Find out in this report how the two Network Monitoring Software solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
The solution can save costs by improving incident resolution times and reducing security incident costs.
There is a lack of adequate local support from the Indian side.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
Splunk's technical support is amazing.
I would rate the support at eight, meaning there's some room for improvement.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
Cisco products are incredibly stable, boasting a 200% stability.
Splunk User Behavior Analytics is a one hundred percent stable solution.
Splunk User Behavior Analytics is highly stable and reliable, even in large-scale enterprise environments with high log injection rates.
Sometimes issues occur when handling long-term data.
The solution should have the ability to analyze security events not only at the network layer but also at the application and OS layers.
I encountered several issues while trying to create solutions for this advanced version, which seem unrelated to query or data issues.
High data ingestion costs can be an issue, especially for large enterprises, as Splunk charges based on the amount of data processed.
Advanced reporting could see enhancements as there are some issues with latency.
Cisco solutions are considered to be very expensive.
Comparing with the competitors, it's a bit expensive.
The pricing is based on the amount of data processed, and it is considered a high-level investment for enterprises.
The most valuable features include encrypted traffic analytics and the ability to fulfill requirements at the network level.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Splunk User Behavior Analytics is known for its advanced analytics and data correlation capabilities, which help in detecting patterns, anomalies, and security threats.
Features like alerts and auto report generation are valuable.
Cisco Secure Network Analytics is a highly effective network traffic analysis (NTA) solution that enables users to find threats in their network traffic even if those threats are encrypted. It turns an organization’s network telemetry into a tool that creates a complete field of vision for the organization’s administrators. Users can find threats that may have infiltrated their systems and stop them before they can do irreparable harm.
Cisco Secure Network Analytics Benefits
A few ways that organizations can benefit by choosing to deploy Cisco Secure Network Analytics include:
Cisco Secure Network Analytics Features
Some of the many features that Cisco Secure Network Analytics offers include:
Reviews from Real Users
Cisco Secure Network Analytics is a solution that stands out even when compared to many other comparable products. Two major advantages that it offers are the way that it enables users to define the threshold at which the solution will issue a warning to administrators and the predefined alerts that it offers straight out of the box.
Gerald J., the information technology operations supervisor at Aboitiz Equity Ventures, Inc., writes, “StealthWatch lets me see the ports running in and out and the country. It has excellent reporting, telemetry, and artificial intelligence features. With the telemetry, I can set thresholds to detect sudden changes and the alarms go through the PLC parts. I can see all the ports running on that trunk.”
A senior security engineer at a tech services company, says, “Cisco Stealthwatch has predefined alerts for different types of security issues that might happen in the network. Whether it's PCs or servers that are used for botnets or Bitcoin mining we receive the alerts automatically. This functionality is what we receive from the solution out of the box.”
Splunk User Behavior Analytics is a behavior-based threat detection is based on machine learning methodologies that require no signatures or human analysis, enabling multi-entity behavior profiling and peer group analytics for users, devices, service accounts and applications. It detects insider threats and external attacks using out-of-the-box purpose-built that helps organizations find known, unknown and hidden threats, but extensible unsupervised machine learning (ML) algorithms, provides context around the threat via ML driven anomaly correlation and visual mapping of stitched anomalies over various phases of the attack lifecycle (Kill-Chain View). It uses a data science driven approach that produces actionable results with risk ratings and supporting evidence that increases SOC efficiency and supports bi-directional integration with Splunk Enterprise for data ingestion and correlation and with Splunk Enterprise Security for incident scoping, workflow management and automated response. The result is automated, accurate threat and anomaly detection.
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