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5G mobile technology will completely transform global telecommunication networks—and mission and business operations. It will forge bonds between physical and digital devices, deliver tremendous performance advantages, help reduce costs, generate massive amounts of data, and enable a connected world on an unprecedented scale. What’s more, it will drive federal and defense organizations to expand their ongoing security monitoring activities to the private 5G networks they’re developing for a wide range of missions and use cases.

5G’s changes will translate to new vulnerabilities and attack vectors. The deployment of mission-critical Internet of Things (IoT) devices—sensors and controllers, for example—will generate huge volumes of data vulnerable to theft, manipulation, and destruction. 

Now, baseline 5G architectures include some security. And organizations are deploying scores of complex cybersecurity tools that emit and absorb data on a massive scale. But many security teams are unable to outpace advanced cyber threats because they can’t make the most of their data. Organizations are already drowning in their own data in traditional networks—and the problem will be compounded with the launch of private 5G networks.

Congress has directed the Defense Information Systems Agency (DISA) and U.S. Cyber Command (CYBERCOM) to develop a 5G continuous monitoring capability for non-commercial Department of Defense (DOD) networks. Hence, operators of private 5G networks should implement a continuous monitoring strategy. These organizations need capabilities to provide security monitoring of the network and real-time feedback on the current security posture of the network.

In developing these capabilities, organizations should also embrace data-driven cybersecurity by figuring out how to extract, normalize, and apply increasingly complex and diverse data sets to accelerate security operations—ideally faster than adversaries. To that end, organizations should leverage security analytics, artificial intelligence (AI), machine learning (ML), and automation to collect, manage, and use security information to full advantage. These steps can enable security teams to detect anomalies tied to advanced cyber threats.