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As corporations, remote workers and major brands migrate their operations online, security teams need strong support from intelligent machines to work successfully and protect their networks from cyber attacks. AI cybersecurity uses machine learning (ML) to identify threats and take decisive action before the damage is done. Incorporating ML into your existing systems reduces the number of false positive alerts and frees up time for security analysts to focus on analyzing risks and taking immediate action.

Using the power of ML to automate research tasks and deliver curated analysis makes it possible for security teams to quickly identify the most important risks, while also eliminating manual steps to investigate and prioritize a new attack. ML algorithms are used in every aspect of cybersecurity, from classification to identifying patterns of behavior that might indicate an anomaly. In addition, ML can analyze data and connect disparate signals to create a full picture of an attack.

According to TechRepublic, a midsized company gets 200,000 alerts every day. Humans can’t keep up with the volume of threats, which means some will go unnoticed and cause damage to the network. AI can be more effective in detecting unknown threats because it can look at the totality of the data coming into a network and detect the most dangerous information.

Morpheus leverages the power of ML to observe all data flows in a network, apply AI inferencing to each piece of data and packet, and flag when patterns shift. This data can then be compared to a library of known malware, ransomware, or phishing attacks and automatically analyzed for signs of compromise.