{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_dataisland","slug":"dataisland","name":"Dataisland","type":"product","url":"https://www.dataisland.com.ua","page_url":"https://unfragile.ai/dataisland","categories":["data-pipelines","code-review-security"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_dataisland__cap_0","uri":"capability://data.processing.analysis.ai.driven.sensitive.data.classification.and.tagging","name":"ai-driven sensitive data classification and tagging","description":"Automatically identifies and classifies sensitive data elements (PII, PHI, financial records, trade secrets) across unstructured and semi-structured datasets using machine learning models trained on regulatory frameworks (GDPR, HIPAA, SOC 2). The system applies metadata tags and confidence scores to data fields, enabling downstream policy enforcement without manual inventory work. Classification rules are customizable per industry vertical and compliance regime.","intents":["I need to discover what sensitive data exists across our data lake without manually auditing thousands of files","I want to automatically tag PII and PHI so we can apply encryption and access controls consistently","I need to prove to auditors that we've cataloged all regulated data in our systems"],"best_for":["Mid-market to enterprise organizations in finance, healthcare, and legal sectors","Teams managing hybrid data environments (on-prem + cloud)","Compliance officers and data governance teams modernizing legacy systems"],"limitations":["Classification accuracy depends on data quality and format consistency — unstructured text with poor formatting may produce false negatives","No real-time streaming classification — batch processing only, with latency of minutes to hours depending on dataset size","Custom classification models require labeled training data (typically 500+ examples) to achieve >95% accuracy","Limited to text-based sensitive data; image and video PII detection not mentioned in available documentation"],"requires":["Connectivity to data sources (S3, GCS, Azure Blob, on-prem databases via VPN)","Minimum dataset size of 100MB to train effective models","API credentials for target data platforms","Compliance framework specification (GDPR, HIPAA, PCI-DSS, etc.)"],"input_types":["structured data (CSV, Parquet, database tables)","semi-structured data (JSON, XML)","unstructured text (documents, logs, emails)"],"output_types":["classification metadata (JSON with confidence scores)","tagged datasets with sensitivity labels","compliance audit reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_1","uri":"capability://safety.moderation.encryption.at.rest.and.in.transit.policy.enforcement","name":"encryption-at-rest and in-transit policy enforcement","description":"Enforces cryptographic controls across data pipelines by integrating with cloud KMS providers (AWS KMS, Azure Key Vault, GCP Cloud KMS) and on-premises HSMs. Policies are defined declaratively (e.g., 'all PII must use AES-256-GCM with key rotation every 90 days') and automatically applied to classified data during ingestion, transformation, and storage. Supports key versioning, audit logging of all encryption operations, and automated key rotation without application downtime.","intents":["I need to ensure all sensitive data is encrypted at rest and in transit without manually configuring encryption for each data pipeline","I want to enforce a company-wide encryption standard across teams using different cloud providers","I need to prove encryption compliance to auditors with detailed key usage and rotation logs"],"best_for":["Enterprise security teams managing multi-cloud or hybrid infrastructure","Organizations subject to HIPAA, PCI-DSS, or SOC 2 compliance requirements","Teams with limited cryptography expertise who need policy-driven enforcement"],"limitations":["Key management integration requires pre-configured KMS access — no built-in key generation or storage","Performance overhead of 5-15% on data throughput due to encryption/decryption operations","Automated key rotation may cause brief latency spikes during rotation windows","No support for homomorphic encryption or searchable encryption — encrypted data cannot be queried without decryption"],"requires":["Active AWS KMS, Azure Key Vault, or GCP Cloud KMS account with appropriate IAM roles","Network connectivity to KMS endpoints (or on-prem HSM with PKCS#11 interface)","Encryption policy definition in YAML or JSON format","Audit logging infrastructure (CloudWatch, Azure Monitor, or Stackdriver)"],"input_types":["data at rest (databases, object storage, data warehouses)","data in transit (API payloads, message queues, ETL pipelines)"],"output_types":["encrypted data with metadata (key ID, algorithm, timestamp)","encryption audit logs with key usage details","compliance reports showing encryption coverage"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_2","uri":"capability://safety.moderation.access.control.and.role.based.data.masking","name":"access control and role-based data masking","description":"Implements fine-grained access control policies that automatically mask or redact sensitive data based on user roles, departments, and data classification levels. Uses attribute-based access control (ABAC) to evaluate policies at query time, applying transformations like tokenization, hashing, or partial redaction (e.g., showing only last 4 digits of SSN). Integrates with identity providers (Okta, Azure AD, Keycloak) to sync roles and enforce policies consistently across data platforms.","intents":["I need to ensure analysts in different departments only see the data relevant to their role without building separate datasets","I want to automatically redact PII when non-authorized users query sensitive tables","I need to grant contractors temporary access to specific data subsets without copying data to external systems"],"best_for":["Large organizations with complex role hierarchies and multi-department data sharing","Teams managing shared data warehouses or data lakes with mixed sensitivity levels","Regulated industries requiring granular audit trails of data access"],"limitations":["Policy evaluation adds 50-200ms latency per query depending on policy complexity and data volume","Masking transformations are deterministic (same input always produces same masked output) — may allow inference attacks if attacker has access to multiple masked values","Requires identity provider integration — no built-in user management","Cannot mask data in real-time streaming scenarios — only batch and query-time masking supported"],"requires":["Identity provider (Okta, Azure AD, Keycloak, or LDAP) with role/attribute sync capability","Data platform with query interception capability (Snowflake, BigQuery, Redshift, or Databricks)","Policy definition framework (YAML or JSON with attribute matching rules)","Audit logging infrastructure to track masked data access"],"input_types":["user identity and role attributes from identity provider","data classification metadata from classification engine","query requests with user context"],"output_types":["masked or redacted query results","access audit logs with user, timestamp, and data accessed","policy compliance reports"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_3","uri":"capability://data.processing.analysis.automated.data.lineage.and.impact.analysis","name":"automated data lineage and impact analysis","description":"Tracks data flow from source systems through transformations to final outputs, building a directed acyclic graph (DAG) of data dependencies. When sensitive data is reclassified or a security policy changes, the system automatically identifies all downstream datasets and pipelines affected, enabling impact analysis without manual tracing. Supports lineage visualization and generates reports showing which systems access which sensitive data elements.","intents":["I need to understand where our sensitive data flows through the organization so we can apply controls consistently","When we discover a data breach, I need to quickly identify all systems that may have been exposed","I want to know the blast radius before changing an encryption policy or access control rule"],"best_for":["Enterprise data teams managing complex ETL pipelines with 50+ data sources","Organizations responding to data incidents and needing rapid impact assessment","Data governance teams building compliance documentation"],"limitations":["Lineage tracking requires instrumentation of data pipelines — legacy systems without logging may not be fully traceable","DAG construction has O(n²) complexity for n pipeline stages — performance degrades with 1000+ interconnected pipelines","Impact analysis is static (based on code/configuration) — cannot detect runtime data flows or undocumented access patterns","No support for lineage across heterogeneous systems without custom connectors"],"requires":["Data pipeline instrumentation (Airflow, dbt, Spark, or custom logging)","Metadata repository or data catalog integration (Collibra, Alation, or custom)","Network connectivity to all data sources and transformation engines","Schema information for all datasets in the lineage"],"input_types":["pipeline definitions (DAGs from Airflow, dbt models, Spark jobs)","data catalog metadata (table schemas, column lineage)","query logs from data warehouses"],"output_types":["lineage graph (JSON or GraphML format)","impact analysis reports (affected datasets, systems, users)","lineage visualizations (interactive DAG diagrams)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_4","uri":"capability://automation.workflow.compliance.audit.report.generation.and.evidence.collection","name":"compliance audit report generation and evidence collection","description":"Automatically generates audit reports demonstrating compliance with regulatory frameworks (GDPR, HIPAA, SOC 2, PCI-DSS) by collecting evidence from security controls, access logs, encryption configurations, and data classification results. Reports include control attestations, remediation tracking, and exception management. Supports scheduled report generation and integrates with audit management platforms (Workiva, AuditBoard) for centralized compliance tracking.","intents":["I need to generate HIPAA compliance reports for our annual audit without manually collecting evidence from 10+ systems","I want to track remediation of security findings and prove to auditors that we've addressed them","I need to demonstrate to regulators that we've implemented required data protection controls"],"best_for":["Compliance officers and audit teams in regulated industries","Organizations preparing for SOC 2, ISO 27001, or regulatory audits","Teams managing multiple compliance frameworks simultaneously"],"limitations":["Report accuracy depends on completeness of underlying control implementations — cannot generate evidence for controls that don't exist","Regulatory requirements change frequently — framework definitions require manual updates or vendor maintenance","Reports are point-in-time snapshots — continuous compliance monitoring requires scheduled report generation","No built-in remediation workflow — exception management requires manual tracking or integration with ticketing systems"],"requires":["Compliance framework definitions (GDPR, HIPAA, SOC 2, PCI-DSS, etc.)","Audit logging infrastructure capturing security events","Access to security control configurations (encryption, access policies, etc.)","Integration with audit management platform (optional but recommended)"],"input_types":["security control configurations","access logs and audit trails","encryption key management records","data classification and lineage metadata"],"output_types":["compliance audit reports (PDF, HTML, or JSON)","control attestation evidence","remediation tracking reports","exception management logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_5","uri":"capability://data.processing.analysis.data.transformation.and.anonymization.pipeline.orchestration","name":"data transformation and anonymization pipeline orchestration","description":"Orchestrates ETL workflows that apply anonymization and pseudonymization techniques (differential privacy, k-anonymity, l-diversity) to sensitive datasets, enabling safe data sharing for analytics and testing. Pipelines are defined declaratively and executed on distributed compute (Spark, Dask) with automatic scaling. Supports reversible pseudonymization (tokenization with secure key storage) for authorized users and irreversible anonymization for external sharing.","intents":["I need to create anonymized datasets for data scientists and contractors without exposing real PII","I want to apply differential privacy to aggregate statistics so we can share insights without revealing individual records","I need to pseudonymize production data for testing without manually building transformation logic"],"best_for":["Data engineering teams building secure data sharing pipelines","Organizations sharing data with external partners or researchers","Teams balancing data utility with privacy requirements"],"limitations":["Anonymization is irreversible — cannot re-identify individuals after anonymization","Differential privacy adds noise to results, reducing statistical accuracy — utility-privacy tradeoff must be tuned per use case","Pseudonymization requires secure key storage — keys must be protected separately from pseudonymized data","Pipeline execution latency depends on dataset size and anonymization technique — can take hours for multi-TB datasets"],"requires":["Distributed compute cluster (Spark, Dask, or cloud-native compute)","Source data with clear PII fields and schema information","Anonymization technique selection (k-anonymity, l-diversity, differential privacy, etc.)","Key management infrastructure for pseudonymization keys"],"input_types":["structured datasets (CSV, Parquet, database tables)","schema definitions with PII field annotations","anonymization policy specifications"],"output_types":["anonymized datasets (Parquet, CSV, or database tables)","anonymization audit logs (which records were modified, which technique applied)","utility metrics (data loss, statistical accuracy)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_6","uri":"capability://data.processing.analysis.real.time.data.quality.and.anomaly.detection","name":"real-time data quality and anomaly detection","description":"Monitors data pipelines in real-time using statistical baselines and machine learning models to detect quality issues (missing values, schema violations, outliers) and security anomalies (unusual access patterns, data exfiltration attempts). Anomalies trigger alerts and can automatically pause pipelines to prevent propagation of bad data. Baselines are learned from historical data and adapt over time to seasonal patterns.","intents":["I need to catch data quality issues before they propagate downstream and corrupt analytics","I want to detect unusual data access patterns that might indicate a security breach","I need to automatically pause pipelines when data quality degrades below acceptable thresholds"],"best_for":["Data engineering teams managing mission-critical data pipelines","Organizations with strict data quality requirements (financial services, healthcare)","Teams needing real-time security monitoring of data access"],"limitations":["Anomaly detection requires 2-4 weeks of historical baseline data — cannot detect anomalies in new pipelines immediately","False positive rate depends on baseline quality — noisy or inconsistent data produces high false positive rates","Real-time monitoring adds 10-50ms latency per record depending on model complexity","Automated pipeline pausing can disrupt downstream consumers — requires careful alert tuning to avoid false negatives"],"requires":["Data streaming infrastructure (Kafka, Kinesis, Pub/Sub) or batch pipeline instrumentation","Historical baseline data (minimum 2-4 weeks of clean data)","Alert notification system (email, Slack, PagerDuty, etc.)","Pipeline control capability (ability to pause/resume pipelines)"],"input_types":["streaming data records (JSON, Avro, Protobuf)","schema definitions and quality rules","historical baseline data"],"output_types":["real-time anomaly alerts (JSON with anomaly type and severity)","quality metrics dashboards","anomaly investigation reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_7","uri":"capability://tool.use.integration.multi.cloud.and.hybrid.data.integration.with.unified.governance","name":"multi-cloud and hybrid data integration with unified governance","description":"Provides a unified data governance layer across heterogeneous cloud providers (AWS, Azure, GCP) and on-premises systems, enabling consistent policy enforcement regardless of where data resides. Abstracts away cloud-specific APIs and storage formats, allowing teams to define policies once and apply them everywhere. Supports data movement between clouds with automatic re-encryption and policy re-application.","intents":["I need to enforce the same security policies across AWS, Azure, and on-prem systems without learning each platform's native tools","I want to move data between cloud providers without losing encryption or access control context","I need a single pane of glass to see all sensitive data across our multi-cloud infrastructure"],"best_for":["Enterprise organizations with multi-cloud strategies","Teams managing hybrid infrastructure (cloud + on-prem)","Organizations avoiding vendor lock-in through cloud-agnostic governance"],"limitations":["Abstraction layer adds 5-15% overhead compared to cloud-native tools","Cloud-specific features (e.g., AWS Lake Formation, Azure Purview) may not be fully exposed through abstraction","Data movement between clouds incurs egress charges and network latency","Requires network connectivity to all cloud providers and on-prem systems — air-gapped environments not supported"],"requires":["Cloud provider accounts (AWS, Azure, GCP) with appropriate IAM roles","On-premises connectivity (VPN or direct connect) for hybrid scenarios","Unified policy definition framework (YAML or JSON)","Network bandwidth for cross-cloud data movement"],"input_types":["cloud provider credentials and configurations","on-premises data source connections","unified policy definitions"],"output_types":["unified governance reports across all clouds","cross-cloud data lineage and impact analysis","compliance reports aggregating evidence from all clouds"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_dataisland__cap_8","uri":"capability://data.processing.analysis.sensitive.data.discovery.and.inventory.management","name":"sensitive data discovery and inventory management","description":"Continuously scans data repositories (databases, data lakes, cloud storage) to discover and catalog sensitive data elements, building a living inventory of what sensitive data exists, where it's stored, who accesses it, and how it's protected. Uses pattern matching, ML-based classification, and metadata analysis to identify sensitive data without requiring manual tagging. Integrates with data catalogs (Collibra, Alation) to enrich existing metadata.","intents":["I need to know what sensitive data we have across all our systems without manually auditing each one","I want to track where sensitive data is stored and ensure it's properly protected","I need to identify shadow IT or undocumented data stores that might contain sensitive information"],"best_for":["Enterprise security and compliance teams","Organizations undergoing data governance initiatives","Teams managing large, heterogeneous data environments"],"limitations":["Discovery requires read access to all data repositories — may not be feasible in highly restricted environments","Scanning large data lakes (multi-PB) can take days or weeks to complete","Classification accuracy depends on data quality and format consistency","Cannot discover data in encrypted or obfuscated formats without decryption keys"],"requires":["Read access to all data repositories (databases, object storage, data warehouses)","Network connectivity to all data sources","Sufficient compute resources for scanning (can be resource-intensive)","Data catalog or metadata repository (optional but recommended)"],"input_types":["data repository connections (database URLs, S3 buckets, etc.)","classification rules and patterns","metadata from existing data catalogs"],"output_types":["sensitive data inventory (JSON or CSV with location, type, classification)","data discovery reports","integration with data catalogs"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Connectivity to data sources (S3, GCS, Azure Blob, on-prem databases via VPN)","Minimum dataset size of 100MB to train effective models","API credentials for target data platforms","Compliance framework specification (GDPR, HIPAA, PCI-DSS, etc.)","Active AWS KMS, Azure Key Vault, or GCP Cloud KMS account with appropriate IAM roles","Network connectivity to KMS endpoints (or on-prem HSM with PKCS#11 interface)","Encryption policy definition in YAML or JSON format","Audit logging infrastructure (CloudWatch, Azure Monitor, or Stackdriver)","Identity provider (Okta, Azure AD, Keycloak, or LDAP) with role/attribute sync capability","Data platform with query interception capability (Snowflake, BigQuery, Redshift, or Databricks)"],"failure_modes":["Classification accuracy depends on data quality and format consistency — unstructured text with poor formatting may produce false negatives","No real-time streaming classification — batch processing only, with latency of minutes to hours depending on dataset size","Custom classification models require labeled training data (typically 500+ examples) to achieve >95% accuracy","Limited to text-based sensitive data; image and video PII detection not mentioned in available documentation","Key management integration requires pre-configured KMS access — no built-in key generation or storage","Performance overhead of 5-15% on data throughput due to encryption/decryption operations","Automated key rotation may cause brief latency spikes during rotation windows","No support for homomorphic encryption or searchable encryption — encrypted data cannot be queried without decryption","Policy evaluation adds 50-200ms latency per query depending on policy complexity and data volume","Masking transformations are deterministic (same input always produces same masked output) — may allow inference attacks if attacker has access to multiple masked values","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.282Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=dataisland","compare_url":"https://unfragile.ai/compare?artifact=dataisland"}},"signature":"sJhtcrxyR+yG5ZBDF6jGGj95O4Fab7VEKU26XPdWzqeiKvJdEylibdY+5aK1/53Q+0yEkt8OZEzuzR5V6MKeBA==","signedAt":"2026-06-21T18:16:49.230Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dataisland","artifact":"https://unfragile.ai/dataisland","verify":"https://unfragile.ai/api/v1/verify?slug=dataisland","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"}}