Virtualization Technology News and Information
White Papers
RSS
White Papers Search Results
Showing 1 - 1 of 1 white paper, page 1 of 1.
Preparing for the Safe Adoption of Agentic AI in Networking and Security
Agentic AI can autonomously patch vulnerabilities, reroute traffic, and pre-empt outages in enterprise networks, yet its power poses new operational and security risks. The article argues that safe adoption depends on a high-quality data foundation and continuous verification through network digital twins, which mirror topology, policies and state. Acting as guardrails, twins let CIOs capture agentic AI’s efficiency and agility while preventing unintended consequences.

Forward Networks co-founder Nikhil Handigol explains how organisations can harness agentic AI in networking and security without jeopardising reliability.

What makes AI “agentic” – Unlike chatbots, agentic systems pursue goals, choose their own tools and act autonomously. Market researchers expect the segment to surge from US $5.1 billion in 2025 to more than US $47 billion by 2030, and Gartner predicts that one-third of enterprise software will embed such capabilities by 2028. 

Upside for networks – Agents can:

remediate newly disclosed vulnerabilities or block malicious traffic,
resolve connectivity issues and reroute flows around failures,
analyse patterns to predict and prevent impending outages.
 
These abilities promise dramatic efficiency gains for short-staffed IT teams and better user experience.        
Risks to manage – Because an agent can act without human oversight, a mis-trained model or bad data could break compliance, introduce downtime or even cause physical harm. “Trust but verify” must therefore guide every deployment.

Build the data bedrock – 
Autonomy is only as sound as the data it relies on. Enterprises need a complete, accurate record of every device, configuration and packet path. A network digital twin—a mathematically precise, continuously updated software replica of the live environment—provides that single source of truth.
 
Digital twins as guardrails

Pre-change simulation: Before any AI-driven or manual change, test it exhaustively in the twin to catch policy violations, compliance breaks or connectivity loss.

Continuous verification: Twin-based rules monitor live behaviour and alert operators to drift or emergent problems. With this safety layer, CIOs can accelerate agentic AI adoption while preserving control.

Bottom line – Agentic AI’s promise in networking is real, but benefits accrue only if robust data pipelines and digital-twin guardrails are in place. When those prerequisites are met, AI agents can handle routine operations and incident response, freeing humans for higher-value work and increasing organisational resilience.