Topic starter
23/03/2026 2:24 pm
TL;DR: AI-native DSPM, now integrated with Cohesity recovery workflows, is meant to close a visibility gap that leaves 60% of enterprises unable to see at least half their data estate, making recovery decisions harder as AI spreads sensitive data across cloud, SaaS, and hybrid environments, according to Cyera. The governing problem is no longer just where data sits, but whether organisations can classify and prioritise it fast enough to restore what matters most.
Editorial analysis by NHI Mgmt Group, based on content published by Cyera: “Cyera + Cohesity: Data Security Meets Cyber Resilience”.
By the numbers:
- Cyera says its classification approach delivers 95%+ accuracy across structured and unstructured data.
Key questions
Q: How should teams prioritise recovery when sensitive data is scattered across hybrid estates?
A: Teams should prioritise restoration by sensitivity, compliance exposure, and business criticality, not by whichever system happens to be easiest to recover.
Q: Why does poor data visibility make ransomware recovery harder?
A: Poor visibility slows recovery because teams cannot quickly identify what was exposed, what is business-critical, or what carries regulatory risk.
Q: What breaks when classification is not connected to cyber recovery workflows?
A: Recovery breaks at the decision layer.
Practitioner guidance
- Map sensitive data propagation across recovery estates Inventory where sensitive data is duplicated across cloud, SaaS, on-prem, and backups so recovery teams can see the full blast radius before an incident forces decisions.
- Tie classification to restore order Update recovery runbooks so restoration priority is driven by sensitivity, compliance impact, and business criticality instead of storage location or backup age.
- Reduce ROT data before incidents expose it Identify redundant, obsolete, and trivial data in backup and production estates, then remove it to reduce exposure scope and lower storage overhead.
Bottom line: Cyera's analysis ties recovery quality to data visibility, showing that enterprises cannot restore confidently when sensitive data is scattered across hybrid estates and backups without reliable classification.
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This topic was modified 4 days ago by NHI Mgmt Group
08/10/2026 6:08 pm
Recovery without data context is a governance failure, not a storage failure. The article's core issue is that organisations cannot recover confidently when they do not know what they are recovering. That is an identity and governance problem because access, sensitivity, and restoration priority all depend on trustworthy data intelligence. The practitioner implication is that recovery workflows now need classification state as an input, not an afterthought.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: What should security, IAM, and resilience teams be accountable for in data recovery?
A: They should be accountable for making sure recovery decisions reflect current data sensitivity, access context, and regulatory impact. That means governance teams define the rules, identity teams understand who can reach the data, and resilience teams execute restores against that context. Shared accountability is essential when data context drives incident outcomes.
👉 Read our full editorial: AI-native DSPM closes the data visibility gap in cyber recovery