From Detection to Config Push in One Click

Alert-Triggered Remediation: From Detection to Config Push in One Click

Every network engineer has lived this moment. An alert fires — a bandwidth threshold breach, a rogue traffic pattern, a route leak. You know what the fix is. You’ve done it dozens of times. But between the alert and the fix, there’s a gauntlet of manual steps: log into the device, verify the situation, craft the config change, test it, push it, verify again. The cognitive load isn’t in the fix itself — it’s in the ceremony around it.

What if the alert came with the fix already attached? <!– truncate –>

The Idea: Remediation as an Alert Action

WhiteOwl Networks already connects alerting with configuration management. Alerts fire based on thresholds, AI investigates the root cause, and Ansible-driven config push lets you deploy changes across devices. But these are still separate steps. The operator is the glue between “something is wrong” and “here’s the config to fix it.”

The next evolution is making configuration remediation a first-class action on an alert — the same way you can currently acknowledge, investigate, or silence an alert. When an alert fires, the system evaluates whether a remediation template is associated with that alert rule. If one exists, it automatically renders the template using live context from the alert — the offending IP addresses, ports, interfaces, traffic volumes — and presents a ready-to-deploy configuration change. The operator reviews it, optionally edits it, and pushes it to the affected devices. One click from alert to fix.

How It Works

The system has three components: remediation templates tied to alert rules, automatic variable population from alert context, and a review-and-push workflow that puts the operator in control.

Remediation Templates

Each alert rule can optionally have a remediation template attached. These are Jinja2 templates — the same templating engine WhiteOwl already uses for probe configuration and device config management. The template defines the configuration change that should be applied when this specific alert condition occurs.

Here’s a concrete example. Say you have an alert rule that fires when a single source IP exceeds 500 Mbps on a WAN interface — a classic bandwidth abuse or DDoS indicator. The remediation template for that rule might look like this:

! Remediation: Rate-limit excessive source traffic
! Alert: {{ alert_name }}
! Triggered: {{ triggered_at }}
! Source: {{ source_ip }} → {{ destination_ip }}
! Observed: {{ observed_bps | human_readable_bps }}

ip access-list extended ACL-RATELIMIT-{{ source_ip | replace('.', '-') }}
 permit ip host {{ source_ip }} any

class-map match-all CM-RATELIMIT-{{ source_ip | replace('.', '-') }}
 match access-group name ACL-RATELIMIT-{{ source_ip | replace('.', '-') }}

policy-map PM-WAN-INGRESS
 class CM-RATELIMIT-{{ source_ip | replace('.', '-') }}
  police {{ rate_limit_bps | default('100000000') }} conform-action transmit exceed-action drop

The template is pure IOS configuration, but the values — the source IP, the rate limit, the alert metadata — are variables that get populated automatically when the alert fires.

Automatic Variable Population

When the alert engine detects a threshold breach, it already has rich context: which metric breached, on which device, at what value, and for how long. For flow-based alerts, it also knows the top talkers — source and destination IPs, ports, protocols, application names from DPI, and AS numbers from GeoIP enrichment.

This context becomes the variable set for template rendering. When a remediation template is associated with the alert rule, the system populates a variable map like this:

{
  "alert_name": "High BPS - WAN Uplink",
  "alert_severity": "critical",
  "triggered_at": "2026-03-04T14:32:00Z",
  "device_hostname": "core-rtr-01",
  "device_ip": "10.1.1.1",
  "interface": "GigabitEthernet0/0/1",
  "metric_name": "interface_bps_in",
  "metric_value": 892000000,
  "threshold_value": 500000000,
  "source_ip": "203.0.113.45",
  "destination_ip": "10.10.5.20",
  "source_port": 44892,
  "destination_port": 443,
  "protocol": "TCP",
  "application": "HTTPS",
  "as_number": 64512,
  "as_name": "EXAMPLE-TRANSIT-AS",
  "observed_bps": 892000000,
  "rate_limit_bps": 100000000
}

The template renders against these variables and produces a complete, device-ready configuration block. No copy-paste, no fat-fingering an IP in a CLI session at 2 AM.

Review and Push

This is the critical part — the operator stays in the loop. The rendered config appears in the alert detail view as a proposed remediation, not an automatic change. The UI shows the fully rendered configuration with syntax highlighting, the target device or devices the change will be pushed to, a diff view if the device’s current running config is available, and options to edit the rendered config before pushing.

The operator reviews the change, clicks “Push to Device,” and WhiteOwl handles the rest: Ansible backs up the current running config, applies the change, and verifies the device is healthy. The remediation is logged against the alert with full audit trail — who approved it, when, what was pushed, and what the config looked like before and after.

Template Examples for Common Scenarios

The power of this approach is that templates can be pre-built for the scenarios your team encounters repeatedly.

Blackhole a DDoS Source

When a volumetric alert fires with an identifiable source:

! Blackhole route for DDoS source {{ source_ip }}
! Alert: {{ alert_name }} | {{ triggered_at }}
! Observed: {{ observed_bps | human_readable_bps }} (threshold: {{ threshold_value | human_readable_bps }})

ip route {{ source_ip }} 255.255.255.255 Null0 tag 666 name BLACKHOLE-{{ source_ip | replace('.', '-') }}

Simple, surgical, and reversible. The alert context tells you exactly which IP to block without hunting through flow data.

Block a Port Across Edge Firewalls

When DPI or flow analysis identifies traffic on an unexpected port — say, a new vulnerability is being actively exploited on a specific service port:

! Block exploitation traffic on port {{ destination_port }}
! Alert: {{ alert_name }}
! Detected {{ protocol }}/{{ destination_port }} traffic from {{ source_ip }}
! Application: {{ application | default('unknown') }}

ip access-list extended ACL-EMERGENCY-BLOCK
 deny {{ protocol | lower }} any any eq {{ destination_port }} log

Adjust SNMP Polling on an Overloaded Device

When a CPU alert fires on a device that you suspect is being hammered by polling:

! Reduce SNMP polling load on {{ device_hostname }}
! CPU at {{ metric_value }}% (threshold: {{ threshold_value }}%)

snmp-server queue-length 20
snmp-server packetsize 2048

Shut an Interface Seeing Errors

When an interface error rate alert fires and the link is flapping:

! Administratively shut flapping interface
! {{ device_hostname }} {{ interface }}
! Error rate: {{ metric_value }} errors/sec (threshold: {{ threshold_value }})

interface {{ interface }}
 shutdown
 description SHUT-BY-WHITEOWL-{{ triggered_at[:10] }}-ERROR-RATE

Each of these is a template you write once, associate with the right alert rule, and never think about again until it fires — at which point the config is ready to review and push.

Multi-Device Remediation

Some scenarios require pushing changes to more than one device. A route leak, for example, might require adding a prefix filter on multiple edge routers simultaneously. WhiteOwl handles this by letting remediation templates target device groups rather than single devices.

When the alert fires, the system renders the template once per target device, substituting device-specific variables like hostname and interface names. The operator sees a summary of all proposed changes across all target devices, can review each one individually, and pushes them as a batch — with Ansible executing sequentially or in parallel depending on the configuration.

This is where the combination of alerting and config management really pays off. Manually pushing an emergency ACL change to eight edge routers is slow and error-prone. Having the system pre-render the change for each device and push them all in one operation — with pre-change backups on every device — turns a 30-minute fire drill into a 30-second review.

Safety Rails

Automated remediation is powerful, but it’s also dangerous if not constrained. WhiteOwl builds in several safety mechanisms.

Templates go through a validation step before they’re saved. The system parses the Jinja2 syntax and checks for undefined variables to catch typos and logic errors before they reach production. Remediation actions always require explicit operator approval — there’s no “auto-push” mode. The rendered config appears as a proposal, and a human clicks the button.

Every remediation push creates a pre-change backup of the affected device’s configuration. If the change causes problems, the previous config is one click away. Additionally, remediation templates can define a rollback template — a corresponding change that undoes the remediation. If you blackholed an IP and want to remove it, the rollback template is pre-populated and ready.

Rate limiting prevents alert storms from generating dozens of remediation proposals simultaneously. If the same alert fires repeatedly within a cooldown window, only the first remediation is proposed.

Finally, remediation actions are gated by the same role-based access controls as any other config push. The alert can generate the proposal for anyone to see, but only operators with write access can approve and execute the push.

The Bigger Picture

This feature sits at the intersection of several trends in network operations. Intent-based networking talks about defining what the network should do and letting automation handle the how. AIOps tools are getting better at identifying root causes. Configuration-as-code practices from the DevOps world are finally reaching network teams.

What’s been missing is the connective tissue. The monitoring tool knows something is wrong. The automation tool can fix it. But the translation from “alert context” to “config change” has been a human exercise — requiring tribal knowledge about which template to use, which variables to fill in, and which devices to target.

By embedding remediation templates directly in the alerting workflow and auto-populating them from alert context, WhiteOwl makes that translation automatic. The tribal knowledge gets codified into templates. The context mapping becomes structured data. And the engineer’s job shifts from executing the fix to approving it.

That’s the goal — not to remove the human from the loop, but to have everything ready when they arrive.

WhiteOwl Networks is a self-hosted network monitoring platform combining NetFlow/IPFIX analysis, SNMP monitoring, deep packet inspection, synthetic monitoring, AI-powered insights, and Ansible-driven configuration management. Learn more at nitronet.ai.