Notifications
Clear all
Topic starter
10/06/2026 11:32 pm
TL;DR: Data governance best practices now depend on identity controls as much as policy design, because compliance, access oversight, and auditability all fail when data ownership and entitlements are not aligned, according to Netwrix. The practical shift is that data governance programmes must be run as an access governance problem across human, non-human, and autonomous identities, not as a documentation exercise.
NHIMG editorial — based on content published by Netwrix: 10 data governance best practices for compliance
Questions worth separating out
Q: How should organisations align data governance with identity governance?
A: Organisations should align them by treating every data policy as an access control problem.
Q: Why do non-human identities complicate data governance?
A: Non-human identities complicate data governance because they access data at machine speed, often outside human review loops.
Q: How can teams tell whether data classification is actually working?
A: Teams can tell classification is working when labels change real access decisions, logging, and review outcomes.
Practitioner guidance
- Tie data owners to entitlement owners Require every sensitive dataset to have an accountable owner for the access paths that reach it, including application roles and non-human credentials.
- Inventory non-human access to governed data Build a current inventory of service accounts, API keys, tokens, and certificates that can read, write, or export governed datasets.
- Make classification enforceable at the access layer Check that sensitive-data labels trigger real controls such as conditional access, logging, and least-privilege scope on both human and machine identities.
What's in the full article
Netwrix's full blog covers the operational detail this post intentionally leaves for the source:
- Practical governance checklists for ownership, classification, and accountability that can be applied during programme rollout.
- Implementation detail on how to connect data controls to access review and audit workflows.
- Operational guidance for reducing risk around sensitive data, retention, and access visibility.
- Examples of governance tasks that matter when teams are preparing for compliance reviews.
👉 Read Netwrix's 10 data governance best practices for compliance →
Data governance best practices: what IAM teams should change?
Explore further
12/06/2026 5:40 am
Data governance breaks first at the identity layer, not the policy layer. Most governance programmes focus on classification, retention, and stewardship, but those controls assume access is already well-bounded. In real environments, human users and non-human identities create the actual enforcement surface, and that is where drift accumulates. The practitioner takeaway is that data governance maturity is inseparable from IAM and NHI control maturity.
A few things that frame the scale:
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks, according to The 2024 ESG Report: Managing Non-Human Identities.
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, including 46% confirmed and 26% suspected.
A question worth separating out:
Q: Who should own data governance when access spans humans and machines?
A: Data governance should be owned by the business data owner, but enforced jointly with IAM and security teams. The data owner defines sensitivity and acceptable use, while identity teams control access paths, credential lifecycle, and auditability. When either side owns the process alone, gaps appear between policy and enforcement.
👉 Read our full editorial: Data governance best practices are now identity governance issues
