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10/06/2026 11:14 pm
TL;DR: Shadow data is data stores and copies that sit outside approved visibility and control, creating exposure that governance and DSPM programmes cannot manage if they cannot find it, according to Netwrix. The security issue is not just data sprawl, but the identity and access blind spots that let sensitive information remain reachable long after teams think it is contained.
NHIMG editorial — based on content published by Netwrix: What is shadow data and how to secure it
Questions worth separating out
Q: How should security teams identify shadow data across cloud and SaaS environments?
A: Start with data discovery across sanctioned and unsanctioned repositories, then map each sensitive copy to the identities that can access it.
Q: Why does shadow data create IAM risk as well as data security risk?
A: Shadow data creates IAM risk because access often persists through the same human accounts, service accounts, and integrations that were used for the original system.
Q: What breaks when shadow data is not included in access reviews?
A: Access reviews become incomplete and misleading because they certify only the repositories the team already knows about.
Practitioner guidance
- Map shadow data to actual identity paths Build an inventory that links every discovered sensitive data store to the human, service account, and third-party identities that can still reach it.
- Extend DSPM to copied and exported data Include cloud exports, endpoint caches, collaboration folders, and analytics replicas in your discovery scope so unmanaged copies do not sit outside monitoring and classification.
- Reconcile retention with access removal Verify that retention schedules, deletion workflows, and entitlement revocation operate on the same inventory.
What's in the full article
Netwrix's full blog covers the operational detail this post intentionally leaves for the source:
- Practical examples of where shadow data tends to appear across cloud storage, SaaS exports, and collaboration tools
- Step-by-step ways to identify shadow data using DSPM workflows and classify the exposed records
- Remediation guidance for deleting or restricting copied data without breaking business workflows
- Discussion of the security and governance consequences when shadow data remains unremediated
👉 Read Netwrix's blog on what shadow data is and how to secure it →
Shadow data: what it means for IAM, DSPM, and governance?
Explore further
12/06/2026 5:06 am
Shadow data is a visibility failure before it is a confidentiality failure. If security teams cannot enumerate copied and exported data, they cannot govern its access, lifecycle, or exposure window. That means the real break is not that data exists in more places, but that the organisation has lost the ability to apply identity controls consistently. Practitioners should treat shadow data as a governance boundary problem, not only a data classification problem.
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.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
A question worth separating out:
Q: How should organisations handle shadow data in retention and offboarding workflows?
A: They should treat every data copy as part of the lifecycle, not an exception. That means linking discovery to deletion, entitlement removal, and third-party offboarding so sensitive copies do not survive the business process that created them. NIST Cybersecurity Framework 2.0 is most effective here when discovery is complete.
👉 Read our full editorial: Shadow data exposes hidden identity risk across unmanaged data
