They often assume productivity and infrastructure files are low risk because they are not databases or documents. In practice, those files can contain credentials, topology details, business context, or cryptographic material. If they are not parsed and classified, they become ungoverned copies of the information the organisation most needs to protect.
Why This Matters for Security Teams
Notebook, diagram, and keystore files sit in a blind spot because they are often treated as working artifacts rather than governed records. That assumption is risky. A notebook can expose API keys in cells, a diagram can reveal trust boundaries or network paths, and a keystore can contain material that directly supports authentication or signing. Under NIST SP 800-53 Rev 5 Security and Privacy Controls, the security outcome depends on controlling information lifecycle, access, and data handling, not only on classifying obvious business documents.
Teams get this wrong when they rely on file extension, storage location, or intended purpose as a proxy for sensitivity. A notebook stored in source control may be more dangerous than a formal policy document because it often contains live credentials, sample data, query results, or embedded outputs. Diagram repositories can also leak architecture details that help attackers map identity pathways, external dependencies, and privileged systems. In practice, many security teams encounter exposure only after a repository is shared, indexed, or copied into a collaboration platform, rather than through intentional classification.
How It Works in Practice
The practical issue is not just where these files live, but what they contain and how they are used. Notebook files such as Jupyter notebooks often mix executable code, narrative text, and captured output. Diagram files may embed text labels, IP ranges, account names, service endpoints, or comments that expose internal design decisions. Keystore files are even more sensitive because they can hold private keys, certificates, secrets, or references that enable cryptographic operations.
A sound approach is to treat these formats as content-rich assets that require inspection before access is granted broadly. That means discovery, parsing, and classification need to happen at ingestion and during change, not only during incident response. Security teams typically apply a layered process:
- Identify notebooks, diagram formats, and keystores across source control, file shares, object storage, and collaboration tools.
- Parse the file contents, not just the metadata, to detect secrets, keys, tokens, endpoints, and sensitive operational context.
- Classify the file by actual content and intended sharing scope, then apply retention and access rules accordingly.
- Scan for hardcoded secrets and rotate exposed credentials when notebooks or diagrams have been committed or exported.
- Apply stronger controls to keystores, including separation of duties, protected access, and audit logging for use and export.
For development and platform teams, the key lesson is that these files often bridge human-readable documentation and machine-executable operations. That makes them especially valuable to attackers who want both context and access. Guidance from OWASP on secret handling and from MITRE on adversary abuse patterns is useful here, especially where notebooks or diagrams are used in AI pipelines or automation workflows that touch credentials or signing material. These controls tend to break down when teams store mixed-purpose files in ad hoc collaboration spaces because content extraction, ownership, and retention rules are rarely enforced there.
Common Variations and Edge Cases
Tighter inspection of notebook, diagram, and keystore files often increases engineering overhead, requiring organisations to balance faster collaboration against stronger content control. That tradeoff is real, especially for data science, platform engineering, and infrastructure teams that depend on rapid iteration.
There is no universal standard for classifying every notebook cell or diagram annotation, so current guidance suggests using risk-based rules rather than trying to force a perfect taxonomy. For example, a notebook used for exploration may be low risk until it contains live credentials, production queries, or copied customer data. A diagram exported for a presentation may be harmless in one context and highly sensitive in another if it exposes identity flows, trust boundaries, or incident response paths. Keystore handling is even less forgiving: if private keys or signing material are present, the file should be treated as highly sensitive regardless of its filename or repository.
Teams should also be careful with derived files. Exported PDFs, rendered images, cached previews, and sync copies can preserve sensitive content even after the original file is edited. Where notebooks are used with AI tooling, prompt history, outputs, and embedded execution traces can become part of the exposure surface. The practical takeaway is to govern the artifact and its derivatives together, not as isolated files.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and MITRE ATLAS address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.DS | These files often contain sensitive data or secrets that need protection in storage and transit. |
| OWASP Non-Human Identity Top 10 | Keystore and embedded secrets directly affect non-human identity and machine access governance. | |
| NIST AI RMF | GOVERN | AI-related notebooks and artifacts need governance over content, provenance, and downstream use. |
| MITRE ATLAS | AML.TA0002 | Notebook and pipeline files can be abused to leak secrets or manipulate AI workflows. |
| NIST SP 800-53 Rev 5 | MP-6 | Media sanitization and content handling matter when files are copied, exported, or retired. |
Define ownership and review rules for AI notebooks before they reach shared or automated environments.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 19, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
