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24/06/2026 8:53 pm
TL;DR: AI systems magnify unreliable inputs into biased outputs, poor executive reporting, and slower deployments when data quality is not observable, according to Collibra. The governance gap is not model intelligence but confidence in the data pipeline that feeds it.
NHIMG editorial — based on content published by Collibra: Garbage in, generative out: Why your AI is only as smart as your data’s reliability
Questions worth separating out
Q: How should security teams govern AI when data quality is inconsistent?
A: Security teams should treat data quality as a governance dependency, not a background hygiene issue.
Q: Why do unreliable data inputs create risk for AI governance programmes?
A: Unreliable inputs create risk because AI systems do not reliably detect or correct bad source data.
Q: How do teams know whether data observability is actually working?
A: Data observability is working when teams can detect drift early, trace failures to the source quickly, and prove that priority datasets still match approved expectations.
Practitioner guidance
- Define decision-critical datasets Identify the datasets that directly affect access decisions, reporting, automation, or AI outputs.
- Baseline data behaviour before automation Record expected values, freshness, and lineage for priority pipelines before adding more AI or workflow automation.
- Trace every failed output back to source changes Require incident review to identify the upstream field, transformation, or ownership change that caused the bad result.
What's in the full article
Collibra's full blog post covers the operational detail this post intentionally leaves for the source:
- Examples of how data observability is applied across real pipeline stages and ownership boundaries.
- Specific monitoring, profiling, and anomaly-detection capabilities described by the vendor for data teams.
- Practical questions Collibra recommends data and engineering leaders ask before approving another AI pilot.
- The vendor's framing of observability as the control layer for scaling AI with confidence.
👉 Read Collibra's analysis of why AI is only as reliable as its data →
AI data reliability: what IAM and governance teams should fix first?
Explore further
25/06/2026 5:46 am
Data reliability is now an identity governance issue, not just a data engineering issue. When approvals, entitlements, telemetry, or asset records are inconsistent, every downstream decision inherits that uncertainty. The practical consequence is that governance teams can no longer treat data quality as a separate concern from access governance, because control decisions are only as trustworthy as the records behind them. Practitioners should treat reliability as part of the control surface.
A few things that frame the scale:
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
- 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.
A question worth separating out:
Q: What is the difference between data observability and basic monitoring?
A: Basic monitoring tells you that a system is up or down. Data observability tells you whether the data still behaves as expected, why it changed, and where the change originated. For AI and governance teams, that difference matters because availability without reliability still produces unsafe decisions.
👉 Read our full editorial: Data reliability is the control plane AI governance still lacks
