Imagine two cars are racing. The first is a Ferrari, while the second is a 1999 Ford Taurus. The comparison seems unfair, yet this is one way to view the relationship between today’s government IT environment and IT expectations. The Ferrari represents government employee and citizen expectations for security and reliable data access. The Ford Taurus represents aging government networks that cannot keep pace with a wide variety of emerging security threats. In the current vehicles, it’s an impossible race to win.
However, this scenario doesn’t need to be the case. Machine learning in the network can help detect and negate attacks. Similar to the idea of automatically upgrading the engine in the Ford Taurus, weaving real-time intelligence via machine learning into the network infrastructure can help keep pace with emerging threats. In a world where attacks can occur at any time, the network needs agile defensive and offensive capabilities. With machine learning built into the network, a heightened level of awareness is integrated in to your environment to address zero-day threats as well as other service disruptive anomalies.
While many machine-learning capabilities are still being developed, this is the time for agencies to prepare. Government should take three steps to leverage machine learning for your network within the next few years.