Generic conversion framework for unmanaged devices → custom asset types

Problem description

GLPI currently allows converting unmanaged devices only into a limited set of core asset types (Computer, Network Equipment, Printer, Phone) via the Convert feature.

However, modern environments contain a rapidly growing number of business-owned, network-connected devices that:

• Cannot run agents

• Are not well represented by existing core asset types

• Still must be inventoried and managed

Examples include (non-exhaustive):

• IP cameras

• Biometric readers

• Door/access controllers

• Wireless access points

• IoT / OT devices

• Environmental sensors

• AV equipment

At present, administrators must:

• Manually create custom asset types

• Manually recreate assets discovered as unmanaged devices

• Manually correlate MAC/IP/ports after the fact

This results in:

• Duplicate effort

• Inconsistent workflows (Convert exists for some types but not others)

• Increased risk of data inconsistency

• Poor scalability in environments with large numbers of non-agent devices

Proposed solution

Introduce a generic, user-defined conversion framework that allows administrators to explicitly define how unmanaged device discovery data can be converted into custom asset types, while preserving GLPI’s existing data-integrity guarantees.

High-level concept

1. Administrator creates a custom asset type (e.g. Camera)

2. Administrator defines a conversion template that maps:

   • Unmanaged device fields → asset fields
     (e.g. MAC, IP, name, network port, location)

3. GLPI validates the mapping:

   • Required fields present

   • No ambiguous or invalid mappings

4. Only after validation, GLPI enables:

   • Convert → <Custom Asset Type>
     for unmanaged devices matching the mapping

Conversion remains:

• Explicit

• Opt-in

• Deterministic

• Auditable

No automatic or heuristic guessing is introduced.

Why this is safe

This approach maintains the same (or stronger) safeguards as the existing Convert feature:

• No implicit field guessing

• No automatic promotion of discovery data

• Explicit administrator approval via mappings

• Required-field enforcement before conversion

• Clear lineage from discovery → inventory

In many cases, this would be safer than today’s manual recreation, which occurs outside GLPI’s visibility.

Use cases

• IP cameras discovered via network scans

• Access control and biometric devices

• Wireless access points not managed by a controller

• IoT / OT devices in enterprise environments

• Any business-owned network-connected device that cannot run an agent

Benefits

• Reduces manual inventory effort

• Improves CMDB accuracy and consistency

• Scales to modern network environments

• Avoids misuse of existing core asset types

• Aligns GLPI with current enterprise CMDB practices

Additional notes

This proposal is intentionally generic and extensible, not specific to any device category.
It replaces hard-coded conversion logic with a controlled framework while remaining fully backward-compatible with existing Convert functionality.