Structured data lives in defined fields and records, while unstructured data sits in notes, files, chats, and attachments. In Salesforce environments, both can contain regulated information, so governance must classify content across formats rather than assuming the risk is limited to obvious database fields.
Expanded Definition
Structured and unstructured data describes two broad content classes that security teams must govern together, not separately. Structured data is organized into predictable fields, rows, and schemas, such as account records, API inventory tables, or entitlement exports. Unstructured data includes free-form content like emails, chat transcripts, file attachments, tickets, screenshots, and generated documents. In NHI and IAM environments, both can carry secrets, identifiers, access instructions, or regulated customer data, so classification cannot stop at the database layer.
Definitions vary across vendors when the term is applied to AI systems, because some platforms treat documents as semi-structured once metadata is extracted. For security governance, the useful boundary is practical rather than theoretical: if content can expose credentials, privileges, or sensitive context, it belongs in the same control scope. That matters because search, indexing, DLP, and retention rules often behave differently across repositories. The NIST Cybersecurity Framework 2.0 reinforces the need to identify and protect information assets across their full lifecycle, regardless of format.
The most common misapplication is assuming that only structured database fields require classification, which occurs when teams ignore notes, attachments, and collaboration tools that carry the same sensitive context.
Examples and Use Cases
Implementing classification rigorously across structured and unstructured data often introduces more tagging, review, and workflow overhead, requiring organisations to weigh broader visibility against operational friction.
- A Salesforce account object stores a service account owner and rotation date, while the related case notes contain copied API keys that also require secret handling.
- A structured entitlement export shows which agent can call a production tool, while an attached spreadsheet in a ticket reveals the same access path in plain text.
- Chat logs in a collaboration platform contain reset instructions for an NHI credential, making the transcript itself a sensitive artifact even though it is not a database field.
- Policy engines classify contract PDFs and uploaded screenshots for embedded secrets or customer identifiers before indexing them into search.
- Teams map file shares and document repositories to the same retention and access review rules used for structured CRM records, because both can contain regulated data.
For a deeper NHI context, the Ultimate Guide to NHIs — Key Research and Survey Results shows how often secrets and service-account risk appear outside intended control points. Standards-oriented teams often pair that operational view with the NIST Cybersecurity Framework 2.0 to ensure classification supports detection and protection workflows.
Why It Matters in NHI Security
Structured and unstructured data becomes a security issue when identity-related content is scattered across repositories that were never designed to enforce the same controls. A service account token in a CRM note can be as dangerous as one in a table column, but it is far less likely to be reviewed, rotated, or revoked on schedule. That is why content governance, not just access control, is central to NHI security.
NHIMG research shows that 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools, which is a strong indicator that sensitive material frequently escapes the boundaries of structured systems. In practice, the risk extends to unstructured files, exports, and collaboration artifacts that are easy to overlook during audits. The governance question is not whether data is neatly formatted, but whether it can reveal access, privilege, or regulated information. Organisations that classify only obvious fields tend to discover the real exposure after a breach report, at which point data format handling becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RM-01 | Risk management depends on identifying sensitive data across all formats, not only database fields. |
| NIST AI RMF | AI governance covers training and prompt data that may be structured, unstructured, or both. | |
| OWASP Agentic AI Top 10 | Agentic systems often consume documents, chats, and records that mix structured and unstructured sensitive content. |
Classify structured and unstructured content together so risk decisions cover every repository that stores sensitive data.
Related resources from NHI Mgmt Group
- How should security teams govern AI classification for unstructured data?
- How should security teams implement automated data classification for unstructured data?
- How should security teams govern unstructured data for GenAI use cases?
- Why do traditional data classification tools fail on structured records?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
