Elastic Security and Kaspersky Anti-Targeted Attack Platform compete in the security software market. Elastic Security has the edge in pricing and ease of deployment, while Kaspersky is preferred for advanced features.
Features:Elastic Security offers comprehensive threat detection, integration capabilities, and competitive customer support. Kaspersky Anti-Targeted Attack Platform excels in advanced threat detection, detailed analytics, and superior security features.
Room for Improvement:Elastic Security users suggest enhanced reporting tools, better analytical tools, and improved user interface. Kaspersky users recommend improvements in system performance, reduced resource consumption, and enhanced scalability.
Ease of Deployment and Customer Service:Elastic Security is straightforward to deploy with prompt support. Kaspersky's deployment process is more complex, but users value their detailed support.
Pricing and ROI:Elastic Security offers cost-effectiveness with good ROI and lower setup costs. Kaspersky is more expensive but justifiable due to its comprehensive features.
| Product | Market Share (%) |
|---|---|
| Elastic Security | 2.4% |
| Kaspersky Anti-Targeted Attack Platform | 0.3% |
| Other | 97.3% |
| Company Size | Count |
|---|---|
| Small Business | 40 |
| Midsize Enterprise | 11 |
| Large Enterprise | 14 |
Elastic Security combines the features of a security information and event management (SIEM) system with endpoint protection, allowing organizations to detect, investigate, and respond to threats in real time. This unified approach helps reduce complexity and improve the efficiency of security operations.
Additional offerings and benefits:
Finally, Elastic Security benefits from a global community of users who contribute to its threat intelligence, helping to enhance its detection capabilities. This collaborative approach ensures that the solution remains on the cutting edge of cybersecurity, with up-to-date information on the latest threats and vulnerabilities.
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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