The technology environment in which businesses and society operate is becoming increasingly complex. Smart cities now rely on tools like AI to coordinate things like traffic, power, and emergency systems. Distributed manufacturing platforms use it to respond to consumer demand in near-real-time. Massive data centers depend on intelligent systems to process elephant flows of information used for complex computations and modeling. And financial trading systems need it to conduct microsecond transactions based on the analysis of vast feeds of data.
But that’s just the start. To keep up, AI is going to have to expand even faster and farther than ever before. 5G, combined with increasingly intelligent endpoint devices and rich media services, will soon be able to create dynamic, ad hoc edge networks that will fundamentally change how data is generated, distributed, and used. Add billions of semi-intelligent IoT devices and dynamic edge routing resources, and we are on the verge of yet another dramatic sea change affecting how we work and live.
Our transition to new, more complex environments relies on hyperspeed and hyperscalability—which means that critical decisions need to be made instantly, seamlessly, and consistently. This is especially true when it comes to security. As a result, organizations are turning to machine learning and AI to manage their dynamic, complex, and often temporary networks. Combining AI-enhanced security systems with reliable, real-time threat intelligence and networking technologies enables a security-driven network approach that can function as a single system.
Such a system—one designed to keep up with and protect these increasingly complex and dynamic networks—relies on tiers of security. It starts with systems woven into the fabric of the network, such as segmentation, behavioral analytics, and zero-trust network access, that work continuously to ensure that the traffic entering and moving across the network is free from threats.
Next, by replacing traditional sensors with learning nodes, a distributed security system is not only able to gather threat information but also function as a first line of defense. It does this by using stored knowledge augmented by machine learning to detect a threat and provide a coarse-grain response.
These systems also share any threat intelligence they gather with the central AI system to ensure that a more refined threat response can be deployed to avoid interfering with critical business processes. This central AI system sees more devices, evaluates more data—from both internal and external sources, and makes more decisions per second than any team of security analysts. It then uses this information to quickly determine if observed behaviors match known attack patterns, and more importantly, to identify and stop threats it may have never seen before.
And since it is the central brain of the security system, it also needs to be able to marshal defenses deployed anywhere across the network—whether security or networking solutions—to neutralize an attack and close attack vectors so it doesn’t happen again. And it needs to do all of this at digital speeds. This requires combining AI-driven threat intelligence with detailed items such as playbooks and external threat feeds with an integrated security platform designed to provide protection and visibility across the entire digital infrastructure, from endpoints to the edge and from the data center to the multi-cloud.
Given the complex nature of the networks we are building today, the reality is that the level of awareness and response needed to defend users, devices, and data simply cannot be achieved by humans, no matter how skilled. AI-enhanced systems are essential for protecting our digital society as we move forward.