These smart AI algorithms are capable of dynamically analyzing past attacks and safeguard future instances by studying common pattern. These algorithms go into analyzing large volumes of data that otherwise wouldn’t be possible for individual cybersecurity researchers to process. Expanding the use of machine learning technology in cybersecurity can be a game changer for companies looking to safeguard systems/devices/networks.
Before machine learning, security teams were using blunter instruments. If an anonymous user tried logging in from an unknown location, their attempt would get blocked. Or in some cases, spam e-mails featuring misspelling of words would get automatically blocked.
For a product like Gmail, where millions of users log in everyday, the amount of traffic that the security team needs to look is too large for them to write rules. Machine Learning has enabled these security teams to analyze large sets of data and detect and prevent unauthorized logins.
Tech companies are providing same technology to customers as well. Amazon’s Macie service is one good example of how machine learning is being used to identify sensitive data. Another positive aspect is the fact that machine learning powered systems will work in all instances and will be far more accurate in detecting threats in comparison to the traditional ways of fighting cybercrime.