Predictive Analytics for Cyber Threats to Enhance Security in the Cyber Supply Chain

Abstract

Using predictive analytics is a key part of making the computer supply chain safer because it helps companies find and stop threats before they happen. Predictive analytics uses complex algorithms and machine learning to look through huge amounts of data for trends and outliers that could point to cyber dangers. Businesses can stay ahead of cyber attackers and keep their digital assets safe with this method. In the online supply chain, one of the best things about prediction analytics is that it can find new threats before they become full-blown attacks. Predictive analytics looks at past data, current trends, and other factors to be able to guess possible computer dangers and weak spots. In turn, this lets businesses take strategic steps to lower these risks, like fixing security holes or adding more protections. Predictive analytics can also improve the cyber supply chain's ability to respond to incidents. Predictive analytics looks at data from many places, like network logs, endpoint devices, and security monitors, to help find and rank possible security events. This lets companies react quickly and effectively to cyberattacks to lessen their effects. The prediction analytics is a great way to make the online supply chain safer. Using advanced algorithms and machine learning, businesses can find and stop cyber dangers before they happen, keep their digital assets safe, and boost their total security.