{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"presidio","slug":"presidio","name":"Presidio","type":"repo","url":"https://github.com/microsoft/presidio","page_url":"https://unfragile.ai/presidio","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"presidio__cap_0","uri":"capability://safety.moderation.context.aware.pii.entity.recognition.via.hybrid.recognizer.pipeline","name":"context-aware pii entity recognition via hybrid recognizer pipeline","description":"Detects 30+ PII entity types (names, SSNs, credit cards, phone numbers, bitcoin wallets, etc.) in unstructured text using a pluggable recognizer system that combines NLP-based entity extraction, regex pattern matching, and machine learning models. The Analyzer component orchestrates multiple recognizers in sequence, applies context enhancement to reduce false positives, and returns scored entity matches with confidence levels and character offsets for precise redaction.","intents":["I need to scan customer support transcripts and identify all personally identifiable information before storing them","I want to detect sensitive data in user-generated content with configurable confidence thresholds to balance precision vs recall","I need to support multiple languages and custom entity types specific to my domain (e.g., internal employee IDs, medical record numbers)"],"best_for":["compliance teams building data privacy pipelines for GDPR/HIPAA/PCI-DSS","data engineers preprocessing datasets before ML training","security teams implementing data loss prevention (DLP) systems"],"limitations":["No guarantee of 100% accuracy — requires defense-in-depth strategy with human review for high-stakes data","NLP-based recognizers require spaCy model loading (~100-500MB memory per language model)","Context enhancement adds ~50-200ms latency per text chunk depending on NLP model size","Regex recognizers may produce false positives in domain-specific contexts (e.g., product codes matching SSN patterns)"],"requires":["Python 3.10+","spaCy language models (en_core_web_sm or larger for NLP-based recognition)","Optional: transformers library for custom ML-based recognizers"],"input_types":["plain text strings","unstructured natural language (emails, chat, documents)"],"output_types":["JSON array of RecognitionResult objects with entity type, score, start/end character positions"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_1","uri":"capability://tool.use.integration.pluggable.recognizer.framework.with.custom.entity.type.support","name":"pluggable recognizer framework with custom entity type support","description":"Provides an extensible architecture for building custom PII recognizers by implementing a base Recognizer interface and registering them with the Analyzer. Developers can create domain-specific recognizers using regex patterns, spaCy NLP pipelines, external ML models, or API calls (e.g., calling a custom ML service to detect proprietary entity types). The framework handles recognizer composition, scoring aggregation, and context passing without requiring framework modifications.","intents":["I need to detect company-specific sensitive data like internal project codes, employee badge numbers, or proprietary identifiers","I want to integrate my existing ML model or third-party service for entity detection into Presidio's pipeline","I need language-specific recognizers for non-English PII patterns (e.g., German tax IDs, Japanese phone numbers)"],"best_for":["enterprise teams with domain-specific PII requirements","ML engineers building custom entity extraction models","organizations supporting multiple languages and regulatory frameworks"],"limitations":["Custom recognizers must implement the Recognizer base class interface — no declarative/YAML-only approach for complex logic","Recognizer composition is sequential; no built-in parallelization for high-throughput scenarios","Scoring aggregation across multiple recognizers is additive; no learned weighting or ensemble methods","Custom recognizers are responsible for their own performance optimization and caching"],"requires":["Python 3.10+","Understanding of Presidio's Recognizer base class and RecognitionResult schema","Optional: spaCy for NLP-based custom recognizers, or external ML framework (TensorFlow, PyTorch)"],"input_types":["text strings","spaCy Doc objects (for NLP-aware recognizers)"],"output_types":["RecognitionResult objects with entity type, score, start/end positions"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_10","uri":"capability://data.processing.analysis.language.agnostic.entity.type.system.with.30.built.in.types.and.custom.type.support","name":"language-agnostic entity type system with 30+ built-in types and custom type support","description":"Defines a standardized entity type taxonomy (PERSON, EMAIL, PHONE_NUMBER, CREDIT_CARD, SSN, LOCATION, ORGANIZATION, etc.) that is language-agnostic and extensible. Built-in recognizers target these entity types, and custom recognizers can define new types (e.g., EMPLOYEE_ID, MEDICAL_RECORD_NUMBER). Entity types are used for operator mapping (e.g., 'PERSON -> redact'), confidence thresholding, and filtering. The system supports entity type hierarchies (e.g., PERSON is a subtype of IDENTITY).","intents":["I want a standard vocabulary for PII types across my organization to ensure consistent policies","I need to define custom entity types for domain-specific PII (medical record numbers, internal employee IDs)","I want to apply different anonymization strategies to different entity types (redact names, hash SSNs, encrypt credit cards)"],"best_for":["organizations standardizing PII terminology across teams","enterprises with domain-specific PII requirements","compliance teams defining entity-type-specific policies"],"limitations":["Entity type taxonomy is flat; no built-in support for hierarchies or relationships between types","Custom entity types must be defined in code; no YAML-based type definition","No built-in type validation; custom types can conflict with built-in types if not carefully named","Entity type mapping to operators is many-to-one; cannot apply multiple operators to same entity type"],"requires":["Python 3.10+","Understanding of entity types and domain-specific PII"],"input_types":["entity type names (strings)"],"output_types":["entity type definitions with recognizers and operators"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_11","uri":"capability://automation.workflow.docker.containerization.and.kubernetes.deployment","name":"docker containerization and kubernetes deployment","description":"Provides pre-built Docker images for Analyzer, Anonymizer, and Image Redactor components that can be deployed as microservices. Includes Docker Compose configurations for local development and Kubernetes manifests for production deployments. Supports scaling individual components independently, health checks, and integration with container orchestration platforms. Enables rapid deployment without manual Python environment setup.","intents":["I need to deploy Presidio as containerized microservices in our Kubernetes cluster","I want to scale the Analyzer service independently from the Anonymizer based on load","I need to integrate Presidio into our Docker-based CI/CD pipeline for automated data protection"],"best_for":["DevOps teams deploying Presidio in containerized environments","organizations using Kubernetes for orchestration","teams requiring reproducible deployments across development, staging, and production"],"limitations":["Docker images add overhead compared to native Python execution (~50-100MB per image)","Kubernetes deployment requires cluster setup and operational expertise","No built-in service mesh integration — requires external tools for advanced networking","Health checks and readiness probes require custom configuration per deployment","Image updates require rebuilding and redeploying containers — no hot-reload"],"requires":["Docker runtime (Docker Desktop, Docker Engine, or container runtime)","Optional: Kubernetes cluster (1.20+) for production deployments","Optional: Docker Compose for local development","Container registry for storing images (Docker Hub, ECR, GCR, etc.)"],"input_types":["Docker image specifications","Kubernetes manifests (YAML)","Docker Compose configurations"],"output_types":["running Docker containers","Kubernetes pods and services"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_12","uri":"capability://safety.moderation.multi.language.nlp.support.with.pluggable.models","name":"multi-language nlp support with pluggable models","description":"Supports PII detection across multiple languages (English, Spanish, Portuguese, French, German, Chinese, Dutch, Greek, Italian, Lithuanian, Norwegian, Polish, Romanian, Russian, Ukrainian) through pluggable spaCy language models. Allows users to specify language per analysis or auto-detect language. Supports custom NLP models by implementing a custom NLP engine interface. Enables language-specific context enhancement and recognizer rules.","intents":["I need to detect PII in customer support tickets that come in multiple languages","I want to use a custom spaCy model trained on our domain-specific language for better accuracy","I need to process documents in Spanish and German with language-specific entity recognition"],"best_for":["multinational organizations processing data in multiple languages","teams with domain-specific language requirements","organizations needing language-aware PII detection"],"limitations":["Each language requires a separate spaCy model (~100-300MB per model) — memory overhead for multi-language support","Language auto-detection adds latency and can be inaccurate for mixed-language content","Custom NLP models require training data and expertise — no pre-trained models provided","Context enhancement rules are language-specific and may not transfer across languages","Some languages have limited recognizer coverage — not all entity types supported in all languages"],"requires":["Python 3.10+","presidio-analyzer package","spaCy language models for required languages (e.g., en_core_web_md, es_core_news_md)","Optional: langdetect or textblob for language auto-detection","Optional: custom spaCy models for domain-specific NLP"],"input_types":["text in supported languages","language code (e.g., 'en', 'es', 'de')"],"output_types":["detected entities with language-specific context"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_2","uri":"capability://safety.moderation.multi.operator.pii.anonymization.with.reversible.transformations","name":"multi-operator pii anonymization with reversible transformations","description":"De-identifies detected PII entities using a pluggable operator framework that supports multiple anonymization strategies: replace (with fixed/random values), redact (mask with asterisks), hash (deterministic hashing for consistency), encrypt (reversible encryption with key management), mask (partial masking like XXX-XX-1234), and custom operators. The Anonymizer component applies operators to text based on entity type mappings, preserves non-PII content, and supports deanonymization for authorized users via encrypted operator state.","intents":["I need to redact PII in logs/transcripts for sharing with support teams while preserving readability","I want to hash sensitive data consistently so the same person always gets the same hash value across datasets","I need reversible anonymization so authorized users can decrypt PII for legitimate business purposes (e.g., customer service)"],"best_for":["data teams preparing datasets for analytics/ML training while maintaining privacy","compliance officers implementing data minimization strategies","organizations requiring audit trails of who accessed deanonymized data"],"limitations":["Reversible operators (encrypt) require secure key management — Presidio does not provide built-in key storage; requires external KMS (Azure Key Vault, AWS KMS, etc.)","Hash operator is deterministic but not cryptographically secure for adversarial scenarios — use only for non-adversarial privacy","Operator composition is per-entity-type; no conditional logic (e.g., 'redact if confidence < 0.8, hash if confidence >= 0.8')","Custom operators must handle edge cases (empty strings, special characters, unicode) — framework provides no validation"],"requires":["Python 3.10+","For encrypt operator: cryptography library and external key management service","For custom operators: implementation of Operator base class"],"input_types":["text strings with character offsets of PII entities","entity type to operator mapping configuration"],"output_types":["anonymized text string","optional: operator state for deanonymization"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_3","uri":"capability://image.visual.ocr.based.pii.detection.and.redaction.in.images.and.dicom.medical.images","name":"ocr-based pii detection and redaction in images and dicom medical images","description":"Detects and redacts PII in image files (PNG, JPG) and medical DICOM images by extracting text via Optical Character Recognition (OCR), running the extracted text through the Analyzer to identify PII entities, and then redacting those regions in the original image using bounding boxes. The Image Redactor component handles image format conversion, OCR engine integration (Tesseract or cloud-based), and supports both text-based and visual redaction (blurring, pixelation) for DICOM images with medical-specific entity types.","intents":["I need to redact patient names and medical record numbers from scanned medical documents before sharing with researchers","I want to automatically remove PII from screenshots and photos in our knowledge base before publishing","I need to process DICOM medical images and redact patient identifiers while preserving diagnostic content"],"best_for":["healthcare organizations handling medical imaging and scanned documents","content teams managing user-generated images with PII","research institutions preparing datasets for publication"],"limitations":["OCR accuracy depends on image quality, resolution, and font — poor quality images may miss or misidentify PII","OCR processing adds significant latency (~1-5 seconds per image depending on size and OCR engine)","DICOM redaction requires careful handling to preserve medical metadata and image integrity — incorrect redaction can corrupt diagnostic data","Bounding box-based redaction may not align perfectly with text boundaries, especially for rotated or skewed text","No built-in support for handwritten text or non-Latin scripts in standard OCR"],"requires":["Python 3.10+","Tesseract OCR engine (system dependency) OR cloud OCR service credentials (Azure Computer Vision, Google Cloud Vision)","Pillow library for image processing","Optional: pydicom for DICOM image handling"],"input_types":["image files (PNG, JPG, JPEG)","DICOM medical image files (.dcm)"],"output_types":["redacted image file (PNG, JPG)","redacted DICOM file with metadata preserved"],"categories":["image-visual","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_4","uri":"capability://data.processing.analysis.structured.data.pii.detection.and.protection.for.csv.json.and.parquet.files","name":"structured data pii detection and protection for csv, json, and parquet files","description":"Detects and anonymizes PII in structured datasets (CSV, JSON, Parquet, databases) by applying the Analyzer to column values, mapping detected entities to anonymization operators, and writing de-identified output in the same format. The Structured component handles schema inference, batch processing of large files, and supports both column-level (redact entire column) and cell-level (redact specific values) anonymization strategies. Integrates with PySpark for distributed processing of multi-gigabyte datasets.","intents":["I need to remove PII from a CSV export of customer data before sharing with a third-party analytics vendor","I want to de-identify a Parquet dataset for ML training while preserving non-PII columns and data types","I need to process a large JSON log file and redact user identifiers while maintaining valid JSON structure"],"best_for":["data engineers preparing datasets for analytics and ML","compliance teams automating data minimization workflows","organizations processing large-scale structured data with PII"],"limitations":["Column-level anonymization is coarse-grained — redacts entire columns, losing all data in that column","Cell-level anonymization requires per-row analysis, which is slower than column-level for large datasets","No built-in support for relational integrity constraints (e.g., foreign keys) — anonymizing one table may break joins with others","PySpark integration requires Spark cluster setup and adds operational complexity","Schema inference may fail on heterogeneous or deeply nested JSON structures"],"requires":["Python 3.10+","pandas library for CSV/JSON processing","pyarrow for Parquet support","Optional: PySpark 3.0+ for distributed processing"],"input_types":["CSV files","JSON files (line-delimited or standard)","Parquet files","pandas DataFrames"],"output_types":["de-identified CSV, JSON, or Parquet files","pandas DataFrame with anonymized columns"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_5","uri":"capability://tool.use.integration.rest.api.microservice.deployment.with.docker.and.kubernetes.orchestration","name":"rest api microservice deployment with docker and kubernetes orchestration","description":"Exposes Presidio's core components (Analyzer, Anonymizer, Image Redactor) as RESTful microservices via Flask/FastAPI, enabling integration into larger systems without Python dependencies. Each component runs in a separate Docker container (ports 5002 for Analyzer, 5001 for Anonymizer, 5003 for Image Redactor) with independent scaling, and supports Kubernetes deployment with auto-scaling, health checks, and service discovery. The REST API abstracts implementation details and enables polyglot integration (Java, Go, Node.js, etc.).","intents":["I need to integrate Presidio into a Java/Node.js application without embedding Python","I want to deploy Presidio as microservices in Kubernetes with auto-scaling based on request volume","I need to call PII detection from a web application without managing Python dependencies"],"best_for":["polyglot teams using multiple programming languages","organizations deploying on Kubernetes or cloud platforms","teams requiring independent scaling of detection vs anonymization"],"limitations":["Network latency between microservices adds ~50-200ms per request compared to in-process Python calls","Docker image size is large (~1-2GB) due to spaCy models and OCR dependencies","Kubernetes deployment requires container orchestration expertise and operational overhead","REST API serialization/deserialization adds overhead for large text payloads (>1MB)","No built-in authentication/authorization — requires external API gateway (Kong, Envoy) for security"],"requires":["Docker 20.10+ or container runtime","Kubernetes 1.20+ (optional, for orchestration)","HTTP client library in target language"],"input_types":["JSON payloads with text, image paths, or file references"],"output_types":["JSON responses with detected entities, anonymized text, or redacted images"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_6","uri":"capability://safety.moderation.context.aware.confidence.scoring.with.entity.type.specific.thresholds","name":"context-aware confidence scoring with entity-type-specific thresholds","description":"Assigns confidence scores (0-1) to detected PII entities based on recognizer agreement, context analysis, and entity-type-specific patterns. The Analyzer aggregates scores from multiple recognizers (NLP, regex, custom) and applies context enhancement to reduce false positives (e.g., 'John' in 'John Smith' is more likely a name than 'John' as a standalone word). Supports per-entity-type confidence thresholds, enabling fine-grained control (e.g., require 0.9 confidence for SSNs but accept 0.5 for names).","intents":["I want to filter out low-confidence detections to reduce false positives in my anonymization pipeline","I need different confidence thresholds for different entity types based on my risk tolerance","I want to understand why Presidio detected something as PII and adjust thresholds accordingly"],"best_for":["teams requiring high precision (low false positive rate) in PII detection","organizations with domain-specific confidence requirements","compliance teams auditing PII detection decisions"],"limitations":["Confidence scores are heuristic-based, not probabilistic — no statistical guarantees","Context enhancement is language-specific and may fail for code-mixed or non-standard text","No built-in mechanism to learn optimal thresholds from labeled data — requires manual tuning","Threshold tuning is dataset-specific; thresholds optimized for one domain may not transfer to another"],"requires":["Python 3.10+","Understanding of entity types and domain-specific risk profiles"],"input_types":["text strings"],"output_types":["RecognitionResult objects with confidence scores and entity types"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_7","uri":"capability://safety.moderation.deanonymization.with.encrypted.operator.state.and.key.management.integration","name":"deanonymization with encrypted operator state and key management integration","description":"Enables authorized users to reverse anonymization applied by encrypt operators by storing encrypted operator state (encryption keys, salt values) alongside anonymized data. The Deanonymizer component uses stored state to decrypt PII values, supporting integration with external key management systems (Azure Key Vault, AWS KMS, HashiCorp Vault) for secure key storage and rotation. Supports audit logging of deanonymization requests for compliance.","intents":["I need to allow customer service representatives to view original customer names/emails for support purposes while keeping data anonymized in logs","I want to decrypt anonymized data for authorized users without re-processing the original dataset","I need to audit who accessed deanonymized data and when for compliance reporting"],"best_for":["organizations requiring selective access to PII for legitimate business purposes","compliance teams implementing fine-grained access control","healthcare/financial institutions with regulatory audit requirements"],"limitations":["Deanonymization requires secure key management — keys must be stored separately from encrypted data, adding operational complexity","Operator state storage is application-specific; Presidio provides no built-in persistence layer","Audit logging is not built-in; requires external logging system (ELK, Splunk, CloudTrail)","Deanonymization is only possible for encrypt operators; redacted/hashed data cannot be recovered","Key rotation requires re-encrypting all operator state, which is computationally expensive for large datasets"],"requires":["Python 3.10+","External key management service (Azure Key Vault, AWS KMS, Vault)","Secure storage for operator state (encrypted database, secure file storage)","Optional: audit logging system"],"input_types":["anonymized text with encrypted operator state"],"output_types":["original PII values (decrypted)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_8","uri":"capability://automation.workflow.no.code.configuration.via.yaml.for.entity.to.operator.mappings.and.recognizer.selection","name":"no-code configuration via yaml for entity-to-operator mappings and recognizer selection","description":"Allows non-developers to configure Presidio's behavior via YAML files without writing Python code. YAML configuration specifies which recognizers to enable, entity-type-to-operator mappings (e.g., 'PERSON -> redact', 'SSN -> encrypt'), confidence thresholds, and custom entity types. The framework loads YAML at startup and applies configurations without code changes, enabling rapid experimentation and deployment of policy changes.","intents":["I want to change anonymization operators (redact vs hash vs encrypt) without modifying code","I need to adjust confidence thresholds per entity type based on feedback without redeploying","I want to enable/disable specific recognizers for different environments (dev vs prod) via configuration"],"best_for":["non-technical compliance officers managing PII policies","teams requiring rapid policy iteration without code deployment","organizations with multiple environments requiring different configurations"],"limitations":["YAML configuration is limited to built-in recognizers and operators — complex custom logic still requires Python code","No validation of YAML syntax at load time; errors may only appear at runtime","Configuration changes require application restart; no hot-reload capability","No version control or audit trail for configuration changes (requires external tooling)"],"requires":["Python 3.10+","YAML file in correct format"],"input_types":["YAML configuration files"],"output_types":["Analyzer/Anonymizer instances configured per YAML"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__cap_9","uri":"capability://automation.workflow.batch.processing.with.progress.tracking.and.error.handling.for.large.scale.datasets","name":"batch processing with progress tracking and error handling for large-scale datasets","description":"Processes large text/image/structured data files in batches with configurable batch size, progress tracking, and graceful error handling. The framework processes each batch independently, reports progress (items processed, items failed, estimated time remaining), and continues processing on errors (e.g., skips malformed images, logs errors, continues with next batch). Supports parallel batch processing via multiprocessing or PySpark for distributed execution.","intents":["I need to anonymize a 10GB CSV file without loading it entirely into memory","I want to process 100,000 images and track progress without the job failing on a single corrupted image","I need to parallelize PII detection across multiple CPU cores to reduce processing time"],"best_for":["data engineers processing large-scale datasets","organizations with strict SLA requirements for batch jobs","teams requiring visibility into long-running anonymization jobs"],"limitations":["Batch processing is sequential by default; parallel processing requires explicit multiprocessing/PySpark setup","Progress tracking adds overhead (~5-10% slowdown) due to logging and state management","Error handling is per-batch; if a batch fails, entire batch is skipped (no per-item recovery)","No built-in checkpointing; if job crashes, must restart from beginning (requires external state management)"],"requires":["Python 3.10+","Optional: multiprocessing for parallelization, PySpark for distributed processing"],"input_types":["large text files","image directories","CSV/JSON/Parquet files"],"output_types":["anonymized files in same format","progress logs"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"presidio__headline","uri":"capability://data.processing.analysis.open.source.pii.detection.and.anonymization.sdk","name":"open-source pii detection and anonymization sdk","description":"Microsoft's Presidio is an open-source SDK designed for the detection and anonymization of Personally Identifiable Information (PII) in text and images, enabling organizations to comply with data privacy regulations.","intents":["best PII detection tool","open-source anonymization SDK for data privacy","how to detect PII in images","anonymization framework for sensitive data","customizable PII detection solutions"],"best_for":["organizations needing data privacy compliance"],"limitations":["may require additional systems for 100% accuracy"],"requires":["Python environment"],"input_types":["text","images"],"output_types":["anonymized data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","spaCy language models (en_core_web_sm or larger for NLP-based recognition)","Optional: transformers library for custom ML-based recognizers","Understanding of Presidio's Recognizer base class and RecognitionResult schema","Optional: spaCy for NLP-based custom recognizers, or external ML framework (TensorFlow, PyTorch)","Understanding of entity types and domain-specific PII","Docker runtime (Docker Desktop, Docker Engine, or container runtime)","Optional: Kubernetes cluster (1.20+) for production deployments","Optional: Docker Compose for local development","Container registry for storing images (Docker Hub, ECR, GCR, etc.)"],"failure_modes":["No guarantee of 100% accuracy — requires defense-in-depth strategy with human review for high-stakes data","NLP-based recognizers require spaCy model loading (~100-500MB memory per language model)","Context enhancement adds ~50-200ms latency per text chunk depending on NLP model size","Regex recognizers may produce false positives in domain-specific contexts (e.g., product codes matching SSN patterns)","Custom recognizers must implement the Recognizer base class interface — no declarative/YAML-only approach for complex logic","Recognizer composition is sequential; no built-in parallelization for high-throughput scenarios","Scoring aggregation across multiple recognizers is additive; no learned weighting or ensemble methods","Custom recognizers are responsible for their own performance optimization and caching","Entity type taxonomy is flat; no built-in support for hierarchies or relationships between types","Custom entity types must be defined in code; no YAML-based type definition","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=presidio","compare_url":"https://unfragile.ai/compare?artifact=presidio"}},"signature":"IWufmDtHmuxcKuPvalYT815wi2No6hC/lw/FsVyAf8RgsXFjmMshI/bn5E4cftQUpwqzGMfUVxfVsVlASS3pCA==","signedAt":"2026-06-22T13:21:53.851Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/presidio","artifact":"https://unfragile.ai/presidio","verify":"https://unfragile.ai/api/v1/verify?slug=presidio","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}