Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Rekha Shenoy, CEO, BackBox
Artificial Intelligence (AI) has quickly evolved from an experimental technology into a critical driver of transformation across nearly every sector of IT. In network operations and cybersecurity, AI has become both an enabler and a disruptor. It accelerates innovation but also intensifies threats.
This allows hackers to exploit vulnerabilities in days, which was once something that took months or years to weaponize. At the same time, AI empowers defenders by helping teams detect anomalies faster, optimize resources, and focus on higher-value remediation work. The dual nature of AI in enterprise environments is reshaping how organizations think about resilience, trust, and automation.
Over the next five years, AI will increasingly serve as an advisory layer in network automation, supporting, rather than replacing, human expertise. Successful vendors will be those who build trustworthy AI systems that prioritize transparency, safety, and explainability over opaque “black box” automation.
The Tension Between Promise and Risk
The rapid adoption of AI across enterprise IT has brought undeniable efficiency gains-but also serious concerns. AI models can hallucinate, misinterpret data, or propagate bias. When the stakes involve uptime, service delivery, or network security, even small errors can have serious consequences.
Transparency remains a major issue. Many AI tools rely on proprietary or pooled datasets that may inadvertently expose sensitive or anonymized customer information. For network automation, this raises valid questions: What data is being used to train these models? Where does it come from? And who can see the results?
A more responsible approach uses AI to analyze publicly available, verified information, such as vendor vulnerability advisories or government databases, while ensuring that customer data remains private and protected. This balance between utility and accountability is key to building user trust.
What Responsible AI Will Look Like
As network automation becomes increasingly AI-driven, the leaders in this field will be those who take a human-centered approach to design. That means positioning AI as a trusted advisor rather than an autonomous operator.
Four priorities will define trustworthy AI development in the datacenter space:
- AI must prioritize the protection of users and their infrastructure.
- Models should produce repeatable, verifiable results that improve over time.
- Automation should support, not replace, skilled engineers.
- Vendors must clearly explain how their AI generates insights, what data it uses, and how outputs are validated.
If these principles guide the next generation of AI-enabled automation, organizations will gain not just faster results but confidence in the integrity of the process itself.
AI in Action: The BackBox Perspective
At BackBox, we’ve adopted AI-not as a trend, but as a tool for solving problems in a meaningful way. Our AI initiatives focus on data enrichment and intelligent automation, enabling network teams to make faster and more accurate decisions.
BackBox utilizes AI to provide a unified, trusted view of network vulnerabilities, integrating data from the National Vulnerability Database (NVD), the Cybersecurity and Infrastructure Security Agency’s (CISA) Known Exploited Vulnerabilities (KEV) catalog, and vendor advisories. This enriched feed consolidates diverse sources into a normalized, prioritized list of vulnerabilities, showing which are actively exploited and where they apply across an organization’s infrastructure.
Through AI-assisted CVE Workaround Enhancements, the platform scans vendor workarounds and converts them into a consistent, readable format. This allows engineers to quickly identify and implement the right fixes without combing through inconsistent documentation.
The Road Ahead
AI will continue to expand its role in modern datacenter and network environments. But its success won’t depend on speed or novelty-it will depend on trust. Enterprises will demand to see how AI makes decisions, why it recommends certain actions, and what data supports its conclusions.
The next era of network automation will belong to the companies that build explainable, ethical, and accountable AI. When done right, AI won’t just predict the future of networks-it will help secure them.
##
ABOUT THE AUTHOR
Rekha Shenoy, CEO at BackBox, brings over 25 years of B2B tech leadership experience. Formerly Chief Product Officer at Syndio and Chief Revenue Officer at Spirion, she drives product development and market expansion in security-centric automation, with prior experience orchestrating buy and sell-side acquisitions, notably at Tripwire and BMC Software.
