Proactive, analyst-led hunts powered by Sentinel APEX threat intelligence — 1,625+ CVE advisories with CISA KEV and EPSS scoring — to find adversaries before automated alerts do.
Select a MITRE ATT&CK hunt template and execute it against our live threat engine — real KQL/Sigma query simulation with MITRE technique mapping.
From hypothesis formation to TTP-mapped detection engineering.
Structured hunts built on testable hypotheses derived from threat intelligence, recent CVE exploitation trends, and adversary TTP patterns relevant to your industry.
Analyst-LedEvery hunt maps discovered behaviors to specific ATT&CK tactics and techniques, building a defensible coverage matrix across your detection stack.
ATT&CK NavigatorHunts triggered by Sentinel APEX threat intel — newly weaponized CVEs, CISA KEV additions, and rising EPSS exploitation probability scores.
Sentinel APEXMove beyond static indicator matching to behavioral analytics that catch novel adversary techniques IOC feeds haven't yet attributed.
Behavioral AnalyticsHunt across existing telemetry sources — Splunk, Microsoft Sentinel, QRadar, CrowdStrike, Defender — without requiring new data collection infrastructure.
SIEM · EDREvery hunt produces a documented, reusable playbook — hypothesis, data sources, query logic, and findings — for continuous re-execution and team handoff.
Playbook LibraryAutomated detection — SIEM correlation rules, EDR behavioral alerts, signature-based AV — catches known patterns. Threat hunting exists to find what automated detection misses: novel techniques, living-off-the-land tradecraft, and adversaries who have already evaded your alerting and are operating quietly within your environment. The median dwell time for undetected intrusions remains measured in weeks for organizations without a dedicated hunting capability.
Effective threat hunting begins with a falsifiable hypothesis, not an open-ended search through logs. A hypothesis might be: "if an adversary gained initial access via a recently disclosed CVE in our externally-facing VPN appliance, they would establish persistence through a scheduled task or registry run key within 48 hours." The hunter then identifies the specific telemetry needed to confirm or refute this hypothesis, executes the search, and documents the outcome regardless of whether it found anything — a hunt that finds nothing still validates detection coverage for that technique.
MITRE ATT&CK provides the common language for both hypothesis formation and coverage tracking. Rather than hunting randomly, structured programs map their hunts against the ATT&CK matrix, building a heat map of which tactics and techniques have been actively hunted versus which remain blind spots. Over time, this produces a defensible, auditable record of detection coverage that satisfies both internal risk management and external compliance reviews (SOC2, ISO 27001) requiring evidence of proactive security monitoring.
The most efficient hunts are triggered by current threat intelligence rather than generic technique lists. Our Sentinel APEX engine continuously ingests CVE advisories — now exceeding 1,625 tracked vulnerabilities — enriched with CISA Known Exploited Vulnerabilities (KEV) status and EPSS (Exploit Prediction Scoring System) probability. When a CVE affecting software in your environment enters the CISA KEV catalog or its EPSS score crosses a high-probability threshold, this becomes an immediate, prioritized hunt hypothesis: "has this vulnerability already been exploited against us, and if so, what persistence or lateral movement followed?"
Indicator of Compromise (IOC) matching — known-bad IP addresses, file hashes, domain names — is fast but brittle; sophisticated adversaries rotate infrastructure and recompile malware to evade hash-based detection within hours of public disclosure. Behavioral detection looks for the underlying technique rather than its specific implementation: unusual parent-child process relationships, abnormal authentication patterns, living-off-the-land binary abuse (LOLBins), and statistical outliers in network traffic. A mature hunting program uses IOC matching as a fast initial triage layer and behavioral analytics as the primary hunting methodology, since IOCs alone will never catch a novel intrusion.
Organizations progress through five maturity levels: HM0 (no organized hunting, relying entirely on automated alerts), HM1 (ad hoc hunting using threat intel feeds without a structured process), HM2 (procedural hunting following documented playbooks from external sources), HM3 (hypothesis-driven hunting with internally developed procedures), and HM4 (automated hunting where successful hunt techniques are converted into permanent automated detections). We assess your current maturity level and provide a roadmap to advance toward HM3/HM4, where each manual hunt that finds a true positive gets operationalized into a standing detection rule.
Hunting does not require new data collection infrastructure in most environments — it requires better use of telemetry you already collect. Process execution logs, authentication events, DNS query logs, network flow data, and EDR behavioral telemetry typically contain the evidence needed for most hunts, but go unexamined because SIEM correlation rules only flag pre-defined patterns. Our hunting engagements work directly against your existing SIEM (Splunk, Microsoft Sentinel, QRadar) and EDR (CrowdStrike, Microsoft Defender, SentinelOne) platforms, building hunt queries native to your stack rather than requiring data export to a separate tool.
The value of threat hunting compounds when findings are operationalized. Every confirmed true-positive technique discovered through manual hunting should be converted into an automated detection rule, closing that gap permanently and freeing hunters to pursue net-new hypotheses rather than re-running the same manual search indefinitely. We document every hunt as a reusable playbook with the exact query logic, data sources, and detection threshold used — handed off to your SOC team for conversion into standing SIEM correlation rules or EDR custom detections.
Mature hunting programs maintain a structured library of hypotheses derived from multiple sources: MITRE ATT&CK technique gaps identified through coverage analysis, threat intelligence reporting on adversary groups targeting your industry vertical, lessons learned from prior incidents within your organization or sector, and emerging exploitation trends surfaced through CVE and EPSS scoring shifts. This library transforms hunting from an ad hoc activity dependent on individual analyst creativity into a repeatable, prioritizable program where hunt selection is driven by data — which hypotheses represent the highest-likelihood, highest-impact gaps in current detection coverage.
The quality of a hunting program is bounded by the quality and completeness of available telemetry. Common gaps that limit hunt effectiveness include insufficient log retention (many organizations retain only 30-90 days, while sophisticated intrusions can have dwell times exceeding this window), missing process-level command-line logging on endpoints, absent DNS query logging that would reveal command-and-control beaconing, and network flow data limited to perimeter traffic without internal east-west visibility. Before executing a hunting program, we assess data source completeness and recommend specific logging enhancements where critical visibility gaps would prevent a hypothesis from being properly tested.
Unlike automated detection, where metrics like alert volume and mean-time-to-detect are straightforward, measuring threat hunting program value requires different metrics: ATT&CK technique coverage achieved through hunting versus automated detection alone, the ratio of hunts that produce actionable findings versus those that confirm clean coverage, time from hunt finding to operationalized detection rule, and — critically — dwell time reduction for incidents subsequently discovered, comparing organizations with mature hunting programs against industry benchmarks for undetected intrusion duration. We help organizations establish these metrics from program inception so hunting investment can be justified with concrete outcomes rather than activity counts alone.
Threat hunting and SOC alert triage are complementary, not competing, disciplines. A well-resourced hunting program reduces SOC burden over time by converting successful hunts into automated detections, narrowing the gap between "what an analyst would catch manually" and "what fires automatically." Conversely, SOC analysts' day-to-day pattern recognition across high volumes of alerts often surfaces the subtle anomalies that seed new hunt hypotheses. Organizations that integrate hunting and SOC operations into a single feedback loop — rather than treating hunting as a separate, isolated function — see compounding improvement in detection coverage over successive quarters.
A hunt hypothesis is only as good as the data available to test it. Before any hunt begins, we assess whether your environment actually generates the telemetry required to validate the hypothesis — endpoint process execution logs, authentication records with sufficient retention, network flow data, and cloud API audit trails. A common finding in initial hunt program assessments is that organizations have the security tooling deployed but configured with default logging levels that omit the specific fields a hunt requires, or retention windows too short to investigate anomalies discovered weeks after they occurred. Closing these visibility gaps is frequently the highest-leverage first step in standing up a hunting capability, since no amount of hunting skill compensates for data that was never collected.
As infrastructure shifts to cloud and SaaS platforms, the highest-value hunt targets shift accordingly. Traditional endpoint and network-centric hunting remains important, but modern intrusions increasingly center on identity compromise and cloud API abuse rather than classic malware deployment. Hunt hypotheses now routinely cover scenarios like impossible-travel authentication patterns, anomalous OAuth token grants to third-party applications, unusual cloud resource provisioning consistent with cryptomining, and service account credentials being used interactively rather than through their expected automated workflow — patterns that require cloud-native audit log access rather than traditional endpoint telemetry alone.
Organizations weighing whether to build an internal threat hunting capability or engage an external team should consider both skill availability and program maturity. Internal teams benefit from deep familiarity with the specific environment's normal baseline behavior, which accelerates anomaly recognition, but threat hunting requires specialized skills — adversary tradecraft knowledge, advanced query languages across diverse log sources, and forensic investigation experience — that are scarce and expensive to hire for a function that may not need full-time staffing in smaller organizations. A blended model, where external hunters bring specialized technique knowledge and a fresh perspective while internal staff provide environmental context and continuity, frequently outperforms either approach alone.
Effective hunt programs document outcomes at multiple levels of detail for different audiences. Technical findings — specific indicators discovered, queries run, detection rules created — serve the engineering and SOC teams who must act on them. Aggregated program metrics — hypotheses tested, coverage achieved, findings converted to permanent detection — serve security leadership tracking program maturity. A concise summary translating these technical outcomes into business risk language — "we proactively identified and contained three instances of credential abuse before they escalated" — gives executive and board audiences a defensible way to understand the value of investment in proactive, rather than purely reactive, security capability.
A hunt that finds no evidence of compromise is not a wasted exercise — when paired with confirmed data source coverage, a clean hunt result is itself meaningful evidence that the hypothesized technique was not used against the environment during the period examined. The critical caveat is distinguishing "we looked and found nothing" from "we couldn't actually see the relevant telemetry," which is why every hunt documents not only its findings but the specific data sources queried and their coverage limitations, so a negative result carries genuine confidence rather than false reassurance from an incomplete search.
How is threat hunting different from incident response? Incident response begins after a confirmed or suspected compromise has been identified; threat hunting actively searches for evidence of compromise that hasn't triggered any alert yet, operating under the assumption that some intrusions evade automated detection entirely.
Do we need a SIEM before we can start threat hunting? Centralized log aggregation significantly accelerates hunting, but meaningful hunts can begin with targeted queries against EDR telemetry and authentication logs even before a full SIEM deployment, as long as retention and query access are sufficient for the specific hypothesis being tested.
Run a free security assessment to see your current exposure, or book a structured threat hunting engagement.