Telecom and infrastructure providers operate on some of the most demanding computer environments in the world. From AI inference at the edge to real-time network analytics, today’s networks rely on GPU-scale infrastructure to deliver low-latency, high-availability services at massive scale. But with that performance comes complexity and risk. Sprawling systems, hybrid environments, and constant change create fertile ground for misconfigurations and gaps. A single overlooked setting can trigger outages, break compliance, or open the door to a breach.

At the same time, attackers are growing more aggressive. In 2024, a Chinese state-backed group infiltrated multiple U.S. carriers by exploiting unpatched routers and an admin account with no MFA, accessing millions of call records. Threats like these make clear that manual monitoring and reactive security aren’t enough. Important gaps often go unnoticed until they cause service disruption or data loss. Even minor config errors can cascade into major failures.

Kindo addresses these challenges by bringing intelligent automation to the heart of telecom security operations. It connects with your identity systems, network tools, and infrastructure platforms, then uses large language models (LLMs) to analyze data and take context-aware action. This streamlines decision-making and reduces response time, without taking control away from your team.

Below are three automation workflows designed to help telecom and infrastructure teams improve security posture, replace manual effort, and stay ahead of operational risk.

1. Remediate Infrastructure Drift & Configuration Errors

Telecom operators have a lot to keep track of, including thousands of routers, switches, servers, and cloud resources that all need to be set up just right to keep everything secure and running smoothly. But it only takes one slip-up to cause major problems. Something as simple as a default password left unchanged or an open port nobody noticed can give attackers a way in. Checking every system manually isn’t realistic at this scale, so a lot of issues don’t show up until it’s too late, when an auditor flags them or customers start complaining. That’s where automation comes in. By automatically spotting and flagging infrastructure drift, you can catch these problems early, before they lead to outages or security incidents. The idea is to keep everything consistently aligned with your policies, from router configs and access control lists to cloud security settings. Not just during annual audits, but all the time. Kindo helps by detecting and suggesting fixes for any misconfigurations it finds. It won’t make changes on its own (approvals still matter), but it takes the heavy lifting off your team and helps prevent downtime before it starts.

Workflow Steps (Drift & Config Remediation)

1. For step one, a Kindo workflow with an API call step can be set to regularly pull config data and state from across your infrastructure. It uses API calls and scripts to fetch router and switch configs (using NetConf, REST APIs, or tools like Cisco DNA Center), as well as cloud settings like VPC security groups and IAM policies. In the example below, we’re calling Cisco Catalyst Center, but you can add as many API calls as you want. The more you include, the more complete your picture.

2. Kindo’s WhiteRabbitNeo LLM then analyzes these configs to pinpoint any drift or errors. The model is prompted with your security policies and best-practice standards. For instance, “All administrative interfaces must be restricted; no public IP access allowed. Default passwords should be disabled. Only approved subnets can communicate with core network devices.” The LLM reviews each system’s settings against these rules (and known hardening guides) and flags discrepancies. Each finding is tagged with a plain-language explanation of why it’s a concern (e.g. “SSH open to all IPs can allow unauthorized access”).

3. In the final step, the workflow generates a structured report of all detected drifts and config errors. Each entry lists the device or system, the specific setting or deviation identified, and a recommended fix. This report can be output as a CSV or PDF and can be sent to the network engineering team or ticketing system.

Value of Automation

By continuously scanning for and highlighting misconfigurations, telecom operators can prevent many incidents before they impact customers. Small errors no longer linger for months waiting to cause an outage or breach, because they are caught early. This dramatically improves reliability and security. The net effect is stronger network hygiene and fewer firefights. Automation also saves massive effort for your team. Instead of manually combing through device configs or running occasional scripts, engineers get a concise report of what needs attention. This frees up their time but also reduces human error since nothing is left to memory or sporadic audits. 

2. Enforce Zero-Trust & Segmentation Dynamically

Telecom networks are highly interconnected, spanning internal IT systems, telecom operations networks, cloud services, partner links, and more. This makes network segmentation and a zero trust approach important. In a zero trust model, no user or device is inherently trusted simply for being inside the network; every access is continuously verified and limited to least privilege. For telcos, adopting zero trust means ensuring that sensitive systems (like core network nodes, customer databases, and management interfaces) are strictly isolated and accessible only to authenticated, authorized entities. Effective segmentation ensures that even if one part of the network is compromised, the intruder cannot freely roam elsewhere. Maintaining this segmentation dynamically is a challenge. Unfortunately, many organizations still rely on periodic network reviews or static ACLs, where mistakes or exceptions can persist unnoticed. Automation can help by continually monitoring the network’s segmentation and access control posture, and flagging or even adjusting any deviations in real time to uphold zero trust principles.

Workflow Steps (Zero Trust Segmentation)

1. The workflow begins by collecting data on network segmentation and access controls across your environment. It uses API integrations to pull firewall rulesets (from physical firewalls or cloud security groups), VLAN and subnet configurations from network controllers, VPN and ZTNA logs, and identity management records that show which users or roles have access to specific network segments or applications. It can also ingest real-time connection logs, such as NetFlow data or SDN controller logs, to see how different parts of the network are actually communicating. In the example below, we’ll use an AWS API call to retrieve security group data.

2. Kindo’s foundation model (WhiteRabbitNeo) then analyzes this information against your defined zero trust and segmentation policies. We prompt the AI with rules and context such as: “Only whitelisted management systems should initiate connections to core network gear. Customer data servers should never be directly accessible from the internet or employee workstations. Flag any unusually broad network ACLs or any cross-segment communication that seems out of policy.” The model evaluates every rule and flow. Because it understands context, it can catch subtle violations. For example, it might detect that “Database segment B is receiving connections from Office Network segment – which is not expected (those should go through an application layer)”. Or “Firewall XYZ has a rule allowing ALL traffic from a vendor network into an internal subnet, which violates least privilege.”

3. In the final step, the workflow generates a report highlighting any segmentation or access control gaps found. For each finding, it outlines the affected segments or systems, what the issue is, the behavior that triggered it, and a recommended mitigation. This report can be fed into your SecOps workflow.

Value of Automation

Dynamic enforcement of zero trust and segmentation policies ensures that your network’s defenses keep up with its changes. This dramatically reduces the window of exposure when something is misconfigured or an unauthorized access attempt occurs. Instead of a hole in your segmentation going unnoticed for weeks or months, the system will catch it perhaps within minutes or hours of it emerging, so it can be fixed before an attacker exploits it. The benefit is a much smaller blast radius for any breach, since attackers can’t easily pivot across your infrastructure. By containing threats to the segment where they originated, you minimize damage, aligning with the core zero trust goal of “never trust, always verify” for every request.

3. Cut MTTD and MTTR with Incident Resolution

When an incident strikes in a telecom environment every second counts, whether it’s a cyber attack or a network outage. Mean-time-to-detect (MTTD) and mean-time-to-resolve (MTTR) are important metrics: a slower response can mean more data exfiltrated, more customers impacted, and more revenue lost. Unfortunately, many teams struggle with lengthy detection and triage times because the signals are buried in disparate systems. This is especially true in telecom where connectivity is mission-critical (imagine a major mobile network outage during a big event. It’s both reputationally and financially devastating). Clearly, accelerating detection and response is paramount. The challenge is that diagnosing an incident requires piecing together information from many sources like network alerts, server logs, user activity, recent changes, threat intel, etc. Doing this manually at 3 AM during an outage is slow and error-prone. This is where a context-aware, AI-driven workflow can be a game-changer. By automatically gathering all relevant context and even suggesting probable causes or fixes, it empowers responders to act much faster and more decisively. The workflow essentially acts as a virtual tier-1 incident responder: aggregating data, correlating events, and producing an initial incident report in minutes.

Workflow Steps (Incident Triage & Response)

1. The workflow begins with a triggered agent that activates when a new Jira ticket is created containing the word “incident” in the title or description. This ensures the response process starts immediately upon issue reporting, without waiting for manual triage or escalation.

2. Once triggered, it automatically collects contextual data from pre-defined sources via API integrations. For security-related incidents, the workflow pulls authentication logs, system logs, recent identity activity (e.g., Okta or VPN access), SIEM alerts, and recent code or config changes. For outages or performance issues, it gathers device metrics, network logs, monitoring alerts, and records of recent infrastructure changes. (Here we are going to use an Okta API call as an example).

3. Next, using WhiteRabbitNeo, an LLM step analyzes the compiled incident data to diagnose what’s happening or at least prioritize the next steps. We prompt the model with instructions and knowledge about typical telecom issues and the environment: “Identify likely root cause or affected components. Determine if this is a known pattern (e.g. configuration error, hardware failure, DDoS attack, malware outbreak).” The AI can cross-reference known issues (perhaps it’s been fed info on common outages or MITRE attack patterns).

4. It then produces a concise summary of the incident, including hypotheses and recommended actions. This analysis is done in natural language but backed by the raw data, so the team gets both the high-level diagnosis and the supporting details if they need to drill in. The report includes the AI’s summary of what’s happening, the key evidence (log lines, error codes, timestamps, etc.), and the recommended next steps. It may also include a severity rating and whether the issue appears to be ongoing or resolved (if the data shows recovery). Because this is machine-generated, it happens almost immediately after detection – far faster than a human could compile such a report. The team now has a single pane of information to work from, rather than scrambling between consoles.

Value of Automation

By leveraging AI to do the heavy lifting in the first minutes of an incident, telecom security teams can drastically cut both MTTD and MTTR. In many cases, the detection of an anomaly itself can be improved – instead of waiting for a human to notice an issue in a dashboard or (worse) a customer call, the workflow might catch a pattern across logs that triggers an alert. Even if existing monitoring raises the flag, automation ensures the response begins immediately by gathering context. This means that issues are identified and confirmed faster, often before they cascade into wider problems. As a result, attackers get far less dwell time to expand their foothold. For outages or performance incidents, faster resolution directly translates to reduced downtime, saving money and customer goodwill.

GPU-Scale Infrastructure Demands Smarter Automation

Kindo brings AI-native automation to GPU-scale telecom environments.

Our platform connects to your tools, analyzes infrastructure in real time, and flags misconfigurations, segmentation gaps, and incident signals before they turn into outages. Operators get fast, actionable insights without the noise.

Use Kindo to catch drift, enforce zero trust, and cut response time from hours to minutes. Deploy automation that matches the speed and complexity of your network.

Deploy AI-native automation for the networks of tomorrow (demo available here).