A SaaS? tool that turns security logs into behavioral detections for anomalous and malicious activity.
Added Jun 1, 2026
Security teams struggle to manually analyze large volumes of logs, threat intelligence, and environmental signals quickly enough to catch emerging attacks. Job postings repeatedly point to a need for behavioral detections, anomaly detection, threat hunting, and correlation across large datasets.
The product ingests security logs and threat intelligence, correlates activity across systems, and helps analysts build, test, and deploy behavioral detection rules. It uses anomaly detection and pattern analysis to surface suspicious behavior, prioritize investigations, and reduce manual threat hunting effort.
Multiple security teams are hiring specifically for detection engineering, threat hunting, log analysis, and machine-learning-based monitoring, suggesting active investment in this capability. Growing attack complexity and expanding telemetry volumes make manual detection development increasingly difficult.
Showing 1-20 of 27 signals
Apply security expertise combined with AI-driven methods to analyze massive telemetry sets using big-data query languages (KQL) and AI-driven analysis, reasoning over data to identify novel malicious patterns and engineer evidence-based detection rules. Contribute to the design and implementation of AI-powered capabilities that autonomously disrupt sophisticated threats in near real-time.
Alarm Baseline and Rule Construction: Responsible for extracting features from logs of various security devices (such as WAF, IDS, EDR, honeypots, etc.) and formulating alarm baseline strategies. Continuously optimize correlation analysis rules to reduce invalid alarms at the source. Data Risk and Underreporting Detection: Utilize AI to conduct behavioral analysis on massive security logs. Through threat hunting (Threat Hunting), actively discover advanced persistent threats (APT) and potential
Design, develop, and maintain high-fidelity detection rules, correlation rules, alerts, and security use cases for newly onboarded and existing log sources. Continuously tune detections to reduce false positives, improve detection fidelity, and expand detection coverage across the environment. Develop security monitoring and detection content for AWS services, Kubernetes, microservices, Linux, macOS, databases, web applications, firewalls, and other enterprise technologies by leveraging the MIT
Go beyond the grade and inspect the evidence behind this opportunity.
Job ads
See which companies and roles are investing in this problem.Google Trends
Explore search interest, history, and momentum over time.Podcast evidence
Read the exact transcript passages behind the idea.