Enterprises need expanded cybersecurity threat detection while working within the confines of a fragmented landscape of security tools and scarce skilled personnel. The challenge is finding faster, easier ways to make sense of the flood of incoming data. The answer lies in security analytics that distills billions of events into a prioritized list of threat leads to maximize productivity of highly sought after cybersecurity staff.
Case Study

Case Studies: Increase Threat Visibility

Interset’s AI-enabled security analytics platform is protecting critical data for the world’s most important enterprises and government agencies. Read our case studies to learn how Interset increases threat visibility for these customers.

AI-enabled Security Analytics

Augment Human Expertise
Find Insider Threats with Anomaly Detection
Leverage Unsupervised Machine Learning

Skilled security personnel are in high demand, and ensuring both productivity and job satisfaction is critical to effective resource planning. Machine learning from a security analytics platform augments cybersecurity staff with automated analysis of vast volumes of big data to relieve skilled threat hunters, SOC analysts, and forensic investigators of traditionally manual processes and allow them to focus on what they do best.

No two insider threats are the same, making detection of new threats exceedingly difficult. To detect anomalies, every entity must be individually profiled to define “unique normal” and then contextually analyzed. An integrated, big data, security analytics platform leveraging unsupervised machine learning is the most practical option for effective insider threat detection.

Machine learning can reduce the dependency on expensive, difficult-to-find data scientists. But different machine learning techniques are effective for different types of threats. Choosing the right technique for the job is critical. Unsupervised machine learning, which automatically discovers patterns from limited datasets, is the most effective and scalable anomaly detection method to detect insider threats.

Find Threats Faster

  • Quickly Spot the Signs of Data Breaches
    Risk visibility is the Achilles’ heel of cybersecurity systems that generate many alerts and events. Underneath the flood of data lies critical signals of a data breach, a compromised account, or an advanced persistent threat. Improving risk visibility requires an integrated security analytics platform for a holistic view of risk.


  • Accelerate Threat Hunting
    With security analytics, threat hunters can actively focus on high-quality leads to begin an investigation instead of spending time testing hypotheses from the data. Interset’s unsupervised machine learning accelerates threat hunting by discovering new patterns without the need for a human to generate and test hypotheses.


  • Speed Up Cybersecurity Threat Investigation
    Security analytics results in faster threat investigation and response. With a combination of big data, AI, machine learning, and an intuitive UI, SOC teams can quickly focus on threats that are mostly like to impact enterprise risk and navigate to the contextual details needed to triage an alert.

Extract More Insights from Existing Security Investments

Enterprise cybersecurity solutions are fragmented across multiple layers and products across perimeter, network, application, data, and response tools. Every tool has a unique purpose and solves a specific problem. Security analytics augments and optimizes existing security investments by creating an ecosystem of data from which automated contextual cybersecurity insights can be extracted.

Scale with Big Data Cybersecurity Architecture

Big data technology is critical to enabling the scalable, extensible, and flexible framework needed to process the volume and variety of security data. The more data sources, the more holistic a view of risk you’ll be given. Only big data architecture can facilitate the computational power needed for highly scalable security analytics.

Transform SOC Efficiency

Visualize a Threat Leaderboard
Increase SOC Productivity
Relieve Alert Fatigue

Connecting the dots between signs of a threat traditionally depends on manual, tedious processes to create a contextual landscape against which to measure the seriousness of a threat. Security analytics enables analysis across entities and gives security operations center (SOCs) a list of prioritized threats to focus on.

Security analytics leverages automated machine learning to analyze and distill billions of events into a handful of qualified threat leads. Once a threat is identified, SOC efficiency is boosted by the ability to instantaneously see relevant context and raw information, bypassing manual processes for investigation and reporting.

Overwhelming data volumes hinder a SOC team’s efficiency, especially when alert fatigue prevents teams from finding the threats that matter. Security analytics can distill the noise created from a cacophony of security tools and save your SOC from a never-ending game of lengthy post-attack dwell times.