Your product may process sensitive human context. When regulators, auditors, or counsel ask what was expressed, what was interpreted, and what policy applied — can you produce an auditable record?
EU AI Act Article 5(1)(f) restricts certain forms of emotion inference in workplace and education contexts (Recital 44 notes biometric concerns)
Without provenance, emotional attributions can be difficult to defend under scrutiny
GRC reviews slow or block deals when emotional-context handling is opaque
How your system handles emotional data determines your regulatory positioning.
DeepaData turns sensitive human context into a governed receipt — not a black-box memory. Interpretation is what enables auditability. By structuring expressed or interpreted content into a governed artifact, the emotional context becomes inspectable, portable, and cryptographically sealed. Without interpretation, emotional context remains inside the model — transient, non-auditable, and often lost after the session. The artifact exists, or it doesn't.
| Dimension | Interpretation | Inference |
|---|---|---|
| Source | Voluntary text and transcription | Biometric and behavioral signals |
| User awareness | Consciously communicated | May be unaware |
| Verifiability | Traceable to source | Derived / opaque |
| EU AI Act 5(1)(f) | Generally lower-risk when traceable to explicit expression (fact-pattern dependent) | Higher scrutiny / restricted in some contexts (incl. workplace/education) |
Governance infrastructure to support audit, review, and safe handling of emotional context. Every emotional interpretation becomes a governed, verifiable record.
Every emotional attribution traces to explicit source content, model version, and timestamp
W3C Data Integrity Proofs ensure records are tamper-evident and audit-ready
Consent basis, jurisdiction, retention policy attached to every artifact
Complete accountability for every interpretation, decision, and policy outcome
| Scenario | Without Governance | With DeepaData |
|---|---|---|
| Regulator inquiry | Reconstruct from logs | Evidence-grade records ready |
| Client dispute | Word vs. word | Source-attributed provenance |
| Enterprise due diligence | Deal blocked by risk committee | GRC-ready documentation |
| Data subject request | Manual data extraction | Portable, consent-governed records |
Traditional model-risk tooling explains model mechanics — why did the model output X? DeepaData explains model meaning — what did the AI imply about a person, and can you prove it?
See how DeepaData governance works with your existing AI infrastructure.