A UNIQUE APPROACH TO DETECTING AND PREVENTING WEB-BASED ATTACKS

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 effectiveness of each attack.  This information is translated into a risk score to prioritise and initiate incident response.

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

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We are delighted to be working with Cyberlytic. Their innovative approach to web application security is disruptive and will challenge more established players to think about their own approach.
Will SempleCyber Threat Detection and Response

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We are excited to have Cyberlytic join our IDEA London programme, they are complementary to our current security portfolio and we are excited about adding them to our Solution Partner programme.
Tom KneenHead of Business Development

Gain the upper hand over attackers

Machine learning analytics reduces the demand on security teams, pin-points high-risk attacks and for the first time, keeps you one step ahead of the attacker.

No Firewall Rules
Self Learning
Risk Based Triage
Active Defence

CYBER SUPPLIER  TO   hm_gov_web_logo-430x75

CYBER THREAT PROFILER

Patented classification approach that provides a proactive, real-time risk assessment of web-based cyber-attacks.  Enables security teams to detect, prioritise and respond immediately to high-risk attacks.

CYBER THREAT DEFENDER

An intelligent, self-learning web application firewall that does not require human intervention or rule management to prevent the most sophisticated web-based attacks.

BOOK A DEMO OR ASK A QUESTION

Use the form below to get in touch, we're looking forward to hearing from you.

CONTACT US

Email: info@cyberlytic.com

Telephone (US): +1 289 707 3242

Telephone (UK):+44(0) 203 290 0011

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