Artificial intelligence (AI)’s ability to analyze vast datasets, predict potential failures, and automate routine tasks makes it indispensable to cybersecurity—specifically network security. AI will play a vital role in securing networks, helping operators detect suspicious activity, identify breaches, and even automate incident response through predefined security protocols, reducing the time it takes to mitigate threats. While businesses are eager to accelerate AI adoption, many struggle with implementation because of several misalignments between their network engineers and executives.
A research report titled “AI-Driven Network Management—A Global Perspective on Adoption and Innovation,” which surveyed 513 Chief Information Officers (CIOs) and Chief Security Officers (CSOs) and 508 network engineers across Western countries, revealed key differences between leadership and technical teams concerning the implementation of AI and appropriate resource allocation. These discrepancies are not inconsequential, and organizations must align both parties to fully realize AI’s potential in securing their networks.
Disparities in the Level of AI Implementation
One of the main areas of disagreement between leadership and network engineers was the level of reported AI adoption. According to the survey results, 94% of CIOs and CSOs said that their companies had begun implementing AI, with 58% reporting a complete AI implementation. Although CIOs and CSOs had an optimistic view of their AI deployments, network engineers did not. In fact, only 42% claimed their organizations had fully implemented AI. Network engineers reported that 63% of organizations integrated AI into their cybersecurity infrastructure to some degree, while only 28% reported full integration.
These findings suggest that while most businesses see the value of AI for cybersecurity, there remains a considerable gap between partial and full adoption. Moreover, the results demonstrate that leadership and technical teams judge the success or scope of an AI implementation much differently. CIOs and CSOs look at broader strategic goals and applications of AI; however, network engineers tasked with addressing immediate operational challenges focus more on AI’s direct impact on enhancing network performance and security, which hints at the incongruity.
Expectations on Resource Allocation
Another point of misalignment that emerged from the survey was the allocation of resources for AI and cybersecurity. Specifically, network engineers’ expectations did not match the actual amount invested by CIOs and CSOs. For example, 66% of CIOs and CSOs allocated a substantial portion of their budget to AI and cybersecurity. Nevertheless, 70% of surveyed network engineers expressed that the level of investment change from one year to the next (meaning the change in additional resources dedicated to AI implementation) would not be enough to meet their organization’s business goals.
Engineers’ views on the key challenges hindering AI deployments reflect their feeling that their companies aren’t allocating enough resources to support AI implementation. When asked what they thought the most common challenges would be to AI implementation, 29% of network engineers pointed to the high initial investment required to deploy AI technologies, making it the most cited barrier. This hurdle especially affects smaller organizations lacking the financial resources for full AI adoption.
Businesses can address this challenge by adopting a step-by-step approach to AI implementation, beginning with partial implementation and progressively scaling. This incremental approach would help engineers gain more experience with the technology as they test its capabilities on a smaller scale. Most importantly, these small-scale tests would allow engineers to provide their company leaders with proofs of concept, making it easier for these decision-makers to see the tangible value of committing more resources.
The Need for More Skilled Professionals
A major obstacle impeding AI implementation was the need for skilled professionals to manage AI-driven systems. To overcome this challenge, 31% of network engineers plan to prioritize training and upskilling efforts with IT staff. In this context, out of band technology would be highly beneficial, empowering network teams to manage AI-integrated networks from remote locations. These technologies also ensure that AI tools remain accessible and operational even during network outages brought about by cyberattacks. Furthermore, the use of out of band solutions will enhance technical teams’ understanding of AI applications within network management.
Out of band technologies enable engineers to create an always-on independent management plane separate from the production network, helping improve the implementation of AI and ultimately bringing engineers to the same confidence levels as leadership. Consider that 32% of those CIOs or CSOs whose organizations are either planning to or have partially implemented AI for network management want to prepare by deploying continuous monitoring and real-time analytics. Out of band technologies deliver independent, secure, and remote access for provisioning, orchestration, management, and remediation, making them invaluable solutions for those network engineers in the day-to-day network trenches.
Aligning on Shared Objectives and Goals
Despite differing perspectives, both CIOs/CISOs and network engineers agree that AI will enhance network management and cybersecurity. Building on this common ground, organizations should foster collaboration between both groups to develop a unified AI-driven strategy that outlines objectives and shared goals and prioritizes key initiatives such as IT training, network resilience, and real-time analytics. By doing so, businesses can maximize AI’s potential to strengthen security and streamline network management.
