The rapid adoption of User and Entity Behavior Analytics (UEBA) has attracted many new vendors with indistinguishable jargon. While the concept of applying machine learning to behavioral analytics is touted widely, the differences in approaches become clear when you look at the details. To separate the hype from reality, we want to describe what sets Niara apart in clear and simple terms.
FULL-SPECTRUM MACHINE LEARNING: Unsupervised, supervised, and adaptive techniques combined to build entity risk profiles. Niara reliably links anomalies with malicious intent to detect attacks and risky behaviors.
MULTIFACETED BEHAVIORAL MODELS:Machine learning across multiple behavioral dimensions for entity: authentication, internal resource access, peer to peer activity, remote access, cloud application usage, internet activity, physical access.
NO FILTERING, NO SUBSETS: Tens of data sources, hundreds of behavioral models across tens of thousands of users. 100% of the analytics applied to 100% of the data, in parallel, without filtering.
ADAPTIVE LEARNING: Analyst feedback incorporated into behavioral models to refine results for superior accuracy.
CUSTOM MODELS: A behavioral analytics framework that allows analysts to define custom use cases, tailoring the solution to specific needs.
The outcome? Niara identifies threats that easily evade other simplistic approaches. Niara detects more anomalies out of the box than other UEBA solutions with greater accuracy while simultaneously providing customers control over shaping the platform to fit their specific needs.
PACKETS, LOGS AND MORE: Niara’s analytics uses packets, flows, logs, external alerts and threat intelligence feeds to deliver a true assessment of an entity’s risk to the business.
DATA FUSION: Niara correlates diverse data elements arriving at varying velocities automatically, saving analysts the manual effort.
USE ANY DATA SOURCE: Ingest any data source, even customer-specific ones, knowing that the right machine learning models can be flexibly applied.
NO BLIND SPOTS: Analyzing diverse sources delivers complete visibility, surfacing anomalies that analysts would otherwise never see.
Why does this matter? The fidelity of data ingested equals the fidelity of results returned. Machine learning on diverse data sources across multiple behavioral dimensions enables accurate attack detection with fewer false positives and negatives.
When it comes to risk scoring, sometimes 1+1 equals 100. Niara’s machine learning models are contextually-weighted to account for the severity, sequencing, distribution and temporal significance of events to arrive at scores that accurately reflect risk. Customers can infuse business context by shaping the risk score at a granular level.
Niara integrates analytics-driven visibility with forensics to support detailed investigations going back months, even years. Quickly determining what happened, when it happened, and who was affected makes it easy for analysts to go from detection to investigation to closure easily.
Niara supports a full range of use cases, automatically detecting attacks and risky behaviors inside organizations and dramatically reducing the time and skill needed to investigate and respond to security events.
FLEXIBLE: Deploy on-prem or in the cloud easily. Professional services are not required.
EXTENSIBLE: Use standalone or integrate with existing security workflows and tools.
UNIVERSAL: Ingest data and alerts from your SIEM, log management solution, packet broker or directly from the source.
SCALABLE: Niara scales from small to large deployments across all stages of the processing pipeline – from data ingestion to analytics.
Quick installation and unmatched security insights within days.
Automated cyber and insider driven attack detection, incident prioritization and investigation and threat hunting in a single platform
Security insights into compromised users and hosts, negligent and malicious insiders via comprehensive behavioral analytics and enabling cost-effective historical incident investigations