What We Did

• Applied AI/ML techniques to cluster, trend, and forecast user and machine behavior for anomaly detection

• Delivered over 20 active ML models to establish baselines and flag potential threats

• Integrated outputs into SOC/NOC dashboards using Splunk, Power BI, Qlik, and Tableau

The Challenge

Organizations with large, distributed infrastructures face constant cyber threats across countless devices, networks, and endpoints. Traditional security monitoring often generates overwhelming volumes of alerts but struggles to differentiate routine anomalies from true risks.

Without the ability to establish reliable behavioral baselines, critical warning signs can be missed, leaving systems vulnerable to insider threats, advanced persistent attacks, and costly breaches.

Lifescale Analytics’ Solution

Lifescale Analytics designed and deployed a machine learning–driven threat detection framework to close these gaps. Our team built over 20 active ML models capable of clustering and forecasting user and machine behavior, creating dynamic baselines of normal activity. Deviations from these baselines were flagged in near real time, allowing security teams to focus on actionable threats rather than noise.

We also integrated anomaly detection outputs into enterprise dashboards via Splunk, Power BI, Qlik, and Tableau, ensuring stakeholders across SOC/NOC operations had a clear, unified view of security posture. This scalable approach not only enhanced day-to-day monitoring but also provided a repeatable process for future model development and expansion.

Impact

With AI-powered detection in place, the organization gained:

• Faster detection of abnormal activity

• Improved visibility across complex environments

• Stronger resilience against internal and external threats