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19/08/2026 3:57 pm
TL;DR: Specialized file formats such as DICOM, EDI, Tableau extracts, pickle files, OneNote notebooks, Draw.io diagrams, Java KeyStores, and LST catalogs often bypass traditional DLP and DSPM coverage because tools treat them as opaque blobs, according to Sentra. That gap leaves regulated data, secrets, and shadow copies hidden in the files teams use every day, so file-format parsing is becoming a governance requirement, not a niche feature.
NHIMG editorial — based on content published by Sentra: specialized file format scanning for DSPM and data governance
Questions worth separating out
Q: How should security teams govern specialised file formats in DSPM programmes?
A: Security teams should inventory the file types that actually carry regulated or sensitive content, then require parsing that exposes the data inside them.
Q: Why do opaque file formats create so much risk for data security?
A: Because many formats hide sensitive content inside containers that conventional scanners do not understand.
Q: What do teams get wrong about notebook, diagram, and keystore files?
A: They often assume productivity and infrastructure files are low risk because they are not databases or documents.
Practitioner guidance
- Expand discovery to specialised formats Add DICOM, EDI, Tableau extracts, pickle/joblib, OneNote, Draw.io, JKS, and LST to the same discovery scope as databases and shared files so high-risk content is not excluded by file type.
- Classify embedded content, not just containers Require content-aware parsing that extracts fields, labels, tables, and metadata from each supported format so policy can target PHI, PII, secrets, and sensitive business records directly.
- Review access to shadow copies and exports Map where extracted files move across cloud buckets, file shares, collaboration tools, and analytics workflows, then remove broad access that was granted because the file looked non-sensitive.
What's in the full article
Sentra's full blog post covers the operational detail this post intentionally leaves for the source:
- Format-by-format extraction behaviour for DICOM, EDI, Tableau extracts, pickle/joblib, OneNote, Draw.io, JKS, and LST
- Examples of how parsed fields map to PHI, PII, PCI, and secrets classifications in practice
- Coverage details for storage locations such as S3, Azure Blob, GCS, file shares, and SaaS environments
- How the extraction engine feeds DSPM policies across multiple file types without manual triage
👉 Read Sentra's analysis of specialised file format scanning for DSPM coverage gaps →
Specialized file formats in DSPM: where visibility still breaks down?
Explore further
19/08/2026 4:35 pm
Opaque file formats are a data-governance failure, not just a parsing problem. When DICOM, EDI, Tableau extracts, and serialized ML artifacts sit outside normal discovery, teams lose the ability to enforce classification at the point of use. That weakens DSPM, but it also weakens identity governance because access decisions are being made without knowing what the file actually contains. Practitioners should treat format support as a control requirement, not a product convenience.
A question worth separating out:
Q: What should organisations do before sensitive files spread across cloud and SaaS tools?
A: They should identify where specialized file types are created, exported, synced, and archived, then apply the same classification and access review discipline used for core data stores. That reduces the chance that regulated content or secrets drift into locations where broader groups can reach them without oversight.
👉 Read our full editorial: Specialized file formats expose the biggest DSPM visibility gap
