Data Governance. VAULTIS-aligned. Audit-ready by default.
The same DHA-tested framework, visible catalogs, federated stewardship, contract-enforced lineage, and PHI/PII classification, applied to whichever portfolio you steward. Healthcare-deep, federally hardened, and built so the auditor never needs a tour to find the receipts.
InterScripts builds federal data governance and metadata frameworks aligned to VAULTIS: visible catalogs, federated stewardship, contract-enforced lineage and PHI/PII classification. The same DHA-tested framework is applied to federal and state agency portfolios, so auditors can find the evidence without a tour. InterScripts is a Minority-Owned, DBE-certified company.
Framework
VAULTIS-aligned governance pillars
Enterprise metadata catalog, searchable, governed, alive.
Automated metadata harvesting across structured and unstructured sources. ML/NLP classification engines retrained monthly. A Python SDK and REST APIs so developers and stewards consume the same source of truth.
- ETL-aware crawlers for Oracle, SQL Server, Postgres, lakehouse
- NLP classification of PHI/PII at table, column, and document grain
- Steward workbench with approval workflow integration
- Federated catalog topology, never a single point of failure
Federated governance that scales beyond a council.
Tri-steward model, domain steward, technical steward, and security steward, with ITSM-integrated approval flows. Approval cycle time falls from 10+ days to 48 hours because the workflow lives where the work happens.
- Tri-steward review boards mapped to data domains
- ITSM-integrated approval workflows (ServiceNow, Jira, Remedy)
- Machine-readable data contracts enforced at the API gateway
- Quarterly stewardship QBR with measurable adoption metrics
Every field. Every transform. Every dashboard cell.
End-to-end lineage from ingestion through transformation to dashboard. SCD-2 history is on by default. Data quality rules execute on every load, and on every dashboard the executives see.
- Column-level lineage across dbt, Spark, and stored procs
- Data contracts: schema, semantics, freshness, and access policy
- Quality scorecards by domain, readiness, completeness, conformity
- Catalog completeness >98% current; metadata latency <24h
DoD VAULTIS, delivered, not slogan-cited.
Every InterScripts data engagement maps explicitly to the seven VAULTIS principles. Each control has an owner, a test, and a piece of evidence, so the auditor doesn't need a tour to find the receipts.
- Visible: catalog discovery for every authorized role
- Accessible: RBAC/ABAC bound to data tags and mission context
- Understandable: plain-language definitions and units of measure
- Linked / Trusted / Interoperable / Secure: standards-first design
Outcomes
The numbers that survive a CO briefing.
90%
HIPAA audit-finding reduction
<24h
Data discovery (from 3 weeks)
94%
ML classification accuracy
40%
Billing error reduction
75K+
Metadata elements catalogued
4 wk
Time-to-publish new product
Compliance-Built Posture
Frameworks We Operate Against
HIPAA. NIST SP 800-53. DoD RMF. ISO 11179. HITRUST r2. SOC 2 Type II. Not aspirational, operational.
What do agencies ask about data governance?
What is the VAULTIS data framework?
VAULTIS is the DoD data strategy framework: Visible, Accessible, Understandable, Linked, Trustworthy, Interoperable, Secure. It is the design baseline for federal data-governance programs and is used by InterScripts as the reference architecture for BytePad-based archival and federal data fabric implementations.
How does InterScripts approach federal data governance?
Four pillars: Catalog (automated metadata harvest, NLP-classified PHI/PII), Steward (workflow, lineage, approvals), Quality (rules, scoring, exception management), and Activate (APIs, search, AI/ML enablement). Built on a federated topology to avoid single points of failure.
Does BytePad fit into a federal data-governance program?
Yes. BytePad provides the governed archival corpus, immutable audit log, and chain-of-custody required for FOIA, OIG, and legal-hold workflows. Metadata flows into the agency catalog; access is policy-governed; retrievals are recorded.
Is PHI / PII classification automated?
Yes. ML / NLP classification operates at table, column, and document grain. Models are retrained monthly against agency feedback. Steward workflows handle exceptions and review.