Threat Intelligence Hunt and OT Anomaly Monitoring

AI-assisted threat analysis workflow for identifying, triaging, and investigating emerging cyber threats in Splunk environments while improving visibility, anomaly detection, and secure monitoring in operational technology systems.

The Problem

“AI-assisted threat hunting and OT anomaly monitoring for Splunk and cyber-physical environments”

Organizations face these key challenges:

1

Manual threat triage is slow and inconsistent across customer environments

2

Splunk searches can be expensive and create platform load when hunts are broad or poorly scoped

3

Threat intelligence is fragmented across reports, feeds, and analyst notes

4

OT monitoring often lacks contextual anomaly detection across process, network, and asset data

Impact When Solved

Reduce time to convert threat intelligence into actionable hunts and detectionsLower Splunk search load through targeted query generation and scheduled orchestrationImprove analyst triage consistency with AI-generated evidence summaries and recommended next stepsDetect OT process anomalies earlier using multivariate models instead of static thresholds alone

The Shift

Before AI~85% Manual

Human Does

  • •Review threat reports, feeds, and analyst notes to identify relevant threats
  • •Write, tune, and schedule Splunk searches for hunts and alert validation
  • •Investigate alerts manually across logs, asset context, and customer environments
  • •Monitor OT dashboards and static thresholds, then escalate anomalies case by case

Automation

    With AI~75% Automated

    Human Does

    • •Approve hunt priorities, search execution, and detection changes for customer environments
    • •Validate AI triage findings and decide investigation or escalation paths
    • •Review OT anomaly recommendations and authorize any high-risk operational response

    AI Handles

    • •Continuously summarize threat intelligence and map indicators to available telemetry
    • •Generate scoped hunt hypotheses, SPL drafts, triage checklists, and investigation summaries
    • •Prioritize alerts and hunts with evidence-backed recommendations and search cost awareness
    • •Detect multivariate OT anomalies and correlate process, network, and asset context for operator visibility

    Operating Intelligence

    How it works

    AI surfaces what is hidden in the data.

    Humans do the substantive investigation.

    Closed cases sharpen future detection.

    Confidence94%
    ArchetypeDetect & Investigate
    Shape6-step funnel
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapefunnel

    Step 1

    Scan

    Step 2

    Detect

    Step 3

    Assemble Evidence

    Step 4

    Investigate

    Step 5

    Act

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Technologies

    Technologies commonly used in Threat Intelligence Hunt and OT Anomaly Monitoring implementations:

    CI/CD integrationOther
    4 mentions
    CyberCX Intel Hunt for SplunkOther
    4 mentions
    Splunk EnterpriseOther
    4 mentions
    Splunk Extension for Visual Studio CodeOther
    3 mentions
    Splunk Search UIOther
    3 mentions
    +3 more technologies(sign up to see all)

    Key Players

    Companies actively working on Threat Intelligence Hunt and OT Anomaly Monitoring solutions:

    CrowdStrike Falcon threat huntingGoogle SecOps threat huntingMicrosoft Sentinel threat hunting workflowsPalo Alto Cortex XDR/XSIAM hunting

    Real-World Use Cases

    CyberCX Intel Hunt for Splunk

    A threat-hunting app that lets security analysts click once to automatically build searches for suspicious indicators from a specific threat campaign.

    Intelligence-led detection orchestration and analyst decision supportprivate beta application completed and positioned as a deployed partner-built app on spl2; early but real implementation.
    10.0

    AI-assisted monitoring and control in operational technology environments

    Use AI to watch industrial systems, spot unusual behavior, and help operators make control decisions more safely.

    Anomaly detection and decision support for cyber-physical operationsemerging but actively proposed for real ot deployment; guidance-focused rather than product-specific.
    9.5

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