In March 2018, Orbitz announced a major security breach that likely exposed information from at least 880,000 customer payment cards, including people’s names, dates of birth, email addresses, street addresses, and genders. The breach occurred sometime between October and December 2017 and involved patrons who used the service in 2016 and 2017.
The Orbitz breach is only one in a long list of cyber hacking and attack events worldwide. As a result, organizations around the world are increasing their investment in cybersecurity. According to Gartner, global spending on cybersecurity will reach $96 billion in 2018. Even though the most common source of a data breach involves the misuse of user credentials, Gartner found that companies tend to spend more on anti-virus protections, malware detection and website firewalls.
In the context of the growing demand for cybersecurity measures, machine learning approaches are helping strengthen authentication and screening techniques that will improve security without interfering in the customer experience, a common concern reported by CEOs. Here are 4 advanced technologies being applied by artificial intelligence using machine learning to help improve enterprise security.
Machines are being used to identify spam and phishing emails based on complex content, sender information, identifying malware, etc.
Machines are able to quickly and efficiently identify unusual activity, data or processes that are likely to be fraudulent
Machines are able to convert “natural” language text into structured intelligence to make it easier to process
Machines are adept at processing data and identifying patterns in order to make predictions and recognize outliers.
Machines are now able to create relationships in data with inputs from various sources, powering faster analytics and helping companies make more informed security decisions. Machines are able to automatically highlight attack methods and cut through the noise to deliver relevant and actionable threat intelligence.
Because AI is able to learn, it can combine entities within context in order to deliver only relevant intelligence to human analysts. For example, relevant details can be analyzed such as:
Cyber security intelligence is able to alert security teams when there is a suspicious event or an event is predicted. The machine will flag a specific vulnerability which has generated an increase in concerning activity. Artificial intelligence, and corresponding technologies that include machine learning, knowledge representations and rule-based systems are being leveraged to help reduce growing security risks posed by the increase in digital applications worldwide.
For more information on mitigating your cyber security risks, and cyber liability insurance, contact us here.