According to the FBI’s Internet Crime Complaint Center, business email compromise (BEC) has become the most financially damaging internet crime, with business losses totaling over $43 billion between June 2016 and December 2021. How can an exploit that’s been around this long still be so effective? Surprisingly, it’s because of its low-tech strategy.
Most BEC attacks don’t contain the malicious URLs or dangerous attachments that typical signature-based security tools look for. Instead, BEC attacks use social engineering and spear phishing emails to take over or spoof a business email account to hack business processes in order to defraud a business or its partners.
BEC prevention tools harness machine learning (ML) and natural language processing (NLP) to read and recognize the types of phishing emails used in BEC attacks to alert IT departments and users—and keep them out of inboxes altogether. Organizations have also invested thousands in training programs to prevent users from clicking on phishing emails. Yet, despite the best efforts of people and machines, these attacks evolve rapidly and continue to succeed at a rapid clip.
What’s the solution? A continuous real-time feedback loop of ML and crowdsourcing can help beat the clock and stop BEC and spear phishing attacks in minutes or seconds—before they can do their damage.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/14/can-machine-learning-help-prevent-business-email-compromise/?sh=739020533bd3
Most BEC attacks don’t contain the malicious URLs or dangerous attachments that typical signature-based security tools look for. Instead, BEC attacks use social engineering and spear phishing emails to take over or spoof a business email account to hack business processes in order to defraud a business or its partners.
BEC prevention tools harness machine learning (ML) and natural language processing (NLP) to read and recognize the types of phishing emails used in BEC attacks to alert IT departments and users—and keep them out of inboxes altogether. Organizations have also invested thousands in training programs to prevent users from clicking on phishing emails. Yet, despite the best efforts of people and machines, these attacks evolve rapidly and continue to succeed at a rapid clip.
What’s the solution? A continuous real-time feedback loop of ML and crowdsourcing can help beat the clock and stop BEC and spear phishing attacks in minutes or seconds—before they can do their damage.
Continue reading: https://www.forbes.com/sites/forbestechcouncil/2022/10/14/can-machine-learning-help-prevent-business-email-compromise/?sh=739020533bd3
