Fidelis Elevate and Kaspersky Anti-Targeted Attack Platform provide robust cybersecurity solutions. Users report greater satisfaction with the pricing and support of Fidelis Elevate. However, Kaspersky Anti-Targeted Attack Platform stands out due to its comprehensive feature set, making it worth the investment for many users.
Features: Fidelis Elevate is praised for its advanced threat detection, automated response capabilities, and efficient network visibility. Kaspersky Anti-Targeted Attack Platform is noted for its comprehensive threat intelligence, in-depth analysis, and real-time threat hunting. Kaspersky offers a broader feature set, gaining an edge in this category.
Room for Improvement: Users point out that Fidelis Elevate needs improvements in integration with third-party tools, reporting features, and user interface. Kaspersky Anti-Targeted Attack Platform users suggest enhancements in system performance, usability, and incident response time. Kaspersky’s performance issues are a more significant concern for users.
Ease of Deployment and Customer Service: Fidelis Elevate is generally reported to have a straightforward deployment process with strong customer support. Kaspersky Anti-Targeted Attack Platform, while also praised for its support, receives mixed reviews on deployment complexity. Fidelis Elevate has a slight advantage in ease of deployment, but both offer commendable support.
Pricing and ROI: Fidelis Elevate is viewed as cost-effective, offering good ROI according to user reviews. Kaspersky Anti-Targeted Attack Platform, despite a higher price point, is considered a valuable investment due to its superior feature set. Users feel that both products provide fair returns, with Kaspersky’s extensive capabilities justifying its cost.
Fidelis Elevate integrates network visibility, data loss prevention, deception, and endpoint detection and response into one unified solution. Now your security team can focus on the most urgent threats and protect sensitive data rather than spending time validating and triaging thousands of alerts.
Today’s cybercriminals constantly design unique and innovative methods of penetration and compromise. To avoid perimeter prevention technologies they use social engineering, non-malware and supply chain attacks to operate under the radar of security designed to catch ‘bad’ traces. It’s not enough to just ‘know’ what’s bad or dangerous – enterprises need to understand what’s normal, and use AI-driven techniques that simplify and automate this process. Targeted Attack Analyzer is a machine learning engine that involves self-learning to establish the baseline of normal, legitimate activities of an entire network. Through continuous network telemetry collection it finds deviations, detects suspicious activities and predicts further malicious actions at the initial stages of multilayered attacks.
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