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Aviz details next-gen DPI engine for AI visibility in enterprise networks

Source Decision Insights Coverage Published December 1, 2025 Companies 3
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3 companies named across 18 categories, one of 71 articles referencing DEEP. Previous coverage: Aviz Open Packet Broker details deep packet inspection with User Defined Filtering (Nov 2025).

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Enterprises face increasing challenges in maintaining visibility over Artificial Intelligence (AI) traffic as traditional Deep Packet Inspection (DPI) tools fail to monitor encrypted and API-based communications. Aviz addresses this gap with its next-generation Deep Packet Inspection (DPI) engine embedded in the Aviz Service Node, which enhances real-time insight into both authorized and unauthorized AI activity across complex network environments, aiding IT and security teams in managing associated risks.

Research overview

AI integration within enterprise applications has accelerated, with projections indicating widespread adoption of conversational AI and unapproved AI use by employees. Security reports highlight that a large portion of AI tools in use carry elevated risk levels, underscoring growing threats including potential data exposure and daily AI-driven attacks expected by security professionals.

This expansion of AI applications across distributed infrastructures introduces visibility concerns that traditional monitoring frameworks, reliant on static network attributes, are unable to address effectively, especially given the prevalence of encrypted and dynamic API-driven traffic.

Key findings on shadow AI risks

Shadow AI comprises unauthorized AI tools employed within organizations without IT oversight, creating potential blind spots. These tools pose risks including unintended data leaks, manipulation through prompt injection, violations of compliance requirements, and complex network tracking challenges across edge and hybrid cloud setups.

Traditional Deep Packet Inspection (DPI) methods, dependent on port and protocol detection, lack the capability to interpret the encrypted or variable nature of such AI-driven communications, necessitating advanced inspection solutions.

Technical breakdown of the Aviz Service Node

The Aviz Service Node is a software appliance designed to provide comprehensive visibility into AI-related network traffic across data centers, edge locations, and hybrid cloud environments. Central to its functionality is a DPI engine that performs payload-centric analysis beyond conventional header inspection.

This engine leverages multi-dimensional packet analysis to identify applications accurately, even under encryption or Application Programming Interface (API) complexity, enabling differentiation between sanctioned AI applications and shadow AI occurrences, thereby supporting network governance.

Operational impact of real-time telemetry and visualization

Data captured by the Aviz Service Node feeds into an analytics and visualization platform known as the Aviz Elastic Node, providing centralized monitoring capabilities. This enables network and security personnel to observe AI traffic flows comprehensively and assess user-level interactions through consolidated dashboards.

The approach facilitates enhanced situational awareness necessary for managing AI workload distribution and detecting anomalous patterns within enterprise infrastructures.

Leadership perspective on unified AI observability

Deploying the Aviz Service Node and its DPI technology allows organizations to encompass both approved and unapproved AI activity within a unified monitoring framework. This consolidates control mechanisms and supports compliance procedures across diverse networking layers.

By integrating real-time packet examination with scalable analytics, the solution equips enterprises to adapt observability practices suitable for the evolving complexity and volume of AI traffic within their operational contexts.

This Blog Signals brief is a factual summary of the vendor blog detailing Aviz's approach to AI visibility challenges in enterprise networks.

Blog post, originally published by Ram Mohan Hariprasad at Aviz Networks.

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