Cyberlytic Case Studies & Use Cases

Overview

  • Detects web injection attacks without the creation or maintenance of firewall rules
  • Cuts through large volumes of data and immediately prioritises high-risk web attacks
  • Protects against critical data loss and increases the efficiency of IT resources
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Use Cases | INSURANCE: Detecting and defending increasing cyber threats

 

Challenge

The insurance industry has become a valuable target to hackers because of the vast amount of data insurers gather and store on individuals and businesses. Insurance companies are digitising their services, meaning a shift to new ways for customers and potential customers to share their data via online portals, mobile-apps, claims tools and other web applications.

The data at risk, typically includes personally identifiable information, medical records and financial details. It is the need to keep up with customer expectations and maintain competitive advantage that has led to investments into new digital systems.

Recent high-profile security breaches highlight the need for defence in depth. Insurers must be do all they can to protect against sophisticated attacks and respond immediately if necessary. According to a survey by Accenture, sixty-one percent of insurers admit it takes “months” to detect breaches. If a cyber-criminal has access to systems for this amount of time, a great deal of damage can be done.

Solution

Advanced, responsive detection is the key to stopping attackers in their tracks and security tools should continually adapt to the changing threat landscape. By using classification to prioritise risk, insurance organisations can take control of their data and reduce response time significantly.

Traditional web application security is no longer an effective way for the insurance industry to protect themselves against sophisticated threats. By using AI to detect attacks and real-time risk assessment to triage alerts, Cyberlytic can help insurers reduce their cyber risk.

Benefits

Advanced Detection: Machine learning classifies web traffic based on threat characteristics, to effectively detect web injection attacks

Risk-based Prioritisation: Immediately cuts through large volumes of data and prioritises high-risk attacks targeting sensitive information

Protection and compliance: Dynamic risk-based reporting demonstrates web threat protection and supports compliance requirements

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