Kaspersky Anti-Targeted Attack Platform and Bitdefender GravityZone Extended Detection and Response (XDR) are robust cybersecurity solutions with distinct strengths. Users favor Bitdefender GravityZone XDR's advanced feature set and comprehensive threat detection, whereas Kaspersky's pricing and customer support are highly appreciated.
Features: Kaspersky offers advanced threat detection, in-depth analysis capabilities, and detailed deployment guidance. Bitdefender GravityZone XDR provides comprehensive threat intelligence, automated response, and broad integration support. Users find Bitdefender's features more comprehensive and easier to manage compared to Kaspersky.
Room for Improvement: Users suggest that Kaspersky could enhance integration options, reduce complexity, and bolster its feature set. Bitdefender users recommend improvements in reporting features, reducing false positives, and refining user interface. Both products have areas for enhancement, but user feedback emphasizes more tangible areas for improvement in Kaspersky.
Ease of Deployment and Customer Service: Kaspersky users appreciate responsive support and detailed deployment guidance. Bitdefender is noted for its straightforward deployment process and effective customer service. Bitdefender's simplicity in deployment garners more favorable user reviews.
Pricing and ROI: Kaspersky users report satisfaction with setup costs and perceived ROI, noting it as cost-effective. Bitdefender, while considered more expensive, is viewed by users as delivering greater ROI through its advanced capabilities and efficiencies. Users generally feel Bitdefender offers better long-term value despite higher initial costs.
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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