Sophisticated adversaries run low-and-slow campaigns across months of enterprise logs, hiding in the gaps between SOC alerts and quarterly hunts — on a timeline no human hunter can match. This brief introduces the AI Threat Hunt Agent: a force multiplier that turns natural-language hypotheses into evidence-backed hunts across your entire connected stack — SIEM, XDR, identity, endpoint, and data lake — and returns verdicts your team can defend.

Inside the Brief:

• Turn any hypothesis into an evidence-backed verdict: Natural-language hunts map to MITRE ATT&CK TTPs, execute via API across your connected tools, and return with the events, baselines, and uncertainty behind every conclusion — no KQL fluency required.
• Hunt across months of history without blowing the budget: The agent queries SIEM, XDR, identity, endpoint, and data-lake sources over long time windows to catch the low-and-slow adversaries annual hunts miss — cost-effectively, at scale.
• Skip the "ask the employee" trap: Context Lake™ supplies organizational history, benign-explanation testing, and prior investigation memory, so hunters validate hypotheses without disruptive interviews that risk tipping off insider threats.
• Trigger 10x more hunts from the SOC, automatically: Findings from the AI SOC Agent become the starting hypotheses for the next hunt — targeting a 10x lift in threat hunter productivity with 24/7 periodic or continuous coverage.
• Close the loop with governed response: Verdicts create cases, notify stakeholders, and recommend, assign, or execute containment per your autonomy policy — coordinated end-to-end with the AI SOC Agent.

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