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22/06/2026 10:18 am
TL;DR: Data sprawl is creating ungoverned copies of sensitive information across cloud, SaaS, and AI-connected systems faster than security teams can inventory or classify them, according to Netwrix. The real problem is not data volume but the loss of ownership, lifecycle control, and consistent policy enforcement across environments.
NHIMG editorial — based on content published by Netwrix: Data sprawl: Managing uncontrolled growth across cloud environments
By the numbers:
- 29% of organizations saw their data volumes grow by 30% or more in a single year.
- 72% of organizations have experienced or suspect they have experienced a breach of non-human identities.
Questions worth separating out
Q: How should security teams control data sprawl in cloud and SaaS environments?
A: Security teams should start with a live inventory of where sensitive data lives, then tie each store to an owner, a classification tier, and a retention decision.
Q: Why does data sprawl increase risk even when security tools are already in place?
A: Security tools only work where data has been discovered and classified.
Q: What do organisations get wrong about shadow AI and data governance?
A: They treat shadow AI as a usage issue instead of a data movement problem.
Practitioner guidance
- Build a live data estate inventory Enumerate customer, HR, financial, and operational data across cloud storage, SaaS applications, databases, and backups.
- Prioritise abandoned and dark stores first Use access-to-volume mismatches to find snapshots, dev/test copies, and integration caches with little legitimate activity.
- Tie classification to retention and deletion rules Assign stewards to every major data domain and bind a default retention period to each classification tier.
What's in the full article
Netwrix's full blog covers the operational detail this post intentionally leaves for the source:
- A step-by-step six-stage governance strategy for mapping, classifying, and remediating distributed data stores.
- Operational guidance for identifying dark data, abandoned snapshots, and SaaS caches that no team actively owns.
- Practical treatment of shadow AI approvals, data retention decisions, and escalation paths for ungoverned repositories.
- Examples of how Netwrix positions access analysis across hybrid Microsoft environments and cloud repositories.
👉 Read Netwrix's guide to managing uncontrolled data growth across cloud environments →
Data sprawl across cloud environments: where governance breaks down?
Explore further
22/06/2026 11:09 am
Data sprawl is a governance failure before it is a storage problem. The critical assumption is that security teams can still account for data location, ownership, and policy coverage once cloud replication and SaaS copying accelerate. That assumption fails when copies proliferate faster than inventory and review processes can track them. The implication is that data governance must be treated as a live control plane, not a periodic audit task.
A few things that frame the scale:
- 72% of organizations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- A separate finding shows that enterprises that have experienced a compromised NHI averaged 2.7 separate incidents in the past 12 months.
A question worth separating out:
Q: Who is accountable when regulated data spreads across multiple clouds and SaaS tools?
A: Accountability belongs to the named steward for each data domain, supported by the control owners for classification, access review, and retention. If no one can prove ownership of a dataset, the organisation cannot prove compliance for deletion, access restriction, or breach response. That is a governance failure, not a tooling gap.
👉 Read our full editorial: Data sprawl in cloud environments is outpacing governance controls
