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IronSOC/AI Enhanced/Threat Hunting

AI Enhanced

Hunt threats at a scale that wasn't possible before

Traditional threat hunting is limited by analyst hours. AI-enhanced hunting runs continuously, generates hypotheses from live threat intel, and sweeps telemetry volumes that no team could cover manually.

Hunting capabilities

Continuous. Proactive. AI-powered.

Hypothesis generation

AI proposes hunting hypotheses based on current threat landscape, your environment's telemetry patterns, and known adversary TTPs targeting your sector.

Behavioral sweep

Hunt queries run continuously across petabytes of telemetry — not just when an analyst has time. AI surfaces anomalies that rule-based detection misses.

Anomaly detection

Baseline normal behavior for every user, service, and system. AI flags deviations that match known attack patterns or represent novel threats.

ATT&CK coverage mapping

Automatically map hunting activity to MITRE ATT&CK and ATLAS techniques. Identify coverage gaps and prioritize hunts against the most likely attack paths.

Cross-source correlation

Hunt across identity, cloud, endpoint, SaaS, and AI telemetry simultaneously. Attackers that blend into one source stand out across many.

Hunt analytics

Track hypothesis success rates, coverage trends, and time-to-discovery. Measure whether your hunting program is finding threats before they detonate.

Hunt examples

What we hunt for

Identity pivoting

Detect attackers who compromise one identity and use it to access other systems, escalate privileges, or create persistence.

Living-off-the-land

Find adversaries using legitimate tools (PowerShell, WMI, cloud CLIs) for malicious purposes — invisible to signature-based detection.

AI supply chain abuse

Hunt for poisoned RAG documents, compromised MCP servers, rogue model tool calls, and shadow AI applications.

Ransomware staging

Identify pre-ransomware behavior: mass credential harvesting, volume shadow deletion, scheduled task creation, and lateral movement.

Hunt methodology

Structured hunts, measurable outcomes

Every hunt follows a structured process: hypothesis, data collection, analysis, and findings. Results feed back into detection engineering to close coverage gaps permanently.

01

Hypothesis formation

AI analyzes your threat landscape, industry targeting data, and current detection coverage to propose hypotheses that target the most likely gaps.

02

Data collection & query

Hunt queries run across identity, cloud, endpoint, SaaS, AI, and network telemetry simultaneously. AI optimizes query patterns for coverage and speed.

03

Analysis & triage

Results are enriched with context, scored for likelihood, and mapped to MITRE techniques. AI flags the highest-confidence findings for human review.

04

Findings & detection

Confirmed findings become new detections. The hunt closes a gap, the detection ensures it stays closed. Coverage metrics update automatically.

Hunt program metrics

Measure what your hunting program finds

24/7
Hunt coverage
AI hunts continuously, not in sprints
6
Surfaces swept
Every telemetry layer, every hunt
100%
Findings actioned
Every finding becomes a detection
Weekly
Hypothesis cadence
New hypotheses from live threat intel

See threat hunting in action

The command center shows active hunts, hypothesis tracking, and coverage metrics in real time.

Open Command Center