Security News > 2020 > February > Gurucul launches new AI/ML behavior analytics for guided proactive hunting of unknown threats

The new AI/ML behavior analytics for guided proactive hunting of unknown threats, enriched with MITRE ATT&CK Framework tactics and techniques as well as risk scoring, pre-built playbooks and case management capabilities reduce detection and response times by 67%. Gurucul provides prebuilt threat libraries that include models, queries, data features and playbooks to support a wide-range of threat hunting uses cases like insider threat detection, data exfiltration, phishing, endpoint forensics, malicious processes, ransomware detection and network threat analytics, as well as cyberthreat, human centric and entity related threat scenarios.

These prepacked libraries help analysts prioritize base activities and focus on the proactive investigation of new and unknown threat patterns using contextual data.

"By combining link analysis and chaining, Gurucul automatically connects all of the events linked to an incident and provides hybrid/borderless context without the need for analysts to run multiple queries or use different applications. Meanwhile, out-of-the-box threat libraries and AI/ML guided threat hunting allows security personnel to detect, analyze, and take immediate remediation actions confidently."

Gurucul AI enabled threat hunting capabilities apply advanced ML algorithms to assess a wide range of behavioral attributes to identify anomalies, outliers and indicators of compromise.

MITRE ATT&CK Framework API-based integration covers threat hunting for industrial control systems, enterprise and mobile, and ensures new threats are automatically detected and prioritized using Gurucul's risk scoring mechanism.

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