Configuration is the most direct expression of intent in a network. When an engineer configures a device, they are translating a policy decision into a set of instructions that determines how traffic moves, what is permitted, and what is not. The accuracy and consistency of that translation, maintained across hundreds or thousands of devices over months and years, is one of the most reliable predictors of network stability and security posture.
What Is Network Configuration and Change Management (NCCM)?
Network Configuration and Change Management, commonly abbreviated as NCCM, refers to the set of processes and tools organizations use to control, track, and audit changes to network device configurations. At its most basic, it includes:
Configuration Backup: Preserves a historical record of what each device was configured to do at different points in time.
Change Tracking: Records when a configuration was modified, by whom, and for what reason.
More advanced NCCM implementations add configuration compliance checking, which compares the actual running state of devices against a defined policy standard, and automated remediation, which corrects detected drift without requiring manual intervention at each affected device.
Why Legacy NCCM Is No Longer Enough
Legacy NCCM tools were designed for environments that were predominantly on-premises and sourced from a limited number of vendors. They could manage configuration backup and basic change tracking effectively in those environments, but their ability to operate across multi-vendor estates, extend into cloud and SD-WAN infrastructure, and support the kind of policy-based compliance that modern regulatory frameworks require is limited.
The networks that most large enterprises operate today are more complex than the environments these tools were designed for, and the gap between what legacy NCCM provides and what modern network configuration management requires has grown wide enough that many organizations are operating with significant blind spots in their configuration governance.
Configuration Backup, Versioning and Change Control
Configuration backup is the foundation of any NCCM implementation. A backup that runs on a defined schedule and stores the full configuration of each device creates the historical record from which changes can be reconstructed, and recovery is possible when a change produces an unexpected outcome.
Versioning adds context to that record by maintaining a structured history of configuration states rather than a flat set of backup files. With versioning, operations teams can:
Compare any two points in a device’s configuration history.
Identify exact differences between versions down to individual CLI syntax lines.
Trace specific changes to precise maintenance windows and incident timelines.
Change control, where changes are reviewed and authorized before execution, closes the loop between documented intent and actual deployment. In environments where emergency changes are made under pressure and not followed up with proper documentation, the gap between the authorized configuration and the actual configuration tends to grow steadily over time.
Configuration Drift Detection and Remediation
Configuration drift occurs when the actual configuration of a device deviates from the intended configuration, whether through unauthorized manual changes, the effects of software updates, or temporary changes that were not reverted. In a large multi-vendor environment, some level of drift is almost inevitable without continuous detection, because the volume of devices and the pace of change make manual audits unable to keep up.
Continuous drift detection compares actual device configuration against the defined policy standard on an ongoing basis and alerts when a deviation is found. When connected to automated remediation, the system can correct detected drift under defined governance policies without requiring manual intervention at each affected device. The network configuration management capabilities within ThreadSpan, Tata Communications’ AI-powered control platform, address drift detection and remediation as integrated functions, making continuous compliance practically achievable at enterprise scale.
Policy and Compliance Across Multi-Vendor Networks
Policy-based configuration management defines the intended state of the network in terms of outcomes rather than device-specific commands, which allows a single policy to be applied across equipment from different vendors without requiring a separate implementation for each. This approach is particularly relevant for compliance requirements that apply across the full network estate, where manual policy verification at device level is not feasible at any meaningful scale.
Compliance reporting generated as a byproduct of continuous policy checking produces the audit evidence that regulatory frameworks require without the manual evidence assembly that periodic audits typically involve. Tata Communications’ ThreadSpan platform connects policy definition, compliance checking, and audit reporting within a single operational framework, which removes the coordination overhead that separate tools for each function create.
Network Automation vs Network Orchestration
The terms automation and orchestration are often used interchangeably, but the operational difference between them is distinct:
Automation: Refers to executing a single, defined action on a specific device without manual intervention (e.g., pushing a script to update a single switch).
Orchestration: Refers to coordinating multiple automated actions across disparate devices and environments in a defined sequence, managing dependencies, and validating outcomes at each step.
A software upgrade deployed to a single device is automation. The same upgrade deployed across a network in phased waves, with automated validation checks between each phase, is orchestration. The governance that orchestration provides is what makes automated changes safe to apply in production environments where an unvalidated change applied simultaneously to all devices could trigger a widespread outage.
AI-Assisted Configuration Validation and Remediation
AI adds a layer of analytical capability to configuration management that rule-based systems cannot provide. Where a compliance rule can check whether a specific configuration element is present or absent, an AI system can evaluate whether the overall configuration of a device is consistent with the intended behavior of that device in the context of the network it operates in. This kind of contextual validation catches configuration problems that are not covered by explicit rules.
The connection between AI-assisted validation and automated remediation creates a feedback loop that allows the network to maintain a closer alignment between intended and actual state than a manually managed system can achieve. This is where configuration management begins to function as an active operational capability rather than a documentation and recovery tool.
Moving From NCCM to Intelligent Infrastructure Automation
The full resource on how network automation orchestration sits within an intelligent infrastructure automation model, and what the technical and organizational requirements of that transition look like, is worth reviewing as a framework for understanding where configuration management fits in the broader automation journey.
The transition from legacy NCCM to intelligent infrastructure automation is a phased progression. Each step, from configuration backup through drift detection, policy compliance, and automated remediation, delivers operational value on its own terms while making the next step more achievable. Organizations that approach this progression sequentially tend to build more durable capabilities than those that attempt to implement advanced automation before the configuration governance foundation is in place.
FAQs
What is configuration drift and why does it matter?
Configuration drift is when the configuration that a system is supposed to have is different from what its configuration is actually running as. It builds up through changes made in emergencies, software upgrades, and also through undocumented modifications. Drift that is not identified can cause security vulnerabilities, compliance violations, and risks of outages.
How do I detect and remediate network configuration drift?
Before introducing the concept of a policy target, automated drift detection first compares the currently running configuration of each device with a baseline. If the system notices a change in the configuration, the changes would be pointed out, and the responsibility to address them would be handed over either to a human reviewer or to an automated remediation system following the established workflow of the organization.
What is the difference between network automation and orchestration?
Automation focuses on the execution of separate tasks on different devices, while orchestration is about the combination of multiple device automations to achieve a full operational workflow. Orchestrating the steps for provisioning a new site, for example, is necessary because this activity involves different devices and systems, so changes must occur across several areas of the network infrastructure.
How do I maintain policy consistency across vendors?
A configuration management platform that supports multiple vendors through a common policy model, translating policy definitions to vendor-specific configuration syntax, is the practical approach. Manual maintenance of separate policy implementations per vendor is reliable only in small, static environments.
How can AI improve network configuration management?
AI identifies anomalous configuration patterns that rule-based tools miss, predicts the impact of proposed changes based on historical outcomes, and reduces the false positive rate in drift detection by distinguishing meaningful deviations from expected variation.
How do I automate compliance validation across a multi-vendor network?
Automated compliance validation works by comparing the running configuration of each device against a defined policy baseline on a continuous basis, flagging deviations as they occur, and generating timestamped evidence records that can be produced for auditors without manual data assembly before each review cycle.
