Fueling Network AI: The Critical Role of Source of Truth Data

As AI takes root in networking, its success hinges not just on raw data—but on large, validated, and diverse datasets that accurately represent the systems they aim to model. AI systems require high-quality input to produce meaningful outputs, and the quality of those insights is directly tied to the volume and veracity of the data available. Poor, incomplete, or inconsistent data leads to unreliable AI outcomes, undermining trust and utility.

This need for comprehensive, structured data is even more pressing in networking. Too often, industry efforts focus on individual devices, treating each as an isolated node. But AI requires a holistic understanding of the network to deliver actionable insights. That’s where a Network Source of Truth (NSoT) becomes essential—capturing the full topology, device roles, and the relationships between them. AI thrives on context, and context comes from accurate, relational data.

Why an SoT?

Nautobot paired with the Single Source of Truth application integrates data from across your ecosystem to build the relationships AI needs to function effectively.

Using an SoT for Populating Other Systems

By using Nautobot as the authoritative data source, organizations ensure consistent, validated information is shared across all network systems. For example, when Nautobot populates observability, telemetry, and incident response tools, it creates reliable interoperability and faster incident resolution.

• Receive a notification from a service provider that circuit A has gone offline.
• A workflow executes to determine if there is a redundant circuit at the location, and if so, what interface it’s connected to.
• The workflow determines if the secondary circuit is active and fails over the traffic. Further workflows execute to verify site availability and run secondary health checks and changes, pausing high-bandwidth, low-priority data that can sustain some outage time.
• Finally, the workflow delivers location information and troubleshooting details, such as physical addresses, wiring cabinet locations, and specific onsite equipment information to assist NOC and onsite engineers.

Without the SoT Data

Without a reliable Source of Truth, AI tools become guesswork engines. Here’s what can go wrong:

• Hypothesized Topologies: AI must infer secondary connections or failover paths from unstructured configs, increasing error likelihood.
• Hallucinations: In the absence of structured relationships, AI may fabricate connections or conclusions.
• Stale Metadata: Outdated config files mislead AI—especially when location or role changes aren’t documented.
• Conflicting Systems: Without a unified source, conflicting tools may confuse AI about which data is accurate.
• Validation Gaps: Without Nautobot’s Data Validation Engine, discrepancies across systems go unchecked.

Why Nautobot

Nautobot is uniquely positioned to power both Network Automation and Network AI. It doesn’t just store network data—it validates it, ensuring accuracy, consistency, and context for automation and AI-driven operations.

The Challenge of Data Population

A persistent challenge in Source of Truth initiatives is the risk of ingesting “bad” data, creating technical debt that compounds over time. The key is to start with the data that matters most to your current use cases—don’t wait for perfection. Premature optimization delays value and increases risk.

(Note that I couldn’t figure out how to do an admonition here—that is what I’m hoping the above is. Let me know if you need words for it; I can get those.)

Nautobot Jobs

Nautobot Jobs establish critical guardrails for AI-initiated actions—especially changes and writes to the network. Read actions that are gathering data are generally safe, but only as useful as the quality of the data.

With Nautobot Jobs, since they are directly connected with the SoT, you now have a set of predefined jobs that receive input from an AI tool. With the Jobs execution you then get:

• Comprehensive logging—including who or what triggered the Job, when, and what was executed.
• Granular RBAC control—specifying which users or systems can execute specific Jobs.
• Approval workflows—enabling human oversight for sensitive operations via GitOps or custom Apps.

There is a need to pair guardrails with AI actions on the network. By providing the framework and an AI-accessible API endpoint, you are able to get the necessary network guardrails. This principle should also extend to data updates within Nautobot, where Nautobot Jobs are used to update data, thereby preventing direct, uncontrolled modifications. These guardrails help to provide a Data Governance for AI framework.