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18/08/2026 2:36 pm
TL;DR: AI data classification programs still over-focus on PII, while AI-ready sensitivity also includes proprietary IP, training data, and access tokens embedded in files and transcripts, according to Sentra. Classification that cannot understand context, not just patterns, leaves the highest-risk material invisible to AI systems and the controls that depend on it.
NHIMG editorial — based on content published by Sentra: The AI Data Readiness Audit, Part 2, Classifying What Matters
Questions worth separating out
Q: What breaks when AI data classification only looks for PII?
A: PII-only classification misses the content that now drives the highest AI risk, including proprietary IP, training data, and secrets hidden in files or logs.
Q: Why do access tokens and API keys need to be classified as sensitive data?
A: Because they are not just information, they are authority.
Q: How can teams tell whether data classification is actually working?
A: Look for measurable evidence that labels match reality across different data types, locations, and business contexts.
Practitioner guidance
- Expand the sensitivity taxonomy Add proprietary IP, model-training datasets, and secrets or tokens as explicit categories in the classification model so AI risk does not default to a PII-only framework.
- Classify by content semantics Use controls that evaluate meaning across structured tables, documents, images, audio, and transcripts rather than relying only on regex, keywords, or file location.
- Link classification to identity controls Route any discovered secrets, tokens, or service credentials into access review, rotation, and offboarding workflows so classification findings change authority, not just labels.
What's in the full article
Sentra's full blog post covers the operational detail this post intentionally leaves for the source:
- Detailed Day 8 classification workflow for extending sensitivity taxonomy across AI data types
- Operational examples of schema analysis, embeddings, OCR, and speech-to-text in classification pipelines
- Validation guidance for measuring false positives and false negatives against real enterprise data
- Part 3 preview on least-privilege access enforcement for AI systems and what comes next
👉 Read Sentra's analysis of AI data classification for sensitive content and AI readiness →
AI data classification and access tokens: what teams miss?
Explore further
18/08/2026 3:21 pm
PII-first data classification is now an incomplete control model. AI programmes handle content that does not fit legacy privacy-first taxonomies, and the governance failure is not subtle. Proprietary IP, training data, and embedded secrets can all be sensitive even when no regulated personal data is present. Organisations that keep classification anchored only to PII create blind spots exactly where AI systems are most likely to operate.
A question worth separating out:
Q: Should security teams treat AI data classification and secrets management separately?
A: No. They overlap whenever credentials, tokens, or service accounts appear inside AI-accessible content. Classification tells you where the sensitive artefact is, while secrets management determines who can use it and for how long. Splitting them creates a gap between discovery and control that AI workflows will exploit.
👉 Read our full editorial: AI data classification is missing the real sensitive assets
