Security teams should investigate the full chain, not just the isolated event. When a human delegates work to an agent, and that agent authenticates, reads data, transforms it, and passes it onward, the real risk sits in the relationships between those steps. A graph-based investigation preserves that chain so analysts can see who acted, what was touched, and where information moved.
How to investigate the whole workflow, not just one alert
When humans, AI agents, applications, and data all participate in one process, the investigation unit should be the workflow graph, not any single log line. Analysts need to preserve delegation, authentication, data access, transformation, and forwarding as one chain of evidence. That is what lets you answer the real questions: who initiated the action, which principal actually executed it, what data was touched, and where the result went.
This is especially important when agent activity is part of the chain. NHI Management Group’s AI Agent Observability, Audit and Incident Response Guide is useful here because attribution, audit trails, and kill-switch decisions only make sense when you can reconstruct the sequence end to end. Without that chain, teams tend to overfocus on the final action and miss the earlier trust decision that made it possible.
A practical investigation starts by stitching together identity, session, workload, API, and data events into a single timeline. If an agent acted on behalf of a person, capture the handoff point, any delegated scope, and the exact resource context used at each step. If a downstream system transformed or enriched the output, preserve that transformation boundary too, because later compromise or leakage often originates in an earlier authorization choice rather than the visible outcome.
What the graph reveals that a flat log review misses
A graph-based view helps investigators see relationship patterns that ordinary event review hides. It can show repeated reuse of the same account or token, indirect access through a tool or middleware layer, and unexpected fan-out from one request to many systems. That matters because the risky behavior may not be the individual event itself, but the way one principal inherits influence across multiple steps.
In agentic workflows, the attack surface is often the sequence of permissions, not one permission in isolation. NHI Management Group’s AI Agent Authorisation Guide is relevant because per-action authorization and least privilege are the controls that determine whether a workflow stays bounded when an agent, human, or application is chained into the same process. If the graph shows an action that exceeded the expected scope, that is usually more useful than asking whether the actor was “allowed” in a generic sense.
The same model helps separate normal orchestration from abuse. A healthy workflow has visible handoffs, bounded access, and clear ownership of each step. A suspicious one often has hidden impersonation, overbroad standing access, or a principal that accumulates authority as the chain progresses. Those are the moments where the graph becomes more than a visualization tool, it becomes an incident detection method.
How to structure the investigation so findings are actionable
Build the case around entities and edges, then validate each edge against a source of truth. Start with the initiator, then confirm the executing principal, the tool or service invoked, the data object read or written, and the destination that received the output. This order matters because investigators often misattribute a workflow when they start from the final system that observed the activity instead of the first actor that set it in motion.
Where AI agents are involved, use the chain to decide whether the issue is normal delegated execution, excessive privilege, or true abuse. NHI Management Group’s Agentic AI Security Guide helps frame those distinctions because the controls differ depending on whether the problem sits in inputs, tools, orchestration, or identity. If the graph shows a benign-looking request leading to an unexpected tool call or data path, investigate the trust boundary that was crossed rather than the last event alone.
When the workflow crosses teams or systems, make ownership explicit. Security may own the investigation, but platform, application, data, and IAM teams each need a role in validating their portion of the chain. The final output should be a narrative that ties the initiating intent to the actual execution path, the sensitive data exposure, and the control failure that allowed the path to exist.
Risk and Threat Considerations
Workflow-level investigation is necessary because the main risk is not just unauthorized action, it is hidden authority propagation. A human can delegate to an agent, the agent can authenticate legitimately, and the resulting activity can still become unsafe if scope, data access, or onward transmission is broader than expected. The same pattern also helps attackers hide in normal orchestration, where each individual step looks plausible on its own.
Failure mechanism: Analysts review isolated alerts, lose the delegation chain, and miss the point where a legitimate identity, token, or tool invocation transferred excessive authority into the next step.
Impact: The team can misclassify abuse as routine automation, preserve the wrong access path, and fail to see where sensitive data moved or which system should be contained first.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent-workflow investigations center on delegated identity and privilege misuse. |
| ASI02 — Tool Misuse | The question involves agents using tools within a shared workflow chain. | |
| Recommendation — Trace delegated authority and flag stepwise privilege expansion across the workflow. Review every tool invocation for scope drift, unauthorized data access, and unsafe forwarding. | ||
| NIST SP 800-53 Rev 5 | AU-6 — Audit Record Review, Analysis, and Reporting | Graph-based investigation depends on correlated audit review across principals and systems. |
| AC-6 — Least Privilege | Workflow risk often comes from excessive scope across human and agent actions. | |
| IA-5 — Authenticator Management | Investigations must account for tokens and credentials that enable chained workflow actions. | |
| Recommendation — Correlate audit records into one timeline that preserves delegation and data movement. Limit each principal to the minimum access needed for its step in the chain. Track credential and token use so attribution survives handoffs and reuse. | ||
Practitioner Guidance
What to verify: Confirm that the investigation can answer four questions in one trace: who initiated the workflow, which principal executed each step, what data was touched, and where the output was sent. If any one of those is missing, the analysis is incomplete.
Decision rule: If the chain includes delegation or a shared orchestration layer, treat the investigation as a relationship problem before it is treated as an event problem. If the graph reveals privilege expansion, unexpected reuse, or an unexplained branch, escalate containment before spending time on a narrow root-cause narrative.
Practitioner takeaway: The most reliable investigations are built from chain-of-custody for actions, not from isolated alerts. If you cannot reconstruct the workflow as a graph, you are likely missing the control failure that made the incident possible.
Related resources from NHI Mgmt Group
- Guide: Red Teaming AI Agents for Identity Abuse
- How should security teams implement centralized authorization when applications, gateways, and AI agents all need the same policy decisions?
- Why do AI agents and copilots create new data security risk compared with traditional applications?
- How should security teams govern applications, infrastructure, data, and AI workloads for autonomous agents?
- How should security teams govern personal data used by AI agents?
Deepen Your Knowledge
Free weekly newsletter
Subscribe to the NHI & AI Identity Journal
The latest on NHI and Agentic AI security – articles, research, breaches, news and events every week.
Bonus 33% off our NHI Course when you subscribe.
Reviewed and updated by the NHIMG editorial team on October 30, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org