What Is Data-Centric Zero Trust?
Definition: Data-centric zero trust is a security approach that applies Zero Trust principles directly to data by enforcing protection at the data layer itself — regardless of where the data is stored, shared, or used.
Unlike network- or identity-centric Zero Trust models, data-centric zero trust ensures sensitive information remains protected even after it leaves trusted systems and environments.
Why Data-Centric Zero Trust Exists
Zero Trust was created to address a perimeterless world.
But most Zero Trust implementations stop at the network or identity layer.
Modern data:
Moves freely across SaaS, cloud, endpoints, and third parties
is copied, shared, and transformed continuously
Is consumed by AI systems that operate outside traditional trust boundaries
Once data leaves the network or application boundary, traditional Zero Trust controls no longer apply.
Data-centric zero trust exists to close that gap.
What Data-Centric Zero Trust Solves
Data-centric zero trust enables organizations to:
Enforce Zero Trust controls that persist beyond networks and identities
Protect sensitive data during sharing, collaboration, and third-party acces
Prevent data exposure when files are copied or moved
Maintain protection when data enters AI workflows
Reduce reliance on implicit trust in users, devices, or applications
Trust is never assumed — and protection never drops.
What Most Organizations Get Wrong About Zero Trust
Many Zero Trust strategies fail because they equate Zero Trust with access control.
Common misconceptions include:
ZTNA is Zero Trust
ZTNA controls access to applications, not data.
IAM enforces Zero Trust
Identity controls do not persist once data is accessed.
Revoking access removes risk
Data can still exist in copies, exports, or AI systems.
Zero Trust at the network or identity layer does not protect data once it is in motion.
ZTNA: Secures access paths, not the data itself
IAM: Controls who can log in, not how data is used.
DLP: Focused on exfiltration, not persistent protection
DSPM: Identifies risk but does not enforce controls
IRM/DRM: Breaks when data leaves the application
Data-Centric Zero Trust
vs Common Approaches
Data-centric zero trust enforces protection within the data, not around it.
How Confidencial Defines
Data-Centric Zero Trust
Selective, object-level encryption
Protection that does not depend on network location or application context
Policy enforcement that travels with the data
Auditable access and usage at a granular level
Confidencial defines data-centric zero trust as embedding enforceable, cryptographic protection directly into sensitive data so Zero Trust controls persist across systems, users, and workflows — including AI.
This approach enables:
Zero Trust becomes a property of the data, not the environment.
Why Data-Centric Zero Trust Matters for AI
AI systems dissolve traditional trust boundaries.​
Data is:
• Ingested
•Transformed
• Embedded
• Reused
Identity-based Zero Trust does not apply one data is processed by AI
With data-centric zero trust:
Sensitive data remains protected before AI ingestion
Protection persists across RAG pipelines and vector databases
AI systems can operate on non-sensitive context
Organizations reduce irreversible exposure
Engineered for control. Architected for precision.
AI adoption without data-centric zero trust is not Zero Trust at all.
Where Data-Centric Zero Trust is Applied
Data-centric zero trust is required wherever sensitive data must remain protected beyond initial access:
Unstructured documents shared internally or externally
Collaboration platforms and SaaS applications
Third-party data exchange
AI training, fine-tuning, and inference
Hybrid and multi-cloud environments