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Enterprise File Governance for Federal Agencies

Federal agencies and defense organizations generate vast volumes of unstructured data: documents, PDFs, emails, contracts, scanned images, and nested archives. These files hold mission-critical intelligence and regulated content. Yet the tools meant to govern them never open them. Catalogs index metadata. DLP watches file movement. SIEMs log access. None of them read what is inside.

That gap is where AI modernization stalls, compliance gaps hide, and security exposure compounds. The DoD Zero Trust Strategy requires classification and policy enforcement at the file level, inside the enclave, at mission scale. Not metadata sampling. Not perimeter inspection. Full semantic awareness of what is in every file.

Ohalo built DataXray to close this gap. The platform opens every file, reads the contents, and classifies what is sensitive, regulated, or mission-relevant. Machine-readable tags flow into the governance, security, and AI tools already in place. No file leaves your environment. DataXray is trusted by defense agencies, intelligence community organizations, and Fortune 500 financial institutions.

Product/Solution: DataXray - Enterprise File Governance Platform
DataXray discovers and classifies unstructured data at petabyte scale using a three-layer engine: NLP/ML annotators for token-level precision, GenAI/LLMs for document-level context, and agency-defined rules for mission-specific logic. It identifies CUI, PII, PHI, ITAR, and ACCM-designated content across on-premises, air-gapped, hybrid, and multi-cloud file stores.

DataXray deploys on containerized RHEL/Podman infrastructure, agentless, and is operational in hours. The platform scans at 100,000+ words per second across environments, connecting to 55+ native connectors and 100+ upstream data sources with no external connectivity, no cloud dependency, and no licensing callbacks.

Classification outputs feed into existing data catalogs, DLP, DRM, SIEM/SOAR, and AI pipelines through native integrations with Collibra, Ping Identity, Virtru, Splunk, Trellix, and Microsoft Purview. Every scan produces structured, SIEM-ingestible audit logs with sensitivity and entitlement metadata for continuous cyber enforcement.

Compliance and certifications:

  • STIG-compliant. ATO artifacts provided. Letter of Attestation from independent auditor. Requirements Traceability Matrix for accelerated ATO.
  • ISO 27001 and SOC 2 Type II certified.
  • Pre-built annotator packs for CMMC/CUI, HIPAA, GDPR, CCPA/CPRA, and PCI-DSS.
  • Supports DoD Zero Trust Data Pillar across all seven capability groups, including ABAC-driven file-level enforcement.

FAQ

How do you prepare unstructured data for AI?

Discover where files live, classify them for sensitivity at the document level, remove or redact risky/ROT content, and apply retention and access policy before the data is embedded or indexed by a RAG/LLM pipeline.

Where does data classification fit in a RAG pipeline?

Before the embed/index step. Classifying and gating files up front means the vector store only contains permitted, current content, and retrieval can honour sensitivity labels.

Why not just let the AI read everything?

An AI system with broad access surfaces whatever it can reach, including PII, regulated, or out-of-policy files. Governing the data first is what makes AI use safe and defensible.

What does DataXRay do to make data AI-ready?

It discovers files across 1,000+ sources, classifies them with a three-layer (NLP/ML + GenAI + custom-rule) engine, redacts sensitive content, and governs which files are eligible for AI, enriching metadata for downstream RAG use.

Which sources does it support?

Connectors across SharePoint, cloud storage, databases, and legacy file shares, for both unstructured and structured data.

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