Notifications
Clear all
Topic starter
18/08/2026 2:42 pm
TL;DR: Sensitive data protection still fails first at discovery, because fragmented on-premises, cloud and SaaS estates hide PII in databases, collaboration tools, logs and backups, according to Ground Labs. For security and privacy teams, the hard problem is building a usable inventory before encryption, access control and compliance can work at scale.
NHIMG editorial — based on content published by Ground Labs: Why is data discovery the hardest part of data protection?
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities.
Questions worth separating out
Q: How should security teams implement data discovery in complex environments?
A: Start by mapping endpoints, databases, file shares, cloud services and SaaS platforms so the discovery scope matches the real estate where sensitive data actually lives.
Q: Why does unstructured data create identity governance risk?
A: Unstructured data creates risk when access is spread across repositories and shares without clear entitlement ownership or review.
Q: What breaks when discovery is missing from data protection programmes?
A: Encryption and access controls still help, but they cannot be applied consistently if teams do not know where sensitive data resides.
Practitioner guidance
- Build a centralized sensitive-data inventory Map databases, collaboration platforms, file shares, logs, backups and SaaS repositories into one governed inventory before expanding control policies.
- Prioritise unstructured data classification Focus classification effort on documents, emails, exports and backups because those stores usually contain the highest volume of hidden PII and regulated content.
- Link discovery to identity review workflows Tie data location results to access recertification so human users, service accounts and application integrations are reviewed against actual data exposure.
What's in the full article
Ground Labs' full blog post covers the operational detail this post intentionally leaves for the source:
- How its discovery approach maps sensitive data across databases, collaboration tools, logs, backups and SaaS repositories
- Which 300-plus PII data types and country-specific patterns the tooling recognises during classification
- How remediation workflows are structured once hidden personal data is found
- What implementation teams need to consider when moving from discovery findings to governance action
👉 Read Ground Labs' analysis of why data discovery is the hardest part of data protection →
Data discovery and dark data: what security teams need to fix?
Explore further
18/08/2026 3:26 pm
Data discovery is now a governance dependency, not a visibility nice-to-have. Organisations routinely frame discovery as a tooling problem, but the real issue is that every downstream control assumes the inventory is already complete. When sensitive data is scattered across clouds, SaaS and endpoint-adjacent stores, policy enforcement starts from partial knowledge. That weakens privacy governance, auditability and lifecycle decisions at the same time. The practical conclusion is simple: if the inventory is incomplete, the control environment is incomplete.
A question worth separating out:
Q: How do organisations know whether discovery is working?
A: Look for measurable coverage across the full estate, including shadow IT, backups, logs and collaboration tools, plus evidence that classified findings feed into access review and remediation workflows. If discovery only finds known systems, it is not changing governance. Effective discovery reduces unknown repositories and shortens the time from identification to control action.
👉 Read our full editorial: Data discovery is the bottleneck in modern data protection
