Start by separating the actor from the authorization and the instruction. An agent can operate under a person’s identity while following a prompt, system message, or embedded instruction that the person never meant to author. Investigators should reconstruct the instruction source, the access grant, and the resulting data movement before assigning intent or accountability.
How to investigate agentic activity without collapsing everything into the human user
Investigations are stronger when they treat an agentic event as three separate questions: who held the account, what instruction path the agent followed, and what the agent actually did with its access. That split helps analysts avoid false attribution when a person owns the login but not the action, or when a hidden prompt, policy, or connector changed the outcome.
The practical aim is to reconstruct the control chain, not just the login trail. If a person’s session launched an agent, the user may still be the right owner for the environment, but that does not prove they authored the instruction or intended the downstream data movement.
Teams should preserve the instruction source, the execution context, and the resulting artefacts together. Without those three layers, it is easy to mistake normal delegated automation, prompt injection, or tool misuse for direct human misconduct.
What evidence separates human intent from agent behaviour?
Start with the instruction stack: system prompt, user prompt, retrieved context, embedded policy, tool configuration, and any external content that the agent could have consumed. The key investigative question is which instruction actually shaped the action, because agentic systems can inherit authority from a human while following a different controlling input.
Then compare that instruction stack with the access grant and the action trace. If the agent had broader access than the user needed, or if a connector allowed reach into systems the user never directly touched, the investigation should focus on privilege and delegation rather than user intent alone.
Finally, build a compact timeline of the agent’s decisions, tool calls, approvals, and data transfers. That sequence shows whether the event was a normal automated workflow, an overbroad delegation, or an abuse path where the agent was steered into actions the human did not explicitly request.
How should investigators assign accountability after the facts are clear?
Accountability should follow the relationship between instruction, authorization, and outcome. If the user knowingly triggered the agent and the agent acted within the expected policy envelope, the user remains the operator of record. If the agent followed a malformed, poisoned, or externally injected instruction, the control failure may sit in the system design, prompt handling, or guardrail layer instead.
In practice, that means investigators should separate operational responsibility from malicious intent. A person can be accountable for provisioning, approving, or supervising the agent even when they did not personally author every action. That distinction matters for incident response, HR review, access governance, and legal hold decisions.
Good case handling also records uncertainty. If the evidence shows delegated authority but not human authorship, say so explicitly. Avoid premature language such as “the user exfiltrated data” when the facts only show that a human-owned agent moved data under a broader runtime instruction chain.
Risk and Threat Considerations
Agentic events create attribution risk because the visible identity, the instruction source, and the effective actor can diverge. That gap is attractive to attackers and careless operators alike, especially when agents inherit credentials, consume untrusted content, or carry forward hidden instructions into tools and data stores.
Failure mechanism: The agent executes under a legitimate human account while a prompt, embedded instruction, or connector-driven input changes the action path, obscuring whether the person intended the result.
Impact: Teams can misclassify abuse, miss prompt injection or overprivilege, and assign blame or remediation to the wrong control point, which delays containment and weakens accountability.
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 and MITRE ATT&CK define the specific risk controls and attack patterns relevant to this topic.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Agent actions can diverge from the human user's intent when identity and privilege are inherited. |
| ASI06 — Memory & Context Poisoning | Hidden or injected context can alter agent behaviour without the user's authorship. | |
| ASI07 — Insecure Inter-Agent Communication | Agentic workflows can propagate unsafe instructions through connected systems and tools. | |
| Recommendation — Separate instruction, authority, and execution paths before assigning responsibility. Inspect prompt, memory, and retrieved context sources for contamination. Validate inter-agent and tool messages before treating them as trusted instructions. | ||
| MITRE ATT&CK | T1552 — Unsecured Credentials | Agent investigations often hinge on whether exposed credentials enabled unauthorized tool use. |
| T1204 — User Execution | Human-triggered activity can launch downstream actions that differ from the user's intended outcome. | |
| Recommendation — Hunt for exposed secrets and correlate them with the agent's access path. Trace the initiating action separately from the agent's later execution chain. | ||
Practitioner Guidance
What to verify: Confirm that investigators can reconstruct the full chain from account, to instruction source, to tool or data access, to output. If any one of those layers is missing, the conclusion should stay provisional.
Decision rule: If the agent’s action was technically allowed but not clearly intended, treat the event first as an authorization and control problem, not as proven human misconduct. If the instruction source cannot be trusted, shift quickly to containment, credential review, and replay analysis.
Practitioner takeaway: The central discipline is attribution discipline, not blame speed, because agentic investigations fail when teams confuse possession of the account with authorship of the action.
Related resources from NHI Mgmt Group
- How should security teams monitor AI agent activity without disrupting developers?
- How should financial institutions monitor core banking and trading applications to detect insider threat without overwhelming security teams with normal user activity?
- How should security teams govern machine identity credentials in agentic AI environments?
- How should security teams use AI in secret scanning without creating new blind spots?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 25, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
