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SDN and Network Security: Auto...Networks are a highly desirable target for attackers, but they are not always protected accordingly. Software-defined networking adds complexity to this problem as it creates environments that are always online (and therefore always exposed), more complex than traditional setups, and more prone to exploitation.
To address network security, organizations must evaluate their networks and ensure that they are protected by automated, sophisticated solutions. Implementations should prioritize automation, both to decrease response times and to conserve resources. Threat detection tools are another important component, but to keep up with attackers, they should be built on AI and machine learning. As the landscape of potential attacks changes, the approach to network security should too.
Software-defined networking (SDN), designed to facilitate managing a network and increase adaptability, has changed substantially in its lifetime. Before the advent of SDN, network control occurred on hardware almost exclusively. While hardware is still involved in network infrastructure, it is controlled through APIs rather than hardware like switches and routers.
Although hardware is a highly effective way of managing a network, physical infrastructure has the problem of limited space that can create challenges. To avoid this problem, large organizations with growing networks sometimes transition to SDN. Software, often cloud-based, controls the network.
While this can reduce costs (or at least make them more predictable) and improve efficiency, there is a tradeoff. Challenges with this type of networking are often security-related. Because software used in SDN is exposed to the web, it is inherently vulnerable to exploitation. Modern network protection can be tricky because of vulnerabilities in APIs as well.
So, organizations need to invest in dynamic, intelligent security solutions. Ideally, these solutions will involve both internal and external protective measures. Useful tools include:
As common practice for controlling and securing networks changes, organizations and their security teams need to stay current. An essential component of network security for modern companies running SDN is a suite of tools that can leverage automation and machine learning to detect and block attacks.
SDN introduces vulnerabilities that traditional infrastructure does not; however, defending these vulnerabilities can be made easier by implementing tools that automate detection and response to attacks. Organizations searching for the right tools should look for a few critical functionalities:
Each of these goes a long way toward helping security teams improve and streamline SDN network security. Although it’s not possible for security teams to catch every attack, implementing automated solutions and threat detection can vastly reduce the number of incidents that teams need to address.
As cloud storage becomes more ubiquitous and applications move to online hosting, companies are increasingly adopting SDN. Hardware-based networks are no longer practical for many organizations, especially those looking to scale up quickly. As more companies come to depend on SDN, security tools and solutions that defend networks automatically will be universally essential.
For many organizations, AI and machine learning are part of future network security. Securing networks with tools that use AI and machine learning helps security teams expand their capabilities. It allows them to focus on large, complex projects that they wouldn’t otherwise have time to build. This in turn improves strategic planning for network security and ultimately makes teams more proactive.
SDN is here to stay. To ensure their networks are secured against the vulnerabilities that come with it, organizations have to begin implementing the most robust network security solutions available. This requires a proactive approach that leverages tools like WAFs and WAAPs, automation, and AI and machine learning.