AI-native DSPM closes the data visibility gap in cyber recovery

At a glance

What this is: Cyera’s partnership update with Cohesity focuses on AI-native DSPM inside cyber recovery workflows, with the central finding that data visibility is now a recovery control as much as a security control.

Why it matters: For IAM and security teams, this matters because data governance, identity governance, and recovery planning now intersect across humans, service accounts, and AI-driven workflows that move sensitive data across many systems.

By the numbers:

• 60% of enterprises lack visibility into at least half of their data estate.
• 34% of organizations hit by ransomware said better data classification and protection would have made the biggest difference.
• 95%+ classification accuracy.

👉 Read Cyera's analysis of AI-native DSPM in cyber resilience workflows

Context

AI-native DSPM combines data discovery, classification, and context so teams can find sensitive information across cloud, SaaS, on-prem, and backup environments. In this post, the key problem is not storage alone but the governance gap that appears when organisations cannot tell what data exists, where it lives, or which copy matters most during recovery.

That gap matters because ransomware response and cyber resilience both depend on knowing what is business-critical before the incident. For teams managing human access, service accounts, and AI-assisted workflows, visibility into the data estate becomes part of access governance, recovery prioritisation, and compliance readiness.

Key questions

Q: How should security teams prioritise restoration after a ransomware event?

A: They should restore first by business criticality and data sensitivity, not by storage location or convenience. The goal is to bring back the most important datasets while avoiding unnecessary re-exposure of compromised material. That requires current classification, known authoritative copies, and restore runbooks that reflect risk, compliance, and operational dependency.

Q: Why does data visibility matter to IAM and governance teams?

A: Because identity controls are only part of the picture when sensitive data is distributed across cloud, SaaS, backups, and AI-driven workflows. If teams cannot see where the data lives, they cannot judge who should access it, which copies are safe, or how recovery should be prioritised after an incident.

Q: How do organisations know whether DSPM is actually improving resilience?

A: Look for measurable reductions in unknown data locations, faster identification of sensitive datasets, and restore decisions that use classification instead of manual triage. If recovery teams still debate what to restore first during incidents, DSPM has not yet become an operational control.

Q: What is the difference between data classification and backup protection?

A: Data classification tells you what the data is, how sensitive it is, and how it should be handled. Backup protection tells you whether copies are available and recoverable. You need both, because a recoverable backup is still a liability if teams cannot identify what it contains or whether it should be restored first.

How it works in practice

AI-native DSPM across hybrid data estates

AI-native DSPM combines discovery, classification, and policy context across structured and unstructured data without requiring every source to be managed manually. The mechanism matters because modern estates span cloud, SaaS, on-prem systems, backups, and increasingly AI-enabled workflows. Traditional controls often fragment at those boundaries, leaving teams with incomplete inventories and stale sensitivity labels. DSPM closes that gap by continuously mapping where data lives and how it is exposed, so security teams can relate data value to protection state instead of treating all storage equally.

Practical implication: use continuous discovery and classification to build a current inventory before you rely on recovery or compliance decisions.

Why recovery workflows need data classification

Recovery is not just a restore operation. It is a prioritisation decision that depends on understanding which datasets are sensitive, which are business-critical, and which copies are safe to bring back first. When classification is absent, incident response teams often restore based on storage location or operational convenience rather than risk. That creates avoidable exposure, delays, and compliance uncertainty. Integrating classification into recovery workflows lets teams filter backups, flag compromised assets, and restore in a controlled order that matches business and regulatory priorities.

Practical implication: tie restoration runbooks to sensitivity labels so restore order follows business impact instead of guesswork.

Agentless governance for data security and cyber resilience

Agentless data discovery reduces deployment friction because it can inspect data sources without installing heavy collectors or modifying each workload. In large hybrid environments, that matters because coverage gaps often appear where tools are hardest to deploy. The architectural advantage is not just speed. It is the ability to keep discovery and governance aligned as environments change, including shared storage, AI copilots, and machine-generated data. This makes data governance more operational and less dependent on periodic point-in-time projects.

Practical implication: prefer agentless coverage where possible to reduce blind spots in fast-changing environments.

NHI Mgmt Group analysis

Data visibility has become a recovery prerequisite, not just a security metric. When 60% of enterprises cannot see at least half of their data estate, incident response starts from uncertainty rather than control. That is not merely a tooling gap. It is a governance failure that affects ransomware triage, compliance scoping, and restore order. The practitioner conclusion is simple: recovery planning is only as strong as data visibility at the moment of failure.

AI-native data classification creates the missing link between storage, exposure, and business impact. Enterprises increasingly move data through cloud, SaaS, backups, and AI-enabled workflows faster than manual governance can track. Classification gives that movement meaning by attaching sensitivity and priority to otherwise opaque copies and replicas. The practitioner conclusion is that data security posture and cyber resilience can no longer be managed as separate programmes.

Cyber resilience without data intelligence produces the wrong restore decisions. Omdia’s finding that 34% of ransomware-hit organisations wanted better classification and protection shows that post-incident regret often centers on not knowing what mattered most. The issue is not simply slower response. It is restoring the wrong things first, or restoring exposed data without understanding the regulatory consequences. The practitioner conclusion is to treat data intelligence as a core recovery input.

Data blast radius: the portion of an estate whose exposure, misuse, or recovery impact is amplified because sensitive data is spread across too many systems and copies. In hybrid and AI-heavy environments, the blast radius grows when teams cannot identify authoritative copies or classify data consistently. The practitioner conclusion is that blast-radius reduction begins with visibility, not just backup tooling.

Agentic data movement intensifies governance pressure across identity and recovery programmes. As AI copilots and automated workflows create and move data at speed, the old assumption that teams can classify and secure data after the fact no longer holds. Visibility must keep pace with how data is generated, copied, and restored. The practitioner conclusion is to align DSPM, IAM, and recovery planning before AI-driven sprawl widens further.

From our research:

• 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
• Only 13% of organisations feel extremely prepared for the reality of agentic AI, which shows how far governance maturity still lags adoption.
• That gap makes the NHI Lifecycle Management Guide a useful next step for teams aligning identity, lifecycle, and recovery controls.

What this signals

Data classification is becoming a resilience signal, not just a compliance artifact. If teams cannot identify sensitive data quickly, they will also struggle to restore the right systems in the right order after ransomware or destructive incidents. That is why recovery planning, DSPM, and governance reporting need to share the same data context.

With 70% of organisations already granting AI systems more access than they would give a human employee performing the exact same job, per the 2026 Infrastructure Identity Survey, the operational risk is no longer limited to stolen credentials. AI-driven data movement expands the number of places where sensitive information can be exposed, copied, or restored incorrectly.

Identity blast radius: the practical reach of a human, service account, or AI workflow once it can create, move, or restore sensitive data across many environments. As that blast radius grows, teams need joined-up visibility from IAM, DSPM, and recovery tooling rather than separate dashboards for each discipline.

For practitioners

• Map sensitive data across every recovery tier Build a current inventory across cloud, SaaS, on-prem, and backup systems so restore decisions are based on sensitivity and business criticality.
• Embed classification into restore runbooks Prioritise restoration order by data sensitivity, business impact, and regulatory exposure instead of restoring by storage location.
• Use agentless discovery to close blind spots Deploy coverage where operational friction is highest, especially in hybrid estates where agent-based tooling leaves gaps.
• Align IAM, DSPM, and resilience teams Treat data visibility as part of access governance so identity controls and recovery workflows use the same data context.

Key takeaways

• AI-native DSPM matters because recovery fails when teams cannot see what data exists, where it lives, or which copy is authoritative.
• The evidence points to a real operating gap: most enterprises still lack full visibility into their data estate, and many ransomware victims say classification would have changed the outcome.
• Practitioners should treat data intelligence as part of cyber resilience, not a separate compliance project, and align it with identity governance and restore planning.

Key terms

• Data Security Posture Management: Data Security Posture Management is the practice of finding, classifying, and monitoring sensitive data across an estate so organisations can reduce exposure and prove control. In complex environments it turns data from an unknown asset into a governed object with location, sensitivity, and risk context.
• Cyber Resilience: Cyber resilience is the ability to continue operating, recover, and make safe decisions during and after a cyber incident. It goes beyond backup availability by combining visibility, prioritisation, and restoration discipline so the organisation can restore what matters without amplifying harm.
• Data Blast Radius: Data blast radius is the spread of impact that occurs when sensitive information is copied, shared, or restored across too many systems. The wider the blast radius, the harder it is to determine what was exposed, what is authoritative, and what should be recovered first.
• Agentless Discovery: Agentless discovery is a method of inspecting data sources without installing software on each workload. It is especially useful in hybrid estates where deployment friction creates blind spots, because it can improve coverage while reducing operational overhead and the chance of missed assets.

Deepen your knowledge

AI-native DSPM and data visibility across hybrid estates are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are aligning recovery planning with identity and data governance, it is worth exploring.

This post draws on content published by Cyera: Cyera + Cohesity: Data Security Meets Cyber Resilience. Read the original.