Sensitive Data Platform (SDP)

Spirion Sensitive Data Platform is a data discovery and classification tool designed to locate, categorize, and remediate sensitive data across on-premises and cloud infrastructure.

Reviewed by 7wData

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Publisher review

Spirion Sensitive Data Platform is a data discovery and classification tool designed to locate, categorize, and remediate sensitive data across on-premises and cloud infrastructure. Acquired by archTIS in 2025, SDP operates as a hybrid-first platform supporting structured and unstructured data across file shares, cloud repositories (S3, Azure, AWS), databases, and endpoints. The platform claims 98% accuracy in detecting personally identifiable information (PII), protected health information (PHI), financial data, and other regulated content using context-aware detection rather than rule-based pattern matching.

SDP addresses a core problem in data governance: most organizations lack comprehensive visibility into where sensitive data resides, creating compliance gaps under GDPR, CCPA, HIPAA, and PCI-DSS. The platform scans differential changes rather than full inventories, reducing computational overhead. It includes automated remediation workflows (encryption, masking, deletion, quarantine), persistent classification tags embedded directly in files, and executive dashboards that translate data risk into financial metrics.

A notable architectural difference from competitors like Varonis is SDP's emphasis on contextual intelligence to reduce false positives—reviewers report this is a genuine strength, though reporting capabilities trail some alternatives. The product integrates with third-party DLP solutions (Thales, Seclore, Atakama) and Microsoft's rights management ecosystem. Deployment is cloud-native (Azure primary, AWS and on-premises planned), which constrains on-premises users awaiting native installation support.

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How it works

  1. Differential Scanning

    Scans only changed data rather than full inventories, reducing scanning time and computational cost while maintaining discovery velocity.

  2. Context-Aware Classification

    Uses pattern matching and semantic analysis beyond regex and data matching to classify sensitive content with 98% accuracy, reducing manual triage.

  3. Automated Remediation Playbooks

    Executes pre-defined workflows to mask, encrypt, quarantine, or delete sensitive data at scale without manual intervention.

  4. Data Asset Inventory (DAI)

    Maps data flows across the environment, tracks ownership and security postures, and catalogs sensitive data locations.

  5. Persistent Classification Metadata

    Embeds classification tags directly into files, enabling consistent policy enforcement across tools and preventing reclassification drift.

  6. Executive Risk Dashboards

    SPIglass™ dashboard translates data risk findings into financial impact metrics for board-level reporting and budget justification.

  7. Subject Rights Request Automation

    Sensitive Data Finder module automates fulfillment of DSAR/GDPR requests by locating and collecting all personal data related to a subject.

Strengths and trade-offs

Strengths

  • High accuracy (98%) in sensitive data detection with context-aware intelligence reduces false positives compared to rule-based alternatives.
  • Persistent classification metadata embedded in files survives tool and system migrations, preventing reclassification churn.
  • Automated remediation at scale (masking, encryption, deletion) minimizes manual effort for teams managing large data inventories.

Trade-offs

  • Lacks native data protection—requires pairing with DLP solutions, increasing tooling complexity and cost for organizations seeking end-to-end coverage.
  • Cloud-native architecture (Azure-primary) limits on-premises deployment options; AWS and private cloud support remain in roadmap, not yet available.
  • Reporting capabilities lag competitors; users report intuitive dashboards for discovery but limited customization and export options for compliance auditing.

Pricing context

Spirion does not publish list pricing for Sensitive Data Platform. Pricing is customized per organization based on deployment model, data volume, scanning frequency, and feature tier. The platform operates as a SaaS subscription on Microsoft Azure with consumption-based and capacity-based pricing models.

Organizations typically request quotes directly from archTIS or through resellers. No public information exists on free tiers or trial periods.

User reviews

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.spirion.com — Product overview, deployment options, use cases, and core features including Data Asset Inventory and executive dashboards.
  2. docs.spirion.com — Technical documentation covering discovery, classification, scanning capabilities, agent deployment, and API functionality.
  3. www.g2.com — Comparative analysis with Varonis; user feedback on contextual detection accuracy and remediation automation strengths and reporting limitations.
  4. www.comparitech.com — Review of strengths (detection accuracy, automation), weaknesses (learning curve, performance on large datasets, lack of native protection), and architectural gaps.
  5. www.spirion.com — Company history: Spirion founded in 2006, acquired by Australian public company archTIS for $15.7M in October 2025.
  6. www.cyberhaven.com — Competitive landscape analysis positioning SDP, Varonis, Cyera, and Strac in 2026 market; differentiators in accuracy and cloud-native architecture.