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24/06/2026 7:00 pm
TL;DR: Manual spreadsheets, email approvals and shared-folder evidence chains break down as regulatory scrutiny, AI oversight and reporting obligations increase, according to Collibra. Compliance automation platforms shift teams toward continuous control, traceability and defensible evidence rather than recurring fire drills.
NHIMG editorial — based on content published by Collibra: Compliance automation platform: From spreadsheet fire drills to automated control
By the numbers:
- For regulatory compliance, stronger governance and automated control can help organizations decrease reputational risk by 38%.
- It can also decrease the risk of regulatory fines and penalties by 58%.
- It can increase productivity for compliance and auditing teams by 3%.
Questions worth separating out
Q: How should organisations reduce manual compliance work without losing audit defensibility?
A: They should connect policies, ownership, lineage and evidence to the systems that create them, then automate collection and monitoring.
Q: Why do spreadsheet-based compliance processes fail as organisations grow?
A: They fail because evidence, approvals and ownership become fragmented across teams and tools.
Q: How do compliance automation platforms help with AI governance?
A: They help by linking each AI use case to the dataset, policy, approval and evidence trail that justify it.
Practitioner guidance
- Map control evidence to a live source of truth Stop storing approval history, lineage and control evidence in disconnected files.
- Define escalation triggers for control drift Identify which changes in policy, data classification, ownership or AI use must force immediate review.
- Link AI use cases to source data and approvals Record which datasets, policies and approvers are in scope before a model or agent goes live.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- How the platform maps policies to data, reports, models and AI use cases in practice
- Workflow patterns for automated evidence collection, approvals and remediation routing
- Examples of how control monitoring is connected to lineage, ownership and traceability
- What the vendor means by proactive guardrails and how that differs from manual review
👉 Read Collibra's analysis of compliance automation as control infrastructure →
Compliance automation platforms: is your control model still manual?
Explore further
25/06/2026 4:05 am
Compliance automation is becoming control infrastructure, not a documentation layer. The article's core point is that governance work fails when proof is assembled manually after events have already happened. That failure mode is broader than audit fatigue because it undermines defensibility across data, reporting and AI workflows. Practitioners should treat the platform choice as an operating-model decision, not a repository decision.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, according to The State of Secrets in AppSec.
A question worth separating out:
Q: What should security and compliance teams measure to know automation is working?
A: They should measure how long it takes to produce evidence, how often control data must be reconstructed manually and how many policy or ownership changes trigger untracked drift. If the answer still depends on email searches and spreadsheet reconciliation, the operating model remains manual.
👉 Read our full editorial: Compliance automation platforms are becoming control infrastructure
