Page 338 - Cyber Defense eMagazine September 2025

Differing Views on AI Implementation

A closer look into the research reveals that the levels of reported AI adoption vary considerably between
leadership teams and network engineers. CIOS and CSOs had an optimistic view of AI adoption, with a
majority citing full implementation. However, network engineers reported a lower level of integration, with
only 42% claiming their businesses had fully implemented AI. Another important finding concerned the
level of AI integration into existing cybersecurity infrastructure. Only 28% of surveyed engineers said AI
had been integrated fully, while 63% reported integration to some degree but not fully.

One reason for this difference could be that leadership focuses on broader strategic goals, whereas
network engineers assess AI based on its direct impact on network performance and security. Engineers
view the world through a granular lens, while leadership focuses on broader strategic goals and
generalities. Put another way, if business leaders were asked, “What’s on McDonald’s menu?” They’d
say “hamburgers” or “burgers and fries." Engineers would probably look up the menu and respond with,
“burgers, fries, chicken, salads, desserts, etc.”

Another interesting point of contention had to do with who should lead AI rollouts in network management.
Some proposed roles included the CIO, CSO, head of IT security, or even an AI/ machine learning
specialist. This discrepancy further highlighted the diverse priorities and areas of expertise within
businesses.

Organizations should establish shared AI deployment frameworks to align clear goals, standard metrics,
and mutual input across teams to bridge these gaps. Encouraging consensus in the early stage helps
align expectations, leading to a more cohesive implementation strategy.

Resource Allocation and AI Integration Challenges

There are a variety of network management and security applications possible through AI. For example,
organizations can use AI to enhance anomaly and threat detection, specifically malware and network
anomalies, ultimately preventing breaches. Nevertheless, differing priorities emerged between leadership
and technical teams when considering resource allocation for AI-driven network management. The report
found that nearly 70% of engineers believe AI will enhance their organization’s ability to respond to
cybersecurity incidents. However, 66% of CIOs and CSOs allocated only 4-10% of their IT and
cybersecurity budget to AI for network management.

In addition to these disagreements between leadership and technical teams, organizations face several
hurdles to AI implementation. The most commonly cited challenge from network engineers was the high
initial investment required to deploy AI technologies, with 29% of respondents identifying this as the top
barrier to AI adoption. This sentiment from engineers mirrors their belief that there aren’t enough
resources getting allocated to support AI implementation. A close second challenge at 28% was
regulatory compliance, followed by the need for skilled professionals to manage AI-driven systems.
Concerning the latter, 31% of network engineers said they plan to prioritize training and upskilling efforts
with IT staff to manage and integrate AI technologies effectively.

Cyber Defense eMagazine – September 2025 Edition 338
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