Agentic SOC Readiness Assessment

Analysts are trained to review, validate and correct AI output.

Named owners are responsible for agent feedback, escalation and oversight.

There is an upskilling plan for analysts moving into AI supervision.

Leadership has agreed which outcomes AI should improve, such as response time or analyst workload.

Core triage and investigation workflows are documented and followed consistently.

Decision logic and escalation paths are written down.

You track baseline metrics such as MTTD, MTTR and false positive rate.

High-impact actions, such as isolating a host or disabling an account, need a defined approval.

Endpoint, cloud, identity and network telemetry land in one platform.

Logs are normalized to a common schema.

Events are enriched with asset, user and threat intelligence context.

Data arrives in near real time and ingestion health is monitored.

There is a safe test environment for automations and agents.

Detection and automation content is under version control with rollback.

SOAR or workflow automation already handles deterministic tasks.

Integrations use least-privilege service accounts and API scopes.

Each agent has a written list of allowed actions, separating recommend from execute.

Agent decisions are logged with the data and reasoning behind them.

AI components have been threat-modeled for prompt injection and data poisoning.

Agent accuracy is monitored for drift and reviewed on a schedule.