Corelight

Start with one alert source or investigation workflow for network detection platform using open network evidence and machine learning to support threat hunting and incident response. Connect a representative but limited set of telemetry and define which actions the AI may recommend, which require approval, and which are prohibited.

Run Corelight in observation or recommendation mode first. Compare its findings with analyst-reviewed cases and preserve the underlying evidence, timestamps, identities, events, and source-system links. Investigate false positives and missed cases rather than tuning only for attractive dashboard metrics.

Introduce automation gradually. Low-risk enrichment and case creation can be automated earlier; account blocking, endpoint isolation, transaction denial, code changes, and other consequential actions should use policy gates, least-privilege credentials, reversible actions, and a complete audit trail.

After rollout, monitor detection quality, analyst overrides, latency, drift, integration failures, and changes in attacker or fraud behavior. Revalidate controls when adding new models, data sources, business lines, regions, identities, or autonomous actions.