Notifications
Clear all
Topic starter
09/07/2026 3:17 pm
TL;DR: Banks are spending nearly 10% of revenue on IT while 70% to 90% of enterprise data is now unstructured and 84% of financial organizations report overexposure, according to Collibra citing Gartner, McKinsey, and Ponemon Institute. The governance problem is not just data volume but whether the information AI systems and agents need is discoverable, classified, and access-controlled before automation scales.
NHIMG editorial — based on content published by Collibra: Clock is ticking, why banks can't ignore unstructured data
By the numbers:
- 84% of financial organizations say their unstructured data is accessible by people with no business need for it.
Questions worth separating out
Q: How should banks govern unstructured data before deploying AI?
A: Banks should first identify the repositories that hold high-value documents, emails, transcripts, and filings, then classify which sources are authoritative for each use case.
Q: Why do unstructured data problems delay AI programmes?
A: Unstructured data delays AI programmes because models need consistent context, and that context is often spread across silos, duplicated records, and hard-to-read formats.
Q: What do security teams get wrong about AI and unstructured content?
A: Teams often focus on model controls while leaving repository permissions, service account access, and content classification unchanged.
Practitioner guidance
- Inventory unstructured data repositories Catalogue where credit files, emails, call transcripts, and underwriting documents live, then identify which identities and service accounts can reach them.
- Tighten access to content sources used by AI Review repository permissions, shared drives, and downstream application access so that only approved users and machine identities can retrieve sensitive content.
- Classify authoritative sources before automation Define which document stores, message archives, and case files are authoritative for each AI workflow.
What's in the full article
Collibra's full post covers the operational detail this post intentionally leaves for the source:
- How the vendor frames unstructured data classification for banking workflows and AI use cases.
- Examples of the data foundation problems that delay fraud detection, credit decisioning, and personalisation.
- The productivity and cycle-time figures cited from McKinsey and PwC for agentic workflows in finance.
- The practical case for treating unstructured content as a governed asset before expanding AI programmes.
👉 Read Collibra's analysis of unstructured data and AI readiness in banking →
Unstructured banking data: what it means for AI governance teams?
Explore further
09/07/2026 3:35 pm
Unstructured data governance has become an AI security dependency: the problem is no longer only whether data exists, but whether it can be discovered, classified, and safely consumed by automated systems. When the most valuable banking knowledge lives in documents and messages, AI governance and data governance converge. Practitioners should treat content access as a control surface, not an afterthought.
A question worth separating out:
Q: Who should be accountable for unstructured data governance in AI projects?
A: Accountability should be shared across data owners, IAM teams, security leaders, and AI programme owners, because the risk spans classification, access, and usage. If any one group owns the problem alone, AI can inherit weak controls from the others.
👉 Read our full editorial: Unstructured banking data is blocking AI readiness and governance
