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28/08/2026 12:03 pm
TL;DR: Most AI observability stacks track uptime and latency, but not what agents do with regulated data, leaving IAM, DLP, DSPM, endpoint, and SIEM blind to lineage from identity to destination, according to Trust3. The security gap is not whether agents run, but whether their scope, data flow, and outputs remain authorised and auditable.
NHIMG editorial — based on content published by Trust3: AI agent observability and the data provenance gap
By the numbers:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
Questions worth separating out
Q: What breaks when AI agent observability does not track data destination?
A: Security teams lose the ability to prove where regulated data went after an agent processed it.
Q: Why do AI agents complicate IAM and data security controls?
A: Because the core controls were built for human sessions and file-centric data movement, while agents act continuously, inherit permissions, and reason over data in context.
Q: How can security teams tell whether AI agent access is drifting out of scope?
A: Look for agents touching systems, data sets, or tools that are outside the intended task boundary, especially when those actions are not part of the approved workflow.
Practitioner guidance
- Instrument agent session lineage Capture identity, source data, tool calls, output destinations, and policy decisions in one record for every agent interaction involving regulated data.
- Define declared scope per agent Attach machine-readable purpose, allowed data classes, and approved destinations to each agent so drift can be measured against policy.
- Monitor output-side exposure Track whether an agent writes summaries, files, messages, or API payloads into systems with broader access or longer retention than the source.
What's in the full article
Trust3's full analysis covers the operational detail this post intentionally leaves for the source:
- A deeper breakdown of how agent execution traces can be assembled into a usable security record across identity, session, and data flow.
- Specific examples of how output-side monitoring changes what counts as a compliance event in agentic workflows.
- The article's full reasoning on behavioural drift, including how scope expansion differs from obvious policy violations.
- The practical questions CISOs and CIOs should ask when validating whether their current observability stack can prove data custody.
👉 Read Trust3's analysis of AI agent observability and data provenance →
AI agent observability and the data destination gap?
Explore further
28/08/2026 12:04 pm
Security observability for AI agents is now a data governance issue, not just a monitoring issue. If a platform can tell you an agent is healthy but not whether it moved regulated data to a new destination, it is solving the wrong problem. The governance failure is the lack of provenance from identity to data to destination, which leaves compliance teams without evidence and security teams without containment context. Practitioners should treat agent observability as a control layer for data handling, not a telemetry dashboard.
A question worth separating out:
Q: Who is accountable when an AI workflow sends regulated data to the wrong place?
A: Accountability usually sits with the organisation that allowed the workflow to operate without adequate runtime controls, auditability, and data handling rules. In regulated environments, teams must be able to show where sensitive data entered, how it was handled, and what controls were in place when the event occurred.
👉 Read our full editorial: AI agent observability leaves the data destination blind spot
