Splunk User Behavior Analytics and Cynet compete in the advanced security analytics and endpoint protection category. Splunk appears to have an advantage for large-scale data environments due to its robust data analysis capabilities, while Cynet stands out for its comprehensive security approach with competitive pricing.
Features: Splunk offers robust data aggregation, advanced searching, and modular capabilities for integration across platforms. It is noted for its powerful querying ability and quick response time. Cynet excels in comprehensive endpoint protection, advanced threat detection, and user behavior analysis. It is valued for autonomous threat blocking and ease of deployment.
Room for Improvement: Splunk could improve its correlation capabilities, reduce complexity, and provide better integration options. It also faces criticism for its pricing model. Cynet could benefit from better mobile support, improved third-party integrations, and reducing false positives. Enhancing detailed reporting and network integration are additional areas for Cynet to focus on.
Ease of Deployment and Customer Service: Splunk supports on-premises and public cloud deployments but is often seen as complex. Its customer support is satisfactory but can be improved in cost-effectiveness. Cynet offers flexible deployment, including private and hybrid clouds, with highly responsive customer service praised for its support quality.
Pricing and ROI: Splunk's pricing model is complex and perceived as expensive, while users report productivity gains as a result of its usage. Cynet is praised for its competitive pricing and flexible licensing, offering substantial ROI with extensive security features at a lower cost compared to competitors, making it attractive to organizations.
The solution can save costs by improving incident resolution times and reducing security incident costs.
Their SOC side support, when a threat is detected, is excellent.
Their technical support can be improved in terms of speed when opening a ticket.
I was very satisfied with their technical support.
Mission-critical offering a dedicated team, proactive monitoring, and fast resolution.
From the responsiveness perspective, Splunk is very responsive with SLA-bound support for premium tiers.
I would rate the support at eight, meaning there's some room for improvement.
The solution is highly scalable.
Cynet is very scalable.
Splunk User Behavior Analytics is highly scalable, designed for enterprise scalability, allowing expansion of data ingestion, indexing, and search capabilities as log volumes grow.
With built-in redundancy across zones and regions, 99.9% uptime is achievable.
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.
There should be more options than deploying solely through group policy, as the assumption that GPO is working isn’t always the case.
Having a DLP feature would also add value.
Integration with local Active Directory, not only Azure AD, is a must.
Global reach allows deployment of apps and services closer to users worldwide, but data sovereignty concerns exist and region selection must align with compliance requirements.
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.
I think the pricing of Cynet is fair and one of the better options in the market.
The price of Cynet is reasonable considering its features and support.
Reserved instances with one or three-year commitments offer lower rates, providing up to 70% savings.
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 valuable aspects of Cynet are its EDR and XDR components, which are available at a reasonable price point.
The most effective features of Cynet are its ransomware protection and lateral movement deception.
The SOAR function, deception, and forensics are very useful.
I also utilize it for anomaly detection and behavior analysis, particularly using Splunk's machine learning environment.
Features like alerts and auto report generation are valuable.
Splunk User Behavior Analytics offers several beneficial features, such as Insider Threat Detection, account compromise detection, risk scoring, threat detection, and machine anomaly detection.
Cynet provides endpoint protection, threat detection, and response. It helps companies secure files, devices, and networks from zero-day threats, reducing the need for extensive support staff through its continuous monitoring, antivirus, and anti-malware functionalities.
Cynet offers comprehensive security features, including EDR, NGAV, and MDR, suitable for cloud, on-premises, and hybrid environments. Organizations benefit from its 24/7 SOC services, seamless integration with other cybersecurity systems, and intuitive graphical interface. Features like autonomous malware blocking, scalability, detailed network user behavior analysis, and rapid policy deployment ensure robust security operations.
What are Cynet's most valuable features?Cynet is implemented across industries like finance, healthcare, and retail due to its comprehensive cybersecurity capabilities. Organizations in these sectors benefit from detailed network user behavior analysis, data aggregation, automation, and incident response, ensuring robust protection for critical infrastructure and sensitive data.
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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