs. AWS Comprehend, Microsoft Presidio, and Google DLP which reportedly drop to 60-70% accuracy on real-world noisy data.
Redacts, pseudonymizes, or synthetically replaces detected PII entities across text, documents, images, and audio using configurable transformation strategies. The system applies entity-specific redaction rules (e.g., masking credit card numbers with asterisks, replacing names with consistent pseudonyms, generating synthetic replacements) while preserving document structure and downstream usability. Supports batch processing across multiple file formats (PDF, DOCX, XLS, XLSX, PPTX, XML, JSON, CSV) and image formats (TIFF, PNG, JPEG with OCR-based redaction).
Unique: Applies context-aware redaction across multiple modalities (text, documents, images, audio) with entity linking to maintain consistency across related documents — e.g., the same person's name is replaced with the same pseudonym throughout a dataset. Handles structured formats (JSON, CSV, XML) with schema-aware redaction.
vs alternatives: Supports multi-format document redaction (PDF, DOCX, spreadsheets, presentations) in a single API call, whereas most PII tools require separate pipelines for text vs. documents vs. images.
Detects PII across 52 languages including support for code-switching (mixing multiple languages within the same document or conversation). The system handles language-specific entity formats (e.g., different date formats, phone number patterns, address structures across countries) and recognizes PII in multilingual contexts without requiring explicit language specification. Supports real-world multilingual data including conversational transcripts with language mixing.
Unique: Supports PII detection across 52 languages including code-switching (language mixing) without requiring explicit language specification, handling language-specific entity formats and multilingual contexts natively.
vs alternatives: Enables code-switched and multilingual PII detection vs. language-specific tools (AWS Comprehend supports ~10 languages, Google DLP is English-focused) which require separate processing per language or fail on code-switched text.
Detects and redacts PII in images and scanned documents by performing optical character recognition (OCR) to extract text and then applying context-aware PII detection to the extracted content. The system handles real-world image quality issues including poor resolution, skewed text, handwritten annotations, and partial visibility. Supports TIFF, PNG, and JPEG formats and can redact detected PII directly in the image output.
Unique: Combines OCR with context-aware PII detection to handle scanned documents and images, including handwritten forms and poor-quality scans, with direct image redaction output preserving document structure.
vs alternatives: Enables end-to-end image PII detection and redaction vs. separate OCR + text PII tools which require manual integration and intermediate text extraction steps.
Detects PII in audio files and speech transcripts by handling automatic speech recognition (ASR) errors, conversational disfluencies, and real-world speech patterns. The system recognizes that ASR output contains errors and uses contextual analysis to identify PII despite transcription mistakes (e.g., 'John' transcribed as 'Jon', 'Smith' as 'Smyth'). Supports audio file input and transcript text with conversational patterns including filler words, interruptions, and informal speech.
Unique: Detects PII in audio and transcripts while handling ASR errors and conversational disfluencies, achieving 99.5% accuracy on physician conversations (Providence Health case study) despite speech recognition imperfections.
vs alternatives: Handles ASR-corrupted transcripts with context-aware detection vs. text-only PII tools which fail when applied to noisy ASR output with transcription errors.
De-identifies structured data formats (JSON, XML, CSV) by applying schema-aware redaction that preserves data structure and enables downstream processing. The system understands structured data schemas and applies entity-specific redaction rules to relevant fields while maintaining referential integrity and data relationships. Supports nested structures, arrays, and complex data hierarchies.
Unique: Applies schema-aware de-identification to structured data formats (JSON, XML, CSV) preserving data structure and relationships while redacting PII, enabling downstream processing and analytics on de-identified structured data.
vs alternatives: Maintains structured data integrity during de-identification vs. text-based PII tools which treat structured data as plain text and may corrupt structure or break relationships.
Connects related PII entities across multiple documents and extracts relationships between detected entities to maintain data consistency and enable entity resolution. The system identifies when the same person, organization, or account appears across different documents (e.g., matching 'John Smith' in one document with 'J. Smith' in another) and tracks relationships (e.g., 'patient John Smith was treated by Dr. Jane Doe'). This enables consistent pseudonymization where the same entity receives the same replacement across a dataset.
Unique: Performs cross-document entity linking to maintain pseudonymization consistency — the same entity receives the same replacement across a dataset. Extracts relationships between entities to enable knowledge graph construction while preserving privacy through consistent entity replacement.
vs alternatives: Enables consistent de-identification across multi-document datasets where standard PII tools would independently redact each document, potentially creating inconsistent pseudonyms for the same entity.
Deploys the de-identification engine as a containerized service within customer infrastructure (on-premises or customer VPC) ensuring sensitive data never leaves the customer's network. The system runs as a Docker container in the customer's environment, processes data locally, and returns only de-identified results. This architecture enables compliance with strict data residency requirements (HIPAA, GDPR, CCPA) and eliminates data transmission risk to third-party servers.
Unique: Provides containerized on-premises deployment where sensitive data never leaves customer infrastructure — data is processed locally and only de-identified results are returned. Enables compliance with strict data residency and data sovereignty requirements without relying on cloud infrastructure.
vs alternatives: Eliminates data transmission risk vs. cloud-based PII detection services (AWS Comprehend, Google DLP) which require sending sensitive data to external servers, making it suitable for highly regulated industries with strict data residency mandates.
+6 more capabilities
Evaluates LLM outputs against curated question sets spanning three distinct hazard domains (biosecurity, cybersecurity, chemical security) using domain-expert-validated benchmarks. The assessment framework maps model responses to risk levels within each domain, enabling quantitative measurement of dangerous capability presence. Responses are scored against rubrics developed by security domain experts to identify whether models can produce actionable harmful information.
Unique: Combines expert-validated questions across three distinct security domains (biosecurity, cybersecurity, chemical) into a unified benchmark framework, rather than treating each domain separately. Uses domain-expert rubrics for scoring rather than automated classifiers, ensuring nuanced assessment of harmful capability presence.
vs alternatives: More comprehensive than single-domain safety benchmarks (e.g., ToxiGen for toxicity) because it measures dangerous knowledge across multiple hazard categories simultaneously, enabling holistic safety evaluation.
Provides standardized evaluation infrastructure to measure the effectiveness of unlearning techniques (methods that remove dangerous capabilities from trained models) by comparing model performance before and after unlearning interventions. The framework isolates the impact of unlearning by holding the benchmark constant while varying the model state, enabling quantitative assessment of whether dangerous knowledge has been successfully suppressed.
Unique: Provides a standardized evaluation harness specifically designed for unlearning research, with built-in comparison logic and side-effect detection. Unlike generic benchmarks, it explicitly measures delta between model states and flags unintended capability loss.
vs alternatives: More rigorous than ad-hoc unlearning evaluation because it enforces consistent benchmark administration, statistical testing, and side-effect measurement across all methods being compared.
WMDP scores higher at 63/100 vs Private AI at 55/100. Private AI leads on quality, while WMDP is stronger on ecosystem.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
Implements a structured scoring framework where model responses to dangerous knowledge questions are evaluated against expert-developed rubrics that assess the degree of hazard (e.g., specificity, actionability, completeness of harmful information). Responses are scored on multi-point scales (typically 0-4 or 0-5) rather than binary pass/fail, capturing nuance in how dangerous a model's output actually is. Rubrics are domain-specific (biosecurity, cybersecurity, chemical) and developed by subject matter experts to ensure validity.
Unique: Uses domain-expert-developed multi-point rubrics rather than automated classifiers or binary labels, enabling nuanced assessment of dangerous knowledge severity. Rubrics are calibrated to distinguish between vague, incomplete, and highly actionable harmful information.
vs alternatives: More interpretable and defensible than black-box classifiers because rubric criteria are explicit and expert-validated; enables stakeholders to understand why a response received a particular score.
Analyzes patterns in how dangerous knowledge correlates across the three benchmark domains (biosecurity, cybersecurity, chemical security), identifying whether models that excel at suppressing one type of hazard tend to suppress others. The analysis uses statistical correlation and clustering techniques to reveal whether dangerous capabilities are independent or coupled in model behavior. This enables understanding of whether unlearning interventions have domain-specific or global effects.
Unique: Explicitly analyzes relationships between dangerous knowledge across domains rather than treating each domain independently. Enables discovery of whether hazards are coupled or independent in model behavior.
vs alternatives: Provides deeper insight than single-domain benchmarks by revealing how safety properties interact across different hazard categories, informing more effective unlearning strategies.
Manages the creation, validation, and versioning of benchmark questions and rubrics through a structured curation pipeline involving domain experts, adversarial testing, and iterative refinement. The pipeline ensures questions are sufficiently difficult to elicit dangerous knowledge without being unrealistic, and rubrics are calibrated through inter-rater agreement studies. Version control enables tracking of benchmark evolution and ensures reproducibility across research papers.
Unique: Implements a formal curation pipeline with expert validation and inter-rater agreement checks, rather than ad-hoc question collection. Versioning enables reproducible research and transparent tracking of benchmark evolution.
vs alternatives: More rigorous than informal benchmarks because it enforces expert review, inter-rater validation, and version control, reducing bias and enabling reproducible comparisons across papers.
Provides a unified interface for evaluating diverse LLM architectures (open-source models, API-based models, fine-tuned variants) by abstracting away implementation differences. The abstraction handles API calls (OpenAI, Anthropic, etc.), local inference (Hugging Face, Ollama), and custom model serving, enabling consistent benchmark administration across heterogeneous model types. This enables fair comparison between models with different deployment modalities.
Unique: Abstracts away differences between API-based, local, and custom-deployed models through a unified interface, enabling fair comparison without reimplementing benchmark logic for each model type.
vs alternatives: More flexible than model-specific benchmarks because it supports any LLM architecture without code changes, reducing friction for researchers evaluating new models.
Implements rigorous statistical testing to determine whether differences in dangerous knowledge scores between models or unlearning methods are statistically significant or due to random variation. Uses techniques like bootstrap confidence intervals, permutation tests, and effect size estimation to quantify uncertainty in benchmark results. This prevents overconfident claims about safety improvements that may not be robust.
Unique: Integrates formal statistical testing into the benchmark evaluation pipeline rather than relying on point estimates, ensuring claims about safety improvements are statistically justified.
vs alternatives: More rigorous than informal comparisons because it quantifies uncertainty and prevents overconfident claims about safety improvements that may not be robust to sampling variation.
Employs adversarial testing techniques to validate that benchmark questions reliably elicit dangerous knowledge and cannot be easily circumvented by prompt engineering. Red-teamers attempt to find questions that fail to elicit dangerous knowledge or rubric edge cases, and the benchmark is iteratively refined based on findings. This ensures the benchmark is robust to adversarial adaptation and captures genuine dangerous capabilities rather than surface-level patterns.
Unique: Incorporates formal red-teaming into the benchmark validation pipeline rather than assuming questions are robust, ensuring the benchmark remains effective against adversarial adaptation.
vs alternatives: More robust than static benchmarks because it actively searches for evasion techniques and iteratively refines questions, reducing the risk that models can circumvent the benchmark through prompt engineering.