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08/07/2026 7:09 pm
TL;DR: Digital twins let telecom operators simulate, test, and optimise network behaviour across fixed and mobile environments, while the article says real-time data accuracy, legacy systems, and fragmented inventories still limit results. The governance issue is not simulation quality alone, but whether operators can trust the data model behind automation and autonomous network decisions.
NHIMG editorial — based on content published by Comarch: digital twins in telecom network operations and planning
Questions worth separating out
Q: How should telecom teams govern automated network changes generated by a digital twin?
A: Treat the twin as a decision-support system until its outputs have been validated against authoritative inventory and failure conditions.
Q: Why do fragmented inventories undermine digital twin accuracy?
A: A digital twin depends on a consistent source of truth for topology, configuration, and asset relationships.
Q: When should operators trust a digital twin enough to support autonomous network operations?
A: Only when the twin is fed by current, reconciled data and its outputs have been tested across realistic failure scenarios.
Practitioner guidance
- Establish a single authoritative network inventory Reconcile topology, configuration, and asset data before relying on a digital twin for planning or automated operations.
- Gate autonomous actions through sandbox validation Test self-healing, self-configuring, and optimisation logic in a controlled environment before any production rollout.
- Tie every model output to traceable change records Record which simulated conditions produced which network decision, then preserve the approval and execution trail for audit and incident review.
What's in the full article
Comarch's full article covers the operational detail this post intentionally leaves for the source:
- The article walks through how its OSS and network planning tools fit together across discovery, reconciliation, and configuration workflows.
- It provides more detail on how simulated network scenarios are translated into validated design changes before deployment.
- It expands on the platform's visualization features across geographical, topological, and hierarchical views of network assets.
- It also describes the sandboxed monitoring approach used to validate AI-driven responses without affecting production systems.
👉 Read Comarch's article on digital twins for telecom network planning and automation →
Digital twins in telecom: what they change for network teams?
Explore further
08/07/2026 7:34 pm
Digital twins expose an operational truth problem before they expose an automation problem. The article’s central challenge is not whether telecom networks can be simulated, but whether the simulation is grounded in current, reconciled state. When inventory, telemetry, and configuration drift apart, the twin becomes a decision aid built on unstable premises. The implication is that operators should treat data authority as the first control boundary.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
A question worth separating out:
Q: What should security and network teams review before linking AI optimisation to production networks?
A: They should review data quality, approval paths, rollback capability, and auditability. If any of those are weak, the organisation can still use the twin for planning, but it should not use the output as a direct control signal for live networks. Safety depends on governing the handoff from simulation to execution.
👉 Read our full editorial: Digital twins in telecom expose the limits of legacy network ops
