Forward Networks Presents at AI Infrastructure Field Day

AI is reshaping network operations with Forward Networks

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Networks are more critical than ever, yet their operational models have remained largely unchanged for decades, relying on manual CLIs, spreadsheets, and often outdated diagrams. This traditional approach struggles to keep pace with the rapid evolution of applications, end users, and adversaries, resulting in extremely high operational workloads due to the sheer complexity of modern networking. Enterprise networks are being fundamentally reshaped by AI, cloud, and data-intensive workloads, requiring reliable, high-performance, and secure infrastructure. While companies have increased visibility into network packets and application performance, these methods cannot answer foundational questions about network inventory, connectivity, security posture, compliance, or whether network behavior aligns with its intended design, akin to reactively treating symptoms in medicine without full diagnostic imaging.

Forward Networks addresses this challenge by pioneering a shift from reactive symptom measurement to a proactive, comprehensive understanding of the network, analogous to full-body imaging scans in medicine. Twelve years ago, the company developed a mathematically accurate digital twin of the network, building on PhD research that broke down complex network behaviors into mathematical primitives. This mathematical underpinning enables the creation of provable assurances for critical aspects such as compliance, security, reliability, and availability. The digital twin is built by exhaustively collecting configuration and protocol state data from every packet-moving device across on-premise infrastructure (switches, routers, firewalls, load balancers, Wi-Fi, SD-WAN) and cloud environments (AWS, Google, Microsoft, IBM), along with security vulnerability data, performance metrics, and contextual business data. This rigorous modeling even accounts for potential device behaviors under varying conditions and firmware changes through extensive testing.

This centralized, mathematically sound digital twin provides instant, accurate answers to a wide range of questions, from inventory and connectivity to security properties, compliance, and the impact of changes. It facilitates a major operational shift by eliminating the “toil” of manual data extraction and cross-referencing, enabling network, security, and compliance teams to collaborate around a single source of truth. Forward Networks clients reportedly experience over $14 million in annual ROI and significantly improved operational confidence. Building on this robust foundation, the company has now introduced Forward AI, a conversational interface that enables users to ask complex questions in plain English and receive trusted answers, making network knowledge effortless. This innovation leverages the digital twin’s “ground truth” to support safe, trusted, and agentic operations, human-supervised, fundamentally transforming how organizations interact with and manage their critical networks.

Personnel: David Erickson

Introducing Forward AI, chat with your network, with Forward Networks

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Forward Networks’ foundational technology is a “digital twin of the network,” which serves as a behavioral source of truth. This software platform connects to all network devices, collecting configuration and state data to build a behaviorally accurate model. It transforms raw, vendor-specific data into a queryable, vendor-independent model, analyzes all possible network behaviors, and proactively traces every conceivable packet path to determine delivery, drops, and the underlying causes. This capability goes beyond mere monitoring by enabling the network’s properties to be provably validated, including connectivity readiness and security isolation between regions. The platform collects extensive multi-vendor, multi-protocol data at scale, including tens of thousands of devices, and organizes it into a hierarchical stack of raw, normalized, behavioral, and contextual data to enable deep insights.

The company identified an “operational gap” in which network and security teams struggle to translate their goals into actionable information from disparate sources and to manage complex, multi-step workflows. Envisioning “agentic operations” where AI assists with routine tasks, Forward Networks emphasizes the critical need for robust data and trustworthy AI outputs. To address this, they introduce Forward AI, a conversational agentic system powered by the network digital twin. Forward AI provides a plain English interface, allowing operators to ask questions about devices, hosts, subnets, packet paths, and vulnerabilities, effectively bridging the gap between human intent and the complex underlying network data. While designed with agentic capabilities, the initial focus is on enabling users to gain trusted insights necessary for informed actions.

Personnel: Nikhil Handigol

Forward AI Demo Troubleshooting Network Operations with Forward Networks

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Nikhil Handigol’s presentation showcases how Forward AI revolutionizes network operations troubleshooting. Before diving into the AI capabilities, Handigol provided a concise tour of the foundational Forward Enterprise platform. This robust, deterministic software connects to all network devices across hybrid multi-cloud and multi-vendor environments to create detailed, point-in-time snapshots of network configuration and behavior. The platform offers various analytical views, including graphical topology, inventory dashboards, vulnerability assessments, blast radius analysis, and precise path tracing. A key component is the Network Query Engine (NQE), which transforms raw configuration data into a normalized, hierarchical data model and supports queries via a SQL-like language, enabling users to extract specific network insights and verify compliance against predefined checks, triggering alerts when discrepancies arise.

The core demonstration focused on how Forward AI, as a conversational interface, streamlines resolving common network connectivity issues. By ingesting a service ticket describing a host’s inability to reach a database server over SSH, the AI agent dynamically constructs and executes a diagnostic plan. This plan involves gathering context about the involved hosts and performing a precise path trace through the network’s digital twin. In the scenario presented, Forward AI swiftly identified the issue: SSH traffic was blocked by a specific firewall due to an explicit Access Control List (ACL) deny rule. Crucially, the system provides a clear, “bottom line up front” diagnosis, supported by detailed explanations of the blocking device, the rule, and the full traffic path, all substantiated with direct links to the relevant “evidence” views within the Forward application, enhancing transparency and user trust.

Extending its utility, Forward AI can also generate proposed Command Line Interface (CLI) commands as a starting point for resolving identified issues, such as creating a new firewall security policy. Nikhil strongly emphasized that these generated fixes are for planning purposes only and require human validation and adherence to established operational change procedures, underscoring that the system does not autonomously execute changes. Discussions highlighted essential guardrails, including the AI’s ability to reject unanswerable requests and the enforcement of Role-Based Access Control (RBAC) to restrict data access and command generation based on user permissions. While a feedback mechanism (thumbs up/down) is in place to gather user input for continuous improvement, future iterations may incorporate business policies into AI recommendations and develop simulation capabilities within the digital twin before deploying changes to production, further building trust and enhancing automation.

Personnel: Nikhil Handigol

Forward AI Demo – Risk Mitigation and Security with Forward Networks

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The presentation by Forward Networks demonstrated how their Forward AI platform addresses the critical security challenge of mitigating risks posed by vulnerable hosts, specifically a host named `batch 01` with unpatchable critical vulnerabilities. Traditionally, blocking internet access for such a host involves a laborious, hop-by-hop network analysis to identify firewalls and their configurations, a process that is time-consuming, prone to errors, and difficult to scale across multiple vulnerable devices. Failure to implement these blocks correctly could leave the network exposed, underscoring the need for an automated, reliable solution.

Forward AI streamlines this process significantly. Upon receiving a natural-language query such as “What firewalls do I have to block in order to remove access to the internet for host batch 01?”, the system first gathers context about the host’s vulnerabilities. It then performs a comprehensive path trace from the vulnerable host’s IP address to the entire internet (`0.0.0.0/0`), identifying all egress paths. The AI pinpoints the specific firewall (e.g., `SJC building one FW01`) and the exact access control rule currently permitting the traffic. It then provides verifiable evidence of these findings, such as showing multiple potential paths and the specific rule, and subsequently suggests precise CLI commands to implement a block, typically by modifying or adding a rule to deny traffic from the vulnerable host, thus offering a critical head start in rapid risk mitigation.

The underlying AI architecture uses state-of-the-art, off-the-shelf Large Language Models (LLMs) from providers such as Anthropic (Sonnet and Haiku models via AWS Bedrock) for natural language understanding and task planning. Crucially, these LLMs are not custom-trained or fine-tuned with proprietary networking data. Instead, deep network analysis, the network’s digital twin, and the “guardrails” that ensure the AI’s suggestions are relevant, accurate, and actionable within the network context reside within the Forward Networks platform’s agent. This modular design allows customers to plug in their own hosted LLMs while relying on Forward Networks for authoritative network intelligence and protective logic.

Personnel: Nikhil Handigol

Forward AI – Config Audit and Compliance with Forward Networks

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Forward AI aims to revolutionize network configuration audit and compliance, particularly for organizations in regulated industries that dread annual audits. These audits are typically manual, time-consuming, and error-prone, and carry a significant risk of penalties. The traditional approach involves painstakingly listing all devices, understanding vendor-specific configuration syntaxes for each operating system, extracting data, correlating it with standards, and generating reports – a task that can span days and requires specialized expertise across multiple vendors. This complexity underscores the critical need for automation and simplification to achieve and demonstrate compliance.

Forward Networks addresses this challenge with Forward AI, allowing users to express their audit goals in natural language, such as validating consistent NTP server configurations across all devices. An agentic system then kicks in, generating a precise query to extract relevant configuration data from Forward Networks’ normalized data model, which contains configurations from all network devices. Crucially, Forward AI understands the nuances of multi-vendor environments and automatically generates platform-specific configuration templates for devices from Cisco, Arista, Juniper, Fortinet, and Palo Alto, enabling accurate interpretation and analysis of NTP settings.

This automated process swiftly assesses hundreds of devices, providing a comprehensive report detailing device names, types, configured NTP servers, and compliance status against the specified standard. For instance, the demo showed an audit of 124 devices completed rapidly, identifying discrepancies and highlighting specific device classes where the target NTP server was absent. This not only streamlines the audit process but also provides solid, verifiable evidence for compliance resolution, dramatically reducing manual effort, improving accuracy, and ensuring organizations can efficiently meet their regulatory obligations.

Personnel: Nikhil Handigol

Forward AI – Security Vulnerability Management with Forward Networks

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The presentation highlighted a security vulnerability management use case that demonstrated a unique way to access Forward AI via Slack. In a common scenario, a CISO asked via Slack which devices were affected by a specific CVE. Forward AI, acting as an agent within the Slack channel, was prompted to investigate. It gathered vulnerability details and responded directly in Slack, identifying affected devices and providing a link to further evidence and details within the Forward Networks platform. The speaker addressed security concerns about Slack integration, emphasizing that specific integrations and channel restrictions are in place to ensure secure communication.

Beyond this demonstration, Forward AI aims to lower the barrier to network understanding by enabling users to ask questions in plain English rather than requiring them to learn complex, network-specific languages. It supercharges efficiency through an agentic architecture that can plan and execute dynamic, multi-step workflows, coordinating actions across multiple systems like ServiceNow and Slack. This capability instantly up-levels teams, enabling non-experts to solve complex network problems using state-of-the-art AI. The foundation of Forward AI’s effectiveness lies in combining the broad general capabilities of modern large language models with the deep, specific knowledge derived from Forward Networks’ mathematically accurate digital twin, which overcomes the challenge of applying AI directly to overwhelmingly complex raw network data.

Looking ahead, Forward AI, built on this robust digital twin, is designed to evolve into an agency system that can interact with other external systems via a general mechanism called MCP, fostering a thriving ecosystem of interacting agents. The core philosophy underpinning these agentic operations is trust, especially given the critical nature of network infrastructure. While striving for speed and efficiency, the current approach for Forward AI is to guide operators and provide deep insights, avoiding direct network changes to ensure safety and prevent unintended disruptions. The digital twin remains the essential foundation for enabling these trusted agentic operations, delivering measurable ROI.

Personnel: Nikhil Handigol