Dataisland

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.

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.

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.

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.

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.

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.

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.

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.

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.