Kaspersky Anti-Targeted Attack Platform and Blackberry Dynamics Apps address distinct aspects of cybersecurity and enterprise mobility, respectively. Kaspersky appears favored for its robust protection capabilities, while Blackberry Dynamics Apps stand out for seamless enterprise integration.
Features: Kaspersky Anti-Targeted Attack Platform is praised for advanced threat detection and mitigation, offering a high level of protection. Features include advanced threat detection, mitigation, and comprehensive security. Blackberry Dynamics Apps are noted for effective mobile device management and productivity tools. Key features include seamless enterprise integration, mobile device management, and productivity tools.
Room for Improvement: Users suggest that Kaspersky could improve in performance and interface complexity, requiring more intuitive navigation and streamlined operations. Blackberry Dynamics Apps feedback indicates a need for better stability, integration flexibility, and user support enhancements.
Ease of Deployment and Customer Service: Kaspersky Anti-Targeted Attack Platform is often seen as challenging to deploy with concerns about the complexity of initial setup, yet it provides responsive customer service. Blackberry Dynamics Apps boast straightforward deployment processes, though user reviews suggest varying experiences with customer support quality.
Pricing and ROI: Kaspersky Anti-Targeted Attack Platform is viewed as a premium investment, with users recognizing significant returns in terms of security enhancements. Blackberry Dynamics Apps are considered cost-effective, offering a good balance between cost and productivity gains.
BLACKBERRY DYNAMICS PLATFORM
THE FOUNDATION FOR POWERFUL AND SECURE MOBILE APPS
Built for the most demanding businesses and delivering high availability, disaster recovery and industry-leading scalability, BlackBerry® Dynamics offers an advanced, mature and tested container for mobile apps.
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.
We monitor all Endpoint Detection and Response (EDR) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
