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11/07/2026 8:37 pm
TL;DR: Fragmented risk data across business, security, and operations tools leaves teams with inconsistent reporting, slower decisions, and weaker oversight, according to OneTrust’s analysis of connected risk insights. The governance gap is no longer collection, but whether organisations can turn scattered signals into timely action.
NHIMG editorial — based on content published by OneTrust: Connecting Risk Insights to Enable Smarter Business Decisions
By the numbers:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems.
Questions worth separating out
Q: How should security teams turn fragmented risk data into usable governance evidence?
A: Start by deciding which system owns each class of evidence, then connect the surrounding tools through controlled integrations.
Q: Why does data fragmentation create problems for IAM and NHI programmes?
A: IAM and NHI programmes depend on accurate ownership, current entitlements, and reliable lifecycle records.
Q: How do organisations know whether connected risk insights are actually working?
A: Look for fewer duplicate reports, shorter time to decision, and clearer accountability for each risk item.
Practitioner guidance
- Define a system of record for each risk domain Assign one authoritative source for vendor risk, vulnerability evidence, access context, and business ownership so teams stop reconciling conflicting spreadsheets after the fact.
- Link identity evidence to risk workflows Connect service account inventories, privileged access records, and remediation tracking so identity findings can move into the same workflow as broader risk issues.
- Reduce manual evidence chasing Replace quarterly evidence collection with continuously updated data feeds where source lineage and ownership metadata stay attached to each control signal.
What's in the full article
OneTrust's full blog covers the operational detail this post intentionally leaves for the source:
- Native integration patterns for connecting risk data across business, security, and operations systems
- Examples of predefined mappings and automation triggers for turning evidence into workflow actions
- Developer-facing options for configuring syncs without custom code
- How the vendor frames connected risk ecosystems for implementation teams
👉 Read OneTrust's analysis of connecting risk insights across the enterprise →
Data fragmentation and risk insight gaps: what teams need to fix?
Explore further
11/07/2026 9:06 pm
Data fragmentation is now a control failure, not a reporting inconvenience. When risk evidence lives across business, security, and operational systems, leaders lose the ability to make consistent decisions. That weakens governance because every downstream control depends on accurate, current, and comparable inputs. The practical conclusion is that risk visibility must be treated as an operational control surface, not a dashboard problem.
A question worth separating out:
Q: Who should own the data model for connected risk and identity evidence?
A: Ownership should sit with the governance team that can define evidence standards, while domain teams maintain the source systems. That split prevents ad hoc reporting layers from becoming a second shadow system and keeps identity, security, and compliance decisions anchored to the same records.
👉 Read our full editorial: Data fragmentation is now a governance problem, not just an operations issue
