Cybercriminals never stop innovating. Their increased use of automated and scripted attacks that increase speed and scale makes them more sophisticated and dangerous than ever. And because of the volume, velocity and sophistication of today’s global threat landscape, enterprises must respond in real-time and at machine speeds to effectively counter these aggressive attacks. Machine learning and artificial intelligence can help deliver better, more effective threat intelligence.

As we move through 2020, AI has started increasing its capacity to detect attack patterns using a combination of threat intelligence feeds delivered by a variety of external sources, ranging from vendors to industry consortiums, and distributed sensors and learning nodes that gather information about the threats and probes targeting the edges of the networks.

This new form of distributed AI relies on something called federated machine learning. Instead of relying on a single, centralized AI system to process data and initiate a response to threats (like in centralized AI), these regional machine learning nodes will respond to threats autonomously using existing threat intelligence. Just as white blood cells automatically react to an infection, and clotting systems respond to a cut without requiring the brain to initiate those responses, these interconnected systems can see, correlate, track, and prepare for threats as they move through cyberspace by sharing information across the network, enabling local nodes to respond with increasing accuracy and efficiency to events by leveraging continually updated response models.

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