Cyberlytic Case Studies & Use Cases


  • 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 | Protecting sensitive HEALTHCARE records Challenge



The global healthcare industry is one of the most targeted industries for cybercrime. In the UK, it accounts for nearly half of all breaches, showing just how attractive it is to cybercriminals.

Healthcare providers are innovating through digitisation, but security concerns are continually emerging as online services are used more frequently. Consumers are gaining the ability to manage many aspects of their healthcare online through patient portals for the first time, as well as being offered connected medical devices. Access to web services needs to be constant, whilst being protected from web attacks.

Hackers try to obtain medical records in order to sell them on the dark web, where medical information can be worth ten times more than credit card numbers. Most healthcare organisations have a public website and portal where personal details, medical records and financial information is shared. It is imperative to secure the website and highly valuable data that sits behind it.

Whilst there are many benefits to digitisation, online portals and digital healthcare significantly increases the risk of cyberattack, potentially resulting in financial and reputational harm.


Cyberlytic has developed a revolutionary approach to detecting and preventing web-based attacks, such as SQL injection(SQLi) and cross-site scripting (XSS) for healthcare organisation. Our software uses machine learning to classify attack data, identify threat patterns and detect anomalies. By analysing web server traffic in real-time, our software detects and immediately determines the sophistication, capability and Results: effectiveness of each attack. This information is translated into a risk score to prioritise incident response or initiate an automated response with our Defender product.

Our patented classification approach is far more effective at identifying attacks than traditional signature-based security solutions and adapts to new or evolving threats without requiring any manual intervention.


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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