As Published in Forbes, Jan. 26, 2020
AI, Machine Learning And The Race To Improve Cybersecurity
The majority of Information Security teams’ cybersecurity analysts are overwhelmed today analyzing security logs, thwarting breach attempts, investigating potential fraud incidents and more. 69% of senior executives believe AI and machine learning are necessary to respond to cyberattacks according to the Capgemini study, Reinventing Cybersecurity with Artificial Intelligence. The following graphic compares the percentage of organizations by industry who are relying on AI to improve their cybersecurity. 80% of telecommunications executives believe their organization would not be able to respond to cyberattacks without AI, with the average being 69% of all enterprises across seven industries.
The bottom line is all organizations have an urgent need to improve endpoint security and resilience, protect privileged access credentials, reduce fraudulent transactions, and secure every mobile device applying Zero Trust principles. Many are relying on AI and machine learning to determine if login and resource requests are legitimate or not based on past behavioral and system use patterns. Several of the top ten companies to watch take into account a diverse series of indicators to determine if a login attempt, transaction, or system resource request is legitimate or not. They’re able to assign a single score to a specific event and predict if it’s legitimate or not. Kount’s Omniscore is an example of how AI and ML are providing fraud analysts with insights needed to reduce false positives and improve customer buying experiences while thwarting fraud.