Who should be accountable when a SOC agent takes the wrong action on bad telemetry?

Why This Matters for Security Teams

When a SOC agent acts on poor telemetry, the issue is not just a bad alert. It is a governance failure that can trigger the wrong containment step, suppress a real incident, or create a false sense of control. Accountability matters because an autonomous or semi-autonomous response path extends the blast radius of weak data quality, unclear approval boundaries, and overbroad tool access.

For that reason, responsibility should be traced through the full operating model: who defined the agent’s remit, who accepted the telemetry sources, who approved response authority, and who is responsible for supervision and escalation. That aligns with the control logic in the NIST AI Risk Management Framework, which treats AI risk as an organisational management issue rather than a purely technical one. The same principle appears in the OWASP Agentic AI Top 10, where uncontrolled action, weak oversight, and unsafe tool use are recognised as core failure modes.

Practitioners often assume the agent is “to blame” when the real defect is that nobody owned the guardrails, the escalation policy, or the quality threshold for the inputs. In practice, many security teams encounter this only after the agent has already quarantined the wrong asset or auto-closed the wrong case, rather than through intentional design.

How It Works in Practice

Accountability in a SOC agent workflow should be mapped to decision rights, not to the software itself. The programme owner is accountable for authorising the use case, the data owner is accountable for the telemetry quality and provenance, and the SOC or platform operator is accountable for the permissions, monitoring, and override path. That is the practical application of good governance under the NIST AI Risk Management Framework and control discipline under NIST SP 800-53 Rev 5 Security and Privacy Controls.

A useful operating model is to separate the questions “may the agent see this signal?”, “may it recommend action?”, and “may it execute action?”. Those are different controls and should not be collapsed into one approval. Current guidance suggests that agents handling response workflows should have narrowly scoped permissions, explicit human override, auditable decision logs, and pre-defined confidence or evidence thresholds before any automated action is taken. Where the system uses multiple feeds, the ingestion pipeline should be treated as part of the control surface, not just a data engineering concern.

• Define which telemetry sources are trusted enough to trigger action.
• Tag each agent action with the triggering evidence and policy version.
• Require explicit approval for destructive or high-impact response steps.
• Test failure paths using poisoned, stale, or incomplete telemetry.
• Route exceptions to a named operator with authority to halt automation.

For threat-informed validation, the MITRE ATLAS adversarial AI threat matrix helps teams think about adversarial manipulation of model inputs and outputs, while the CSA MAESTRO agentic AI threat modeling framework is useful for modelling how tool access, autonomy, and oversight fail together. These controls tend to break down when telemetry is noisy, source ownership is unclear, and the SOC depends on automation to compensate for analyst shortages.

Common Variations and Edge Cases

Tighter automation governance often increases operational friction, requiring organisations to balance faster containment against stronger review and escalation controls. That tradeoff becomes sharper when the SOC agent sits between detection, triage, and response, because bad telemetry can create both false positives and missed detections.

There is no universal standard for this yet, but current guidance suggests three common edge cases. First, if the agent only recommends actions, accountability remains with the human approver, even if the recommendation was generated from faulty inputs. Second, if the agent executes only low-risk actions, the accountable party is still the owner of the policy that defined what “low-risk” means. Third, if the agent is shared across multiple teams, accountability must be assigned by system boundary, not by job title alone.

Agentic environments also introduce identity and privilege questions. If a SOC agent has credentials, API keys, or delegated access to containment tools, then its permissions should be treated as a privileged identity with scope, review, and revocation controls. That is where identity governance intersects with AI governance: the organisation must know who can change the agent’s policy, who can rotate its secrets, and who can disable it when telemetry quality drops below the accepted threshold. In practice, these arrangements fail most often in hybrid SOCs where response ownership is split across MSSP, internal security, and platform teams, because no single party owns the full decision chain.

Related resources from NHI Mgmt Group

• Who is accountable when a managed agent takes the wrong action?
• Who is accountable when an AI SOC platform takes the wrong action?
• Who is accountable when an AI agent takes an unsafe action?
• Who is accountable when an autonomous agent takes an unsafe action?