DEV Community: Khiem Phan

AI Governance: The Foundation for Responsible AI Adoption

Khiem Phan — Fri, 04 Sep 2026 08:36:17 +0000

Your employees are already using AI. But do you know which tools they use, what data they share, or who is responsible for them? As AI adoption spreads across teams and workflows, organizations face a growing challenge: how to enable AI while keeping it visible, secure, and under control.

In this article, we’ll explore what AI governance is, where current approaches fall short, and how organizations can build a stronger foundation for responsible AI adoption.

1. What Is AI Governance?

AI Governance

AI governance is the set of policies, processes, roles, and controls that helps organizations manage AI systems and their usage. It ensures AI systems are used responsibly while protecting data, meeting regulatory requirements, maintaining accountability, and aligning with human values.

For example, a company wants to introduce an AI assistant to help employees summarize internal documents. Before allowing adoption, AI governance helps define:

Which AI tool is approved for use
What types of company data can be shared
Who is responsible for managing the tool
What security and privacy requirements must be met
How the organization monitors its usage over time

AI Governance vs AI Compliance

AI governance and AI compliance are often used interchangeably, but they serve different purposes.

AI compliance focuses on meeting specific legal, regulatory, and industry requirements. It ensures that AI systems follow applicable rules, such as data privacy regulations, security standards, and reporting obligations.

AI governance is broader. It provides the overall framework for managing AI, including how AI systems are selected, deployed, monitored, and controlled throughout their lifecycle.

AI compliance answers “Are we following the required rules?” AI governance answers “How do we manage AI responsibly across the organization?”

2. How Is AI Being Governed in the Workplace Today?

Organizations are actively introducing AI governance programs to manage risks related to security, privacy, compliance, and responsible usage. According to the IAPP AI Governance Profession Report, 77% of organizations are currently working on AI governance initiatives. This shows that AI governance has become a priority for businesses adopting AI.

However, many organizations are still building their governance foundations while employees are already using AI tools across daily workflows. This creates a gap between AI usage and organizational control.

AI tools can be adopted by different teams before security or IT teams have full visibility into their usage. Research from Protiviti found that 47% of large organizations lack full visibility into the AI tools being used by employees, while 65% report challenges related to shadow AI usage.

3. What Makes AI Governance Fall Short?

So why do many organizations still struggle to manage AI effectively?

The challenge is that AI adoption does not follow traditional technology management processes. AI tools can appear quickly across different teams, while governance processes often remain reactive and dependent on known systems.

Two major challenges make AI governance difficult to maintain:

AI Governance Becomes a Static Policy Exercise

Many organizations approach AI governance by creating policies, approval processes, and compliance guidelines. However, governance cannot be effective if it only exists as documentation.

AI environments constantly change. New AI workflows are introduced, existing software adds AI capabilities, or employees discover new agentic AI for their productivity. A policy may define what employees should do, but it does not provide answers to operational questions:

What AI tools are currently being used?
Are employees following the approved guidelines?
Has an AI tool introduced new risks?
Who is responsible for managing each AI system?

Without continuous visibility and monitoring, AI governance becomes a checklist rather than an active management process.

Shadow AI Creates Visibility Gaps

Another major challenge is shadow AI, where employees use AI tools outside official approval processes.

Employees often adopt AI tools because they improve productivity and help solve immediate business needs. However, these tools may be used before security or IT teams have the opportunity to review them.

This creates a visibility problem. Organizations may not know what AI tools are being used, who is using them, what data is being shared, or whether these tools meet security and compliance requirements.

4. Core Components of an AI Governance Framework

The challenges of AI governance cannot be solved by policies alone. Organizations need a structured framework that combines people, processes, and controls to manage AI throughout its lifecycle.

Key components of an effective AI governance framework include:

AI Visibility and Inventory

Organizations need a clear view of the AI systems being used across the business. This includes understanding what tools exist, who uses them, what they are used for, what data they access, and who owns them.

IT teams can also view this as an IT asset management (ITAM) challenge. AI tools are becoming part of the organization's technology environment. They need to be discovered and managed alongside traditional IT assets.

Accountability

Clear ownership prevents AI systems from becoming unmanaged tools that no team is accountable for. Every AI system should have a clear owner responsible for its usage and ongoing management.

For example, a marketing team may introduce an AI content assistant to improve campaign creation. The organization should know who approved the tool and who is responsible for reviewing it.

Transparency and Explainability

Transparency helps employees make better decisions and identify potential issues before they affect business operations.

A customer support team uses AI to summarize customer complaints. The team needs to know what customer information the AI accesses and whether its answers are reviewed or not.

Privacy and Security

AI systems often require access to business information to provide useful results. However, organizations need to control what data can be shared and how it is handled.

Employees may be allowed to use an enterprise AI assistant with internal documents. However, public AI tools may be restricted from accessing confidential company information or asset data.

Human Oversight

AI should support human decision-making, not replace accountability.

Organizations should define where human review is required, especially for high-impact decisions. Human oversight allows teams to validate AI outputs, correct errors, and intervene when AI systems produce unexpected results.

5. Frameworks For Your AI Governance

Many organizations use established frameworks and standards to define their governance approach, assess risks, and create repeatable processes.

These frameworks provide guidance on how to manage AI responsibly, but organizations may adapt them based on their industry, business needs, and regulatory requirements.

NIST AI Risk Management Framework

The NIST AI Risk Management Framework (AI RMF) helps organizations identify, assess, and manage risks associated with AI systems.

The framework is built around four core activities:

Govern: Establish AI governance policies, roles, and accountability across the organization.
Map: Understand the context of AI systems, including their purpose, users, data, and potential risks.
Measure: Evaluate AI performance, risks, and impacts through testing and monitoring.
Manage: Take actions to reduce identified risks and improve AI systems over time.

Instead of focusing only on compliance, the framework provides a practical approach for organizations to build trustworthy AI. It is ideal for organizations that want a flexible framework to manage AI risks across the AI lifecycle.

When a company introduces an AI coding assistant for its development teams, the NIST AI RMF can help define who owns the tool. It can also help assess whether source code or sensitive information could be exposed. The framework helps establish acceptable usage guidelines and monitor whether the tool remains secure and effective over time.

ISO/IEC 42001 AI Management System

ISO/IEC 42001 is an international standard designed to help organizations establish an AI Management System (AIMS).

It provides a structured approach for managing AI responsibly across the organization, including:

Defining AI governance processes
Managing AI risks
Establishing accountability
Maintaining documentation and controls
Continuously improving AI practices

This framework is best for organizations that want a formal, organization-wide AI management system with defined processes and accountability.

For example, a company uses several AI tools across its marketing, HR, and customer support teams. ISO/IEC 42001 can help the company establish a consistent process for evaluating and approving these tools. It can also define who is responsible for each AI system, what controls are required, and how those controls are reviewed over time.

EU AI Act

The EU AI Act is a regulatory framework that focuses on managing AI risks based on the impact of different AI systems.

Instead of applying the same requirements to every AI application, it classifies AI systems into different risk categories, with higher-risk systems requiring stronger controls.

Organizations affected by the regulation may need to evaluate:

How their AI systems are used
What risks they create
What transparency and documentation requirements apply

This framework is suitable for organizations developing, deploying, or using AI systems that fall within the EU AI Act's scope and need to meet its legal requirements.

For instance, a company uses AI to screen job applications. The company needs to determine how the AI Act classifies the system and what requirements apply. This may include stronger controls around risk management, data quality, transparency, and more.

FAQs

1. Who should be responsible for AI governance in an organization?

AI governance should not sit entirely with one department. It typically involves collaboration between IT, security, legal, compliance, data teams, and business leaders. The exact responsibilities depend on how AI is used and the level of risk involved.

2. When should an organization start governing an AI system?

AI governance should begin before an AI system is introduced into a business workflow, especially when it handles sensitive data or influences important decisions. Governance should then continue throughout the system's lifecycle as its usage, capabilities, or risks change.

3. How can organizations govern AI tools that employees adopt independently?

Organizations first need to identify which AI tools are actually being used. They can then assess their risks, determine whether they meet company requirements, and decide whether to approve, restrict, or remove them. This is particularly important for addressing shadow AI.

4. Does AI governance apply to AI features built into existing software?

Yes. AI governance should not only cover standalone AI applications. An existing SaaS platform may introduce new AI features that process company or customer data. Organizations should review these capabilities just as they would a newly adopted AI tool.

5. How can organizations measure whether their AI governance is working?

Organizations can track practical indicators such as the number of AI systems identified, how many have assigned owners, how many have completed risk assessments, and whether unauthorized AI usage is being detected. Regular reviews can also show whether AI systems remain aligned with organizational policies and requirements.

Final Thoughts

AI governance is becoming essential as AI moves from experimentation into everyday business operations. Yet, you cannot effectively govern AI that you cannot see. The next step is knowing what is happening across your organization.

AssetLoom will be launching a new capability to scan and discover access to AI tools across your organization. Stay tuned for a simpler way to uncover AI usage and strengthen your AI governance.

Safe Network Scanning Practices: Top 5 Tips For Your First Network Scan

Khiem Phan — Fri, 04 Sep 2026 06:07:43 +0000

Safe network scanning practice starts with knowing what you are scanning and how your scanner will behave before you run it.

Your first network scan can quickly become harder to manage than expected. A range that is too large can return too many results. Aggressive settings can create extra network traffic. A small setup mistake can even trigger security alerts or scan the wrong systems.

In this article, we will cover five practical tips to help you run your first network scan more safely and get more reliable results.

Tip 1: Start With a Small Network Range

For your first scan, avoid scanning the entire network at once. Start with a small, known IP range so you can see how the scanner behaves and review the results more easily.

For example, you can begin with a /24 subnet, which contains 256 IP addresses, instead of a /16, which contains 65,536.

A smaller range also reduces unnecessary network traffic and makes it easier to spot unexpected devices or configuration issues. If something looks wrong, you only need to inspect a limited range instead of sorting through hundreds of results.

Once the first scan looks correct, gradually expand the scope to other subnets or approved network ranges.

Tip 2: Use a Dry Run When Available

Not every scanner supports this, but if yours does, use a dry run before sending any scan traffic. It gives you a chance to catch mistakes before the actual scan begins.

A dry run can help you confirm:

The target IP range
The estimated number of hosts
The protocols and ports being used
Timeout and concurrency settings
Whether the range includes routed or public addresses

The dry run acts as a final safety check. If the scope, workload, or settings look different from what you expected, fix them first and only start the scan when everything looks right.

For instance, if you planned to scan one office subnet but the dry run shows thousands of hosts, do not start the scan. You should recheck the subnet mask, compare the target with your approved IP ranges, and correct the scope if needed. Once the host count and scan range match what you expected, you can start the actual scan.

Tip 3: Keep Ports and Concurrency Under Control

More ports and higher concurrency can make a scan more aggressive than you actually need. A simple rule is to avoid scanning every possible port unless you have a clear reason to do so.

For a first scan, you may start with a small set of ports and a moderate number of simultaneous requests. You should focus on ports that are relevant to the devices you expect to find. For example, you might scan common services such as:

22 for SSH
80 and 443 for web interfaces
445 for SMB
3389 for Remote Desktop
9100 for network printers

The same applies to concurrency. Higher concurrency can speed up a scan, but it also sends more requests at the same time. On a busy network, this can create traffic spikes or trigger security monitoring.

A practical approach is to start with the scanner's default concurrency setting. If the scan is stable but too slow, increase concurrency gradually rather than jumping straight to a high value.

For example, users may report that printers are responding slowly during the scan. Your monitoring tools may also show a sudden spike in network traffic. If that happens, pause the scan and lower the concurrency before continuing.

The goal is not to finish the scan as fast as possible. It is to collect useful results without putting unnecessary load on the network.

Want to compare other discovery options? Read our article free asset discovery tools

Tip 4: Scan Regularly, Not Aggressively

You don’t need to run an aggressive scan every time you want to check what is connected to your network. In many cases, a lighter scan performed more regularly gives you more useful information over time.

You may run a lightweight scan every week to detect new or missing devices. If something unusual appears, such as new unknown devices appearing on the network, you can then run a deeper scan on that specific range.

This approach helps you avoid putting unnecessary load on the network. It also makes changes easier to spot because you are comparing smaller, more frequent snapshots.

A practical routine could be:

Run a lightweight scan on a regular schedule
Compare the latest results with your previous scan
Check for new, missing, or changed devices
Use deeper scanning only when you need more details

Explore the Advanced Network Discovery Method here

Tip 5: Review & Secure Scan Results

A network scan is not finished when the scanner stops running. You still need to review the results before using them for inventory, troubleshooting, or follow-up scans.

Start by checking whether the results make sense. Look for:

Unknown IP addresses
Duplicate devices
Missing hostnames or MAC addresses
Devices that appear in an unexpected subnet
Open ports that you did not expect to see

If your scan finds a device with an unfamiliar hostname, do not immediately treat it as a new asset. Check the MAC address, manufacturer, assigned IP, and previous scan results first. It may be a guest device, a temporary device, or an existing asset using a new IP address.

You should also secure the scan results after reviewing them. Scan results can contain sensitive network information such as IP addresses, MAC addresses, hostnames, device types, and open ports.

These files should be stored in an approved internal location, and access should be limited to people who need them. Avoid leaving scan exports in shared folders, personal downloads, or temporary locations longer than necessary.

FAQs

1. Why do some devices not appear in a network scan?

A device may be connected to the network but still not appear in the scan. It could be offline, blocking discovery traffic, located behind another network segment, or configured not to respond to certain protocols.

If a device is missing, first confirm that it is powered on and connected. Then check whether the scanner can reach its subnet and whether firewall rules are blocking the discovery method being used.

2. Can network scanning damage devices?

Most modern devices can handle normal network discovery without problems. However, older hardware, embedded systems, IoT devices, and specialized equipment may react poorly to aggressive or unusual requests. This is why it is important to understand what types of devices are on the network before using deeper scanning methods.

3. Can a network scan be detected by security tools?

Yes. Firewalls, IDS/IPS platforms, SIEM systems, and endpoint security tools may detect network scanning activity. A legitimate scan can sometimes look similar to reconnaissance. For planned scans, it is a good idea to make sure your security team knows where the scan is coming from and why it is being performed.

Before scanning, make sure you have authorization and let your security or network team know the source IP, target range, and planned timing.

4. What is the difference between network discovery and vulnerability scanning?

Network discovery focuses on identifying which devices are connected and collecting basic information such as IP addresses, hostnames, MAC addresses, and open services.

Vulnerability scanning goes further. It checks systems for known security weaknesses, outdated software, or configuration issues. Because it performs deeper checks, vulnerability scanning usually requires more planning and stricter authorization.

5. Should I use an agentless scanner or install agents on devices?

It depends on what information you need. Agentless scanning is useful for quickly discovering devices across a network without installing software on every endpoint. Agent-based tools usually provide deeper and more consistent information about managed devices.

For an initial network inventory, agentless discovery is often a practical starting point. You can then use agents or management integrations when you need more detailed endpoint data.

Final Thoughts

Safe network scanning does not mean making every scan as deep or as fast as possible. It means using the right scope, settings, and level of intensity for what you actually need to discover.

For your first scan, keep the process simple. Start with a smaller range, validate the scan before running it, control ports and concurrency, and review the results carefully afterward.

Put these safe network scanning practices into action with the AssetLoom Free Network Scanner. Discover devices across your network, review scan results, and export the data you need without installing agents on every device.

Agentless vs Agent-Based Discovery: Pros, Cons, and When to Use Each

Khiem Phan — Wed, 26 Aug 2026 10:13:15 +0000

If you cannot clearly see what is connected to your network, you risk working with an incomplete inventory. Unknown devices can go unmanaged, asset records can become outdated, and IT teams may struggle to know which devices actually need attention.

This is where the choice between agentless and agent-based discovery matters. In this article, we compare both approaches to see where each works best and which one fits your environment.

1. Agentless Discovery

What Is Agentless Discovery?

Agentless discovery is a method of identifying devices and collecting information about them without installing dedicated discovery software on each endpoint.

For example, an IT team can scan an office network and discover a printer at a specific IP address. The discovery tool may then identify the printer model and other available device details without installing anything on the printer.

How It Works

The discovery tool communicates with devices remotely through the network. Basic network discovery methods can detect active devices. They can also identify IP addresses and other basic information.

More advanced network discovery methods can collect deeper device details. These may include operating system information, hardware details, open ports, and device type.

This makes agentless discovery especially useful when IT teams need to understand what is connected to the network before every device is already known or managed.

2. Agent-Based Discovery

What Is Agent-Based Discovery?

Agent-based discovery collects device information through software installed directly on the endpoint.

For example, a company laptop has a discovery agent installed. The agent can continue reporting the laptop’s operating system, hardware, installed software, and device status. The agent can keep reporting this information even when the employee works from home or outside the corporate network.

How It Works

The agents gather information from inside the system and send it back to the network discovery or IT asset management platform. The device does not need to wait for a network scan to be found again. As long as the agent can communicate with the platform, it can continue reporting information even when the device is outside the corporate network.

Because the agents run on the endpoints, they can collect deeper and more consistent data than a network scan alone. This can include hardware details, operating system information, installed software, users, configuration, and device health.

3. Agentless vs Agent-Based Discovery at a Glance

Agentless and agent-based discovery solve the same basic problem: collecting information about IT devices. The main tradeoff is breadth versus depth.

Agentless discovery is easier to use across a wider range of devices, while agent-based discovery provides more consistent detail from managed endpoints. Here is how the two approaches compare across the areas that matter most.

Installation and Setup

Agentless discovery does not require software to be installed on every device. IT teams can start from a scanner or discovery server and scan a network range for reachable devices.

This makes it easier to begin when the existing inventory is incomplete. However, deeper agentless discovery may still require additional setup, such as credentials, permissions, or firewall configuration.

Agent-based discovery requires an agent to be installed on each endpoint first. This adds an initial deployment step, especially when many devices are involved. Once deployed, however, the agent can collect information directly from the device without repeatedly configuring remote access.

→ Agentless discovery is usually quicker to start, while agent-based discovery requires more upfront deployment.

Discovering Unknown Devices

Agentless discovery can scan a network to find devices not already recorded in the asset inventory. A responding IP address may reveal a forgotten laptop, a new printer, an unmanaged server, or another connected device.

Agent-based discovery generally cannot do this on its own. The software needs to be installed before the device can report itself. If IT does not know the device exists, there may be no agent on it in the first place.

→ Agentless discovery is better for finding unknown and unmanaged devices. Agent-based discovery is better suited to devices that IT already manages.

Device Coverage

Agentless discovery can work across a wider variety of network-connected assets. Besides computers and servers, it can discover devices such as printers, switches, routers, and IoT equipment.

Agent-based discovery is limited to devices that can run the agent. The device needs a compatible operating system, sufficient permissions, and a way to install and maintain the agent. This usually makes it a better fit for managed laptops, desktops, and servers than for printers or other network infrastructure.

→ Agentless discovery generally provides broader device coverage. Agent-based discovery focuses more on supported endpoints.

Level of Device Detail

Agentless discovery can range from very basic to quite detailed. A basic scan may reveal that a device exists on the network. More advanced methods can retrieve richer hardware and system details, depending on what access and permissions are available. The available data depends on network access, protocol support, credentials, and device configuration.

Agent-based discovery collects information directly from inside the endpoint. This typically gives it more consistent access to details such as hardware specifications, operating system versions, installed software, users, configurations, and device health.

→ Agentless discovery can provide useful detail, but the depth depends on the protocol and access available. Agent-based discovery is better when you need more consistent endpoint-level data.

Remote and Off-Network Devices

Agentless discovery works best when the scanner can reach the device. If a laptop leaves the office network and is not reachable through a VPN or another connected network, the scanner may no longer be able to query it.

Agent-based discovery does not depend on the device remaining on the same local network. A laptop can continue sending information while the employee works from home, another office, or while traveling. It only needs to be able to connect to the central platform.

→ Agent-based discovery is usually better for remote and mobile endpoints.

Continuous Monitoring and Updates

Agentless discovery normally updates information when another scan runs. If scans happen daily, weekly, or manually, changes between scans may not immediately appear in the inventory. The frequency may depend on network size, scan duration, and the amount of traffic generated.

Agent-based discovery can report information directly from the endpoint on a regular schedule. Some agents can also report certain changes when they happen, depending on the product. This makes agent-based discovery better suited to environments where endpoint information changes frequently.

→ Agentless discovery is a better fit when scheduled scans are enough to keep inventory reasonably current. Agent-based discovery is better when device changes need to be reported more frequently, especially across remote endpoints.

Maintenance

Agentless discovery removes the need to maintain software on every endpoint. IT teams mainly manage the scanner, discovery configuration, credentials, network ranges, and protocol access.

Agent-based discovery introduces another software component on each managed device. Agents may need to be deployed, updated, monitored, or repaired when they stop reporting correctly.

→ Agentless discovery reduces endpoint maintenance, while agent-based discovery adds an agent lifecycle that IT needs to manage.

4. When To Use Each Type

The right discovery method depends on what devices you need to find, where those devices are located, and how much detail you need to collect.

In many environments, the choice is not simply agentless or agent-based. Each approach is better suited to a different discovery need.

When To Use Agentless Discovery

Agentless discovery is a good fit when your main goal is to find what is connected to the network.

It is especially useful when:

Find unknown or unmanaged devices
Build or validate an asset inventory
Discover printers, switches, routers, IoT devices, and other assets that cannot run an agent
Avoid installing software on every endpoint
Run periodic network checks

For example, an IT team responsible for an office network may use agentless discovery to scan each subnet regularly. This helps them identify new devices, compare discovered devices against the asset inventory, and investigate anything they do not recognize.

When To Use Agent-Based Discovery

Agent-based discovery is a better fit when your main goal is to maintain detailed and ongoing visibility into managed endpoints.

It is especially useful when:

Track remote or mobile laptops
Collect deeper endpoint information
Keep device records updated more frequently
Monitor managed laptops, desktops, and servers
Maintain visibility when devices move between networks

For example, a company with a remote workforce may install discovery agents on employee laptops. The agents can continue reporting device information even when employees rarely connect to the corporate network.

When To Use Both

For many IT environments, combining both approaches provides the most complete coverage. Agentless discovery can help IT teams find devices across the network, including unknown devices and equipment that cannot run an agent. Agent-based discovery can then provide deeper and more continuous information for managed laptops, desktops, and servers.

A common approach is to use agentless discovery first to find devices across the network. Then deploy agents only to the endpoints that need deeper or more frequent monitoring. Keep running agentless scans to catch new or unmanaged devices.

FAQs

1. Does agentless discovery affect network performance?

It can, depending on the size of the scan, the protocols used, and how aggressively the scanner is configured. In larger environments, IT teams often control scan ranges, concurrency, and schedules to avoid unnecessary network load.

2. How often should you run agentless discovery scans?

There is no single schedule that works for every environment. Stable office networks may only need periodic scans, while networks with frequent device changes may benefit from more regular discovery. The goal is to scan often enough that your inventory does not fall significantly behind the actual environment.

3. Can agentless discovery work across multiple subnets or VLANs?

Yes, but the scanner needs network access to those segments. Routing rules, firewalls, and VLAN configuration can affect which devices it can reach, so larger networks may require additional scanner placement or network configuration.

4. Is agentless or agent-based discovery more accurate?

Accuracy depends on what you are trying to identify. Agentless discovery can be more useful for confirming that a device exists on the network, while agents can provide more consistent endpoint information. In practice, comparing discovery data with existing asset records often gives IT teams a more reliable inventory than relying on a single source.

5. What should IT teams do with devices found through discovery?

Discovery should lead to an action, not just a list of IP addresses. Newly found devices should be matched against existing asset records, identified where possible, and reviewed if they are unknown or unmanaged. This helps turn discovery data into a more accurate IT asset inventory.

Final Thoughts

Agentless and agent-based discovery solve different visibility gaps. Use agentless discovery when you need to find what is connected, uncover unknown devices, or cover a wider mix of network assets. Use agent-based discovery when you need deeper, ongoing data from managed endpoints.

Need a clearer view of what is connected to your network? Start with the AssetLoom Free Network Scanner to discover devices without deploying agents first.

Deep Network Discovery: SSH, SNMPv3, and WinRM Explained

Khiem Phan — Tue, 25 Aug 2026 07:44:27 +0000

A deep network discovery method helps when basic scans can find devices but still cannot tell you enough about them.

An active IP or open port may confirm that a device exists, but not its exact OS or running services. Deep discovery uses authenticated methods such as SSH, SNMPv3, and WinRM to collect this information directly from the device.

This article explains how these methods work and how to move from basic discovery to detailed device profiling.

1. Basic, Advanced, and Deep Discovery: What's the Difference?

Basic network discovery methods help IT teams see what is connected to the network. It can identify active IP addresses, MAC addresses, hostnames, and open ports. This gives broad visibility, but often not enough detail to understand what each device actually is.

Advanced network discovery methods add more information to help IT teams identify the device more confidently. Teams can start distinguishing whether a device is a Windows computer, printer, or another type of endpoint. This reduces the number of unknown or poorly identified devices in the inventory.

Deep network discovery methods go further by helping IT teams understand what is inside and running on the device. Authenticated access can reveal hardware specifications, exact operating-system versions, installed software, running services, and other detailed system information.

2. Deep Network Discovery Methods

Once a device has been found and identified, deep discovery can go further. It uses authenticated management protocols to collect more detailed information directly from the system.

Below are some common deep methods for network discovery. Each is suited to different devices and operating environments.

SSH Discovery: Collecting Detailed Information From Linux and Unix Devices

SSH stands for Secure Shell. It is widely used to remotely access and manage Linux and Unix systems. For discovery, it provides a secure way to query the system directly instead of relying only on network responses.

How SSH Discovery Works

The scanner first authenticates to the device using approved SSH credentials or keys. It can then run permitted system commands to collect inventory data directly from the operating system.

This gives the scanner information directly from the device instead of relying only on clues such as open ports or network responses.

What SSH Discovery Can Reveal

SSH discovery helps answer a more detailed question: What is running on this device, and what resources does it have?

Depending on the system and the permissions provided, it may reveal information such as:

Hostname
Operating system and version
CPU and memory
Storage and disk information
Network interfaces
Installed packages
Running services
System uptime

This can turn a basic record like 192.168.1.20 — Port 22 open into a clearer profile of the server or device behind that address.

When SSH Discovery Works Best

One of the main advantages of SSH is that it can collect detailed system information directly from Linux/Unix devices. This makes SSH useful after basic discovery has already found a device. It helps you understand the device in more detail, including its operating system, hardware, software, and running services.

However, SSH needs to be enabled and reachable, and the scanner must have valid credentials or keys. The account also needs enough permission to run the required commands. If access is restricted, the scanner may only retrieve limited information or fail to connect.

SNMPv3 Discovery: Securely Profiling Network Infrastructure

SNMPv3 stands for Simple Network Management Protocol version 3. It is used to monitor and retrieve information from managed network devices such as routers, switches, printers, and UPS systems.

SNMPv1 and SNMPv2 are commonly used to retrieve information from devices that may not have a traditional desktop or server operating system, such as routers, switches, and printers. SNMPv3 builds on this approach by adding authentication and encryption.

How SNMPv3 Discovery Works

A device needs to have SNMPv3 enabled before it can share information. The scanner connects using the correct SNMPv3 credentials and security settings.

If the device accepts the request, it sends back the management information the scanner is allowed to read. Unlike SNMP v1 and v2c, SNMPv3 can protect this exchange with authentication and encryption.

What SNMPv3 Discovery Can Reveal

A basic scan may find a router, switch, printer, or other network device without showing exactly what it is. SNMPv3 can provide the extra management information needed to build a clearer device profile.

SNMPv3 discovery helps answer a more detailed question: What is this device, and what management information can it report?

Depending on the device, it may reveal information such as:

Device name and description
Manufacturer and model
Serial number
Firmware version
System uptime
Network interfaces and their status
IP configuration
Other hardware or device-specific information

This can turn an unknown network address into a clearly identified piece of infrastructure.

When SNMPv3 Discovery Works Best

SNMPv3 can help you collect detailed information from network equipment without requiring direct access to its local interface. This makes it useful for devices such as switches, routers, printers, UPS systems, and other managed infrastructure.

However, SNMPv3 needs to be enabled and reachable. The scanner also needs the correct username, authentication settings, and encryption settings. If these do not match the device configuration, the scanner may receive limited information or no response.

WinRM Discovery: Querying Windows Devices Remotely

WinRM stands for Windows Remote Management. It is a Microsoft protocol used to remotely manage and query Windows devices. It allows authorized tools to retrieve system information from Windows desktops, laptops, and servers without accessing each device manually.

How WinRM Discovery Works

A Windows device needs to have WinRM enabled and configured for remote access. The scanner connects using approved Windows credentials.

If authentication succeeds, it can query Windows management interfaces for system information. This allows the scanner to read details directly from the operating system instead of estimating them from network behavior.

What WinRM Discovery Can Reveal

WinRM discovery helps answer a more detailed question: What Windows system is this, and what hardware and software does it contain?

Depending on the device and permissions, it may reveal information such as:

Computer name
Windows edition, version, and build
Manufacturer and model
Serial number
CPU and memory
Storage information
Network adapters
Installed applications
System configuration

This can turn a generic Windows endpoint into a detailed record of the actual laptop, desktop, or server.

When WinRM Discovery Works Best

WinRM is ideal when a Windows endpoint has already been discovered, but you need more detailed system information.

However, WinRM needs to be enabled and reachable. The scanner also needs valid credentials and sufficient permissions. Firewall rules, domain policies, or settings can also prevent the scanner from connecting or limit the information it can retrieve.

3. How to Move From Basic to Deep Network Discovery

Not every device needs the deepest level of discovery from the start. A practical approach is to begin with lightweight methods, then collect more detail only where it is useful.

Step 1: Start With Basic Discovery

Basic discovery helps you build an initial view of what is connected to the network. Methods such as ICMP, ARP, TCP, and DNS can reveal basic information like:

Active IP addresses
MAC addresses
Hostnames
Open ports

At this stage, the goal is coverage. You want to find as many relevant devices as possible without requiring credentials or direct system access.

For example, you may find: 192.168.1.20 → Active → Port 22 open

Here, you know something is there, but you may not yet know exactly what it is.

Step 2: Add Advanced Discovery for Better Identification

Next, advanced discovery can help identify them more clearly.

Methods such as SNMPv1/2, WMI, and mDNS can provide additional information about the device itself. This is useful when basic discovery leaves you with unknown or incomplete records.

For example: 192.168.1.45 → Active

may become: 192.168.1.45 → HP network printer

At this stage, the goal is identification. You know the device type and possibly the vendor but not its exact model, firmware version, or serial number. That level of detail is where deep discovery comes in.

Step 3: Use Deep Discovery Where More Detail Is Needed

Finally, use deep discovery when identification alone is not enough. It can provide details such as hardware specifications, exact OS versions, installed software, and running services.

For example: 192.168.1.20 → Linux server

may become: Dell PowerEdge R650 → Ubuntu 24.04 → 64 GB RAM → Nginx → installed packages

At this stage, the goal is profiling. You are building a detailed technical record of the device rather than simply identifying it.

Deep discovery also does not need to be used everywhere. In practice, a printer may be sufficiently identified through SNMP. A server may need SSH to collect a more complete inventory.

The right approach is therefore to start broad and go deeper where needed. This keeps discovery efficient while still giving IT teams the level of detail required for accurate asset records.

FAQs

1. Is Deep Network Discovery the Same as Vulnerability Scanning?

No. Deep Network Discovery focuses on identifying devices and collecting detailed inventory information about them. Vulnerability scanning looks for security weaknesses, missing patches, exposed services, or known vulnerabilities.

The two can use some similar network information, but they serve different purposes.

2. Can Deep Discovery Work Across Different Network Segments?

Yes, but the scanner must be able to reach the target devices and their required management protocols. Firewalls, VLAN rules, routing policies, or access controls may block SSH, SNMPv3, or WinRM between network segments.

In larger environments, discovery may therefore need to be planned around network boundaries.

3. How Accurate Is Deep Network Discovery?

Deep discovery can provide highly accurate information because much of the data is retrieved directly from the device or operating system.

However, accuracy still depends on the device configuration, available permissions, supported protocols, and the information the system makes available. Some fields may still be missing or incomplete.

4. Does Deep Network Discovery Affect Device Performance?

Usually, the impact is low when discovery is configured properly. However, deep discovery sends more queries and retrieves more information than a basic network scan.

For large environments, it is better to control scan scope, concurrency, and scheduling so that devices and the network are not queried unnecessarily.

5. How Often Should Deep Network Discovery Be Run?

There is no single schedule that works for every environment. The right frequency depends on how often devices, software, and configurations change.

Basic discovery can usually run more frequently to detect new devices. Deep discovery can then run periodically or when more detailed inventory information needs to be refreshed.

Final Thoughts

Deep network discovery adds the final layer of detail to the discovery process.

Basic discovery helps you find devices. Advanced discovery helps you identify them more clearly. Deep discovery helps you build a more complete technical profile using information collected directly from the device or operating system.

By combining the three levels, IT teams can move from a simple list of IP addresses to a more accurate and useful view of the devices across their environment.

Try the AssetLoom Free Network Scanner to quickly discover devices across your network and export the results for further review.

Advanced Network Discovery: How SNMP, WMI & mDNS Work

Khiem Phan — Wed, 19 Aug 2026 10:53:56 +0000

Basic discovery methods such as ICMP, ARP, and TCP help IT teams find devices that are active on the network. But detecting an IP address or open port does not always tell you what the device actually is.

Level 2: Advanced Network Discovery goes further by collecting richer device information through SNMP, WMI, mDNS, and other identification signals. In this article, we’ll explore how these methods work and when deeper discovery becomes necessary.

1. How Advanced Discovery Improves Device Classification

Basic network discovery methods can confirm that a device is present on the network. However, it often provides only a limited picture of what that device actually is. An IP address, MAC address, or open port may suggest a device type, but these signals are not enough for identification.

Level 2 builds on Level 1 by adding details that help IT identify and classify the device.

For example, imagine a scan finds an unfamiliar device at 192.168.1.25. Level 1 may confirm that the device is active. It may also show a hostname or a few open ports. But IT may still not know whether the device is a laptop, printer, switch, or something else.

Level 2 adds more context about the device, such as:

What type of device it is
Which operating system it is running
What services it provides

This can turn an unknown network entry into a recognizable device. It may be a laptop, printer, switch, or another type of equipment. IT asset discovery can then record it correctly and investigate it if it should not be on the network. The more signals that match, the more accurately IT teams can identify and classify the device.

2. Advanced Methods for Network Discovery

Different discovery methods reveal different types of information about a device. Next, we’ll look at the main Level 2 methods and what each one can uncover.

SNMP Discovery: Getting More Information From Network Devices

SNMP stands for Simple Network Management Protocol. It is commonly used to collect information from network-connected devices such as routers, switches, access points, and printers.

For Level 2 discovery, we mainly focus on SNMP v1 and SNMP v2c. Both use a community string to control access to device information.

How SNMP Discovery Works

A device needs to have SNMP enabled before it can share information. The scanner sends a request using the correct community string. If the device accepts it, the device sends back the information it allows the scanner to read.

That information can include details such as the device name, model, uptime, or network interfaces. So instead of seeing that 192.168.1.25 is active, you can know what kind of device it is.

What SNMP Discovery Can Reveal

A basic scan may find several network devices without showing which ones are printers, routers, or access points. SNMP can provide the extra device information needed to tell them apart.

SNMP discovery helps answer a more detailed question: What kind of device is this, and what information can it report about itself?

Depending on the device, it may reveal some information such as:

Device name and description
Manufacturer and model
Uptime
Network interfaces and their status
Network addresses

This can provide enough information to identify an unknown device as a printer, router, or another type of network equipment.

When SNMP Discovery Works Best

One of the main advantages of SNMP is that it can collect detailed device information without requiring direct access to the device interface. This makes it useful when you need more information about network equipment that basic discovery has already found.

However, SNMP needs to be enabled, reachable, and configured with the correct community string. Otherwise, the scanner may receive little information or no response. SNMP v1 and v2c also have limited security because community strings are not encrypted. SNMPv3 adds stronger authentication and encryption, which we will cover in Level 3.

WMI Discovery: Identifying Windows Devices in More Detail

WMI stands for Windows Management Instrumentation. It is mainly used to collect information from Windows computers and servers.

How WMI Discovery Works

The scanner connects to the Windows device using an account that has permission to access its information. If the connection is allowed, the scanner can ask the device for details about its hardware and operating system.

For example, instead of only seeing that 192.168.1.30 is active, IT may be able to identify it as a specific Windows laptop or workstation.

What WMI Discovery Can Reveal

A scan may find several Windows machines, but IP addresses and hostnames alone may not tell IT which laptop, desktop, or server each one is.

WMI helps you answer the question: What Windows computer is this? Depending on the device and available permissions, it may reveal:

Computer name
Manufacturer and model
Windows version
Processor and memory
Storage information
Network adapters

This gives IT a clearer picture of the Windows device than basic network discovery alone.

When WMI Discovery Works Best

WMI is ideal for Windows laptops, desktops, and servers where IT needs more detailed device information. Its main advantage is the amount of Windows system information it can provide.

On the other hand, the Windows device must allow remote WMI access. Firewall and Windows settings can prevent the connection. It is also Windows-focused, so other methods are usually more useful for printers, switches, routers, and other network equipment.

mDNS Discovery: Finding Devices and Services on the Local Network

mDNS stands for Multicast DNS. It helps devices find each other on a local network without needing a traditional DNS server. It is often used together with DNS Service Discovery (DNS-SD), which allows devices to share information about the services they provide.

How mDNS Discovery Works

The scanner sends a query to devices on the local network. Devices that support mDNS can respond with their name or information about the services they provide.

Instead of only seeing that 192.168.1.40 is active, the scanner may find that the device is sharing a printing service. This provides a strong clue that the device could be a printer.

What mDNS Discovery Can Reveal

A new device appears on the office network, but its name does not clearly explain what it does. mDNS may reveal that the device provides printing, media, or another local service. Depending on what the device shares, you may learn:

Device or hostname
Services provided
Service type
Service port
Network address
Additional information about the service

These details can help identify devices that may otherwise appear only as an IP address during basic discovery.

When mDNS Discovery Works Best

mDNS works best when IT wants to understand which services nearby devices are providing on the local network.

However, mDNS is mainly designed to work on the local network link. Its multicast queries normally do not travel across routers or separate network segments. Discovering the same services across different subnets generally requires additional discovery infrastructure, such as a discovery proxy.

The results also depend on what information each device makes available. If a device does not support mDNS or does not share useful service information, the scanner may receive little or no additional information.

3. When Should You Move to Level 3 Discovery?

Level 2 helps IT identify a device and understand more about what it is. But there is still a limit to how much information methods such as SNMP v1/v2c, WMI, and mDNS can provide.

You may need deeper discovery when you want to answer questions such as:

What software is installed?
Which services and versions are running?
What packages are installed on Linux devices?
What detailed system configuration does the device have?
How can the device be queried using stronger authentication and encryption?

This is where Level 3: Deep Network Discovery comes in.

In the next article, we’ll explore how Level 3 discovery works and when IT teams should use it.

FAQs

1. Does advanced network discovery require installing software on every device?

Not necessarily. Methods such as SNMP, WMI, and mDNS can collect information using capabilities already available on the device or operating system. However, the required service must be enabled and accessible for the scanner to use it.

2. Can advanced discovery change anything on the device?

Discovery should normally be configured as a read-only process. Its purpose is to collect information, not modify device settings. IT teams should still use accounts and permissions with only the access needed for discovery.

3. Can advanced discovery work across VLANs or different network segments?

Sometimes, but it depends on the method and network configuration. Firewalls, routing rules, multicast boundaries, and access controls can prevent discovery traffic from reaching devices on other network segments.

For larger networks, IT teams may need additional scanner locations or network configuration to improve coverage.

4. How often should IT run network discovery?

There is no single schedule that works for every organization. A relatively stable office network may only need periodic scans, while environments where devices frequently join or leave may benefit from more frequent discovery.

The goal is to scan often enough that your asset inventory does not fall far behind the actual network.

5. What should IT do when different discovery methods return conflicting information?

Treat each result as one piece of evidence rather than assuming one method is always correct.

Compare details such as the IP address, MAC address, hostname, manufacturer, operating system, and services. If the results still do not match, flag the device for further investigation instead of automatically assigning a device type.

Final Thoughts

Basic network discovery helps you find what is connected. Advanced discovery helps you understand what those devices actually are. With advanced approaches, teams can classify devices more accurately and build a clearer picture of what exists across the network.

However, Level 2 still has limits. When you need to understand installed software, running services, system configuration, or collect authenticated information, deeper discovery becomes necessary.

Stay tuned for the next article, where we’ll explore Level 3: Deep Network Discovery, including SSH, SNMPv3, WinRM, service detection, software inventory, and deeper device profiling.

Want to put network discovery into practice? Try the AssetLoom Free Network Scanner now.

AssetLoom Is Officially Launched: One Platform for All Your IT Assets

Khiem Phan — Wed, 29 Jul 2026 10:01:09 +0000

After months of preparation, we are excited to officially launch AssetLoom: an ITAM platform built to help teams manage all their IT resources in a more centralized, efficient, and organized way.

This launch is the beginning of our mission to make IT asset management clearer and more accessible for every organization.

1. Why We Built AssetLoom

IT teams often know what assets the company owns. But they may not know where each asset is, who has it, or whether the records are still accurate.

As devices are assigned, returned, repaired, or moved between locations, updates are easily missed. Asset information becomes spread across spreadsheets, emails, and separate tools, leaving teams to manually piece together the latest status.

AssetLoom was built to bring asset records and daily IT operations into one place. It gives teams a clear, up-to-date view of every IT resource. Teams can see what they own, where each item is, who is using it, and what needs attention next.

2. What’s Included In AssetLoom First Release Version

In the first release, AssetLoom can help you with:

Manage Every IT Resource: Track assets, accessories, licenses, components, and consumables in one workspace. You can keep key details and what is available, where each item is stored, and which stock levels need attention.

Track the Full Asset Lifecycle: Follow every asset from purchase and assignment to maintenance, return, retirement, and disposal. Each update becomes part of a clear activity history.

Manage Asset Allocation: Assign, reserve, and return equipment with a clear record of ownership and custody. You now know who has each asset and what its condition is after use.

IT Procurement Tracking: Track purchase requests, orders, vendors, costs, and deliveries in one place. Visibility from the initial request through to receiving and stocking is no longer the problem.

License Management: Track license availability, seat assignments, expiration dates, renewals, and software costs. Your team can act proactively and avoid unused seats or missed expiration dates.

Gain Insights from Reports And Dashboards: Turn asset data into a clear view of inventory, assignments, costs, maintenance, and license usage. Your team can quickly spot trends, identify issues, and decide what needs attention next.

3. A Thank You To Our Users

This launch would not have been possible through our team’s work alone.

We are grateful to everyone who patiently joined the waitlist, tested AssetLoom during early access, and shared honest feedback with us. Your questions, ideas, and real-world experiences helped shape the platform we are launching today.

We have recorded every suggestion and will continue using your feedback to guide future improvements. AssetLoom is built for IT teams, and we want its development to remain closely connected to the people who use it every day.

4. What Comes Next

We are already working on the next stages of AssetLoom.

October 2026: Jamf Pro and Microsoft Intune Integrations

In October 2026, AssetLoom will introduce integrations with Jamf Pro and Microsoft Intune.

For teams managing large numbers of devices, these integrations will handle much of the data work automatically. Instead of creating and updating records manually, teams can sync device data into AssetLoom on a selected schedule.

This will reduce repeated data entry while giving teams control over how information is managed across AssetLoom and their management platforms.

Q4 2026: AssetLoom Mobile App for iOS & Android

In the last quarter of 2026, we plan to release the AssetLoom mobile application for iOS and Android.

The mobile app will make it easier for IT teams to access asset information and handle asset operations while working away from their desks.

More details about its initial capabilities will be announced closer to release.

Alongside these releases, we will continue improving AssetLoom’s core features with valuable feedback from our users. This aligns with our mission to build a platform that better supports how IT teams work every day.

Final Thoughts

The launch of AssetLoom is an important milestone, but it is only the beginning.

We built AssetLoom to give IT teams a clearer and more practical way to manage every IT resource. As more teams start using the platform, we will continue learning, improving, and building around the challenges they face every day.

Thank you to everyone who has supported us throughout this journey. We look forward to shaping the next chapter of AssetLoom together.

Explore AssetLoom and start your Free Trial today: https://assetloom.app/

Avoid Delays with Component Availability for Repairs

Khiem Phan — Wed, 29 Jul 2026 09:28:32 +0000

Component Availability for Repairs can decide whether a device is fixed quickly or left waiting in maintenance.

The problem starts when you cannot confirm whether that component is actually available. The required SSD, RAM module, battery, or power adapter may be listed in a spreadsheet but already used. It may be somewhere in storage with no clear location record. In other cases, the part may simply be out of stock.

In this article, we explain the common challenges of managing repair components. Then, we also show how AssetLoom helps teams track stock, check out parts, and restock before shortages delay repairs.

1. What Is Component Availability for Repairs?

Component availability for repairs means knowing whether the required replacement part is currently in stock and ready to use. In ITAM, it includes details such as the available quantity, storage location, compatibility, and current checkout status.

For IT teams, this can apply to parts such as SSDs, RAM modules, self-repair items, and other components used to repair or upgrade devices.

However, before taking the device out of service, teams need to check the inventory to confirm some information, such as:

Whether the required replacement part is available
Where the component is stored
How many units remain in stock

Without this information, the device may be taken away before the repair is ready to begin. It may need to wait while the team checks storage, contacts support, or waits for a new part to arrive.

2. Common Problems For Component Management

There are some common problems current teams may face when managing components for repairs.

Unclear Record To Check Available Components

Component records are often split across spreadsheets, purchase documents, storage locations, and individual team members. None of these sources provides a complete, up-to-date view of the inventory.

A spreadsheet may show that a component is available, even though it has already been used for another repair. A part may also exist in storage without a clear record of its location, specifications, or compatibility.

This leaves teams unsure whether the required component is actually ready to use. One common scenario: a technician pulls an SSD from the shelf list, only to find it was already installed in another laptop. Because the spreadsheet was never updated, the repair is delayed while someone tracks down a replacement.

Manual Checking Process

When component records are unreliable, teams often have to verify stock manually. Teams may need to double-check in storage or contact other team members to confirm whether a part is available. This adds extra steps before the repair can begin.

Manual checks also make it harder to know whether a component has already been reserved or used for another repair. By the time the team confirms the stock, the device may already be out of service.

Missing Repair Component Details

Using a component is only part of the repair process. Teams also need to record where that component was used.

Without a clear equipment maintenance log, important details can be lost. IT teams may not know which SSD was installed, when a RAM was replaced, or how much the repair cost. It can also leave inventory records inaccurate when a component is used, but its quantity is not updated.

3. How AssetLoom Improves Your Component Tracking

These problems often come from managing component stock separately from the repair process.

Teams need one place to check availability, update quantities, and record where each component is used. AssetLoom brings these activities together in a clear component tracking workflow.

Step 1: Build Your Component Inventory

The first fix is simple but often skipped: one inventory, one source of truth. When component records live in a single place, you can stop guessing and start checking the available items. In AssetLoom, this list can be built manually or imported in bulk from a CSV.

After finalizing the list, one place to check is which parts are available and where they are kept. A clear component inventory helps you avoid duplicate purchases. It also reduces the time spent searching for parts. Therefore, you can prepare each repair before taking the device out of service.

Step 2: Set Minimum Quantity Threshold

The next step is deciding, in advance, how much stock is too little. A minimum quantity threshold turns "we might be low" into a clear alert before the shelf is actually empty. AssetLoom lets you set this per component and notifies admins automatically when stock falls below it.

The threshold helps you spot low stock before a component runs out. When the remaining quantity falls below the threshold, AssetLoom sends an alert to the admins. This allows the team to refill the component before it runs out.

There is no need to check shelves or review stock levels manually. Frequently used items, such as RAM modules, SSDs, and power adapters, can stay ready for upcoming repairs.

Step 3: Check Out The Component

Every checkout should be logged against the asset it was used on: which device, how much was used, and why. AssetLoom builds this into the checkout flow itself, so the record and the repair happen in the same step.

You can select the component, link it to the affected device, and record the quantity used. Notes can also be added, such as the reason for the replacement or the condition of the old parts.

Your team can get a clear record of where each component went. It also updates the remaining quantity automatically, so you see the latest stock level without manual updates.

By linking the component to the device, you can review which part was installed, when it was used, and how it contributed to the repair. This makes future troubleshooting, cost tracking, and repair decisions much easier.

Step 4: Refill Component Quantity

As components are checked out, some stock levels will begin to run low. Minimum quantity alerts help admins identify which parts need to be restocked. Once new items arrive, the team can update the component quantity in AssetLoom.

The new stock becomes visible to you in the same component list. Therefore, your team can keep frequently used parts available and help the repair workflow continue without delays.

Final Thoughts

Clear component availability does more than speed up repairs. It helps teams avoid taking devices offline too early, reduce employee downtime, and plan each repair with greater confidence. The teams that get this right usually aren't the ones with the most parts on hand, but they're the ones with the clearest record of what they have.

With AssetLoom, your team can track component stock, check out parts, and restock before shortages delay repairs.

FAQs

1. Which replacement components should IT teams keep on hand?

IT teams should prioritize parts that are used often, take a long time to reorder, or can quickly block a repair. This may include SSDs, RAM modules, batteries, power adapters, and other components used across common device models.

2. How many spare components should an IT team keep in stock?

The right quantity depends on repair frequency, delivery times, device count, and how critical the component is. Frequently used parts should have a higher minimum stock level than rarely used components.

3. How can IT teams prevent component inventory from becoming inaccurate?

Set a recurring schedule (monthly or quarterly) to physically audit high-value or high-turnover components against system records. Discrepancies usually point to either a missed checkout log or a part that was borrowed informally and never returned.

4. Should replacement components be linked to individual devices?

Yes. Linking the component to the repaired device creates a clearer maintenance history. It also helps teams track replacement dates, component costs, and recurring hardware issues.

5. When should an IT team restock repair components?

Teams should restock before the available quantity reaches zero. Minimum quantity thresholds and low-stock alerts help admins reorder critical parts early and avoid repair delays.

Starting from July 2026, AssetLoom is open for early access. Try it for free and manage unlimited assets until the end of 2026.

Shared Equipment Booking: How IT Teams Stop Double Bookings and Missing Assets

Khiem Phan — Wed, 22 Jul 2026 06:30:51 +0000

A projector is needed for an important presentation, but no one knows who has it. A laptop is supposed to be available, but it is still with the previous user who forgot to return it. An employee asks to reserve equipment, but finds out that someone else already booked it.

As more employees rely on shared IT equipment, managing these assets becomes more than a scheduling task. In this article, we will explore how shared equipment booking works in ITAM and how teams can prevent common issues.

1. What Is Shared Equipment Booking in ITAM?

Shared equipment booking in IT asset management is the process of allowing employees to request and temporarily use company-owned equipment. These assets usually do not need a permanent owner since they are only required for specific situations. A projector may be shared for events, while a loaner laptop may be issued when a main device is repaired.

Instead of assigning these items to one person, organizations keep them available for employees to book whenever a short-term need arises. Under this process, the operations team still keeps control of their availability, custody, and return status.

Because these assets are used by people at different times, a simple calendar entry is often not enough for management. Without a structured booking workflow, shared equipment can quickly become difficult to locate, double-booked, or left overdue.

2. Shared Equipment Is Easy to Book, but Hard to Control

Booking shared equipment may seem simple. An employee sends a Slack message, adds an event to a shared calendar, or asks the operations team directly. However, these methods often record only the request, not what happens to the asset afterward.

Scheduling Conflicts

Two employees may request the same item for overlapping periods because availability is not updated consistently across different channels. One request may be recorded in a calendar, while another is still waiting in a Slack thread or email.

Without clear visibility into asset availability, multiple employees may book the same equipment for the same period. The operations team may also approve overlapping requests without realizing the asset has already been reserved.

Missed Returns

A booking may include an expected return date, but that does not guarantee the asset is returned on time. Without a formal check-out and check-in process, overdue equipment may remain with an employee unnoticed.

As a result, the asset may appear available for the next booking though it is still with the previous employee. The team then has to track down the asset, update the booking, and find alternative equipment for the next user.

Scattered Communication

Requests, approvals, collection details, and return updates may be spread across Slack messages, emails, calendars, and verbal conversations.

When information is scattered, it becomes difficult to track the answers to these questions:

Has the request been approved?
Was the equipment collected?
Who currently has it?
When is it expected to return?
Is it ready for the next employee?

As the number of booking requests grows, these gaps create more manual work for IT and operations teams. What begins as a simple scheduling task can become a problem of unclear custody, inaccurate availability, and missing asset history.

3. What a Shared Equipment Booking Workflow Should Include

A reliable booking workflow should connect each request to the actual asset and track what happens from reservation to return. This gives employees a clearer booking experience while helping IT and operations teams maintain accurate asset records.

Real-time Asset Availability

Before submitting a request, employees should be able to see whether an asset is available. They should also know when it can be booked and how long it will remain available.

This visibility helps prevent overlapping requests and ensures employees only book equipment that will be ready when they need it. It also reduces the time operations teams spend checking availability across calendars, messages, and spreadsheets.

A Clear Request and Approval Process

Each request should include the specific asset or equipment type, booking period, requester, purpose, and approval status.

A consistent booking request process gives the operations team the information needed to decide which requests should be prioritized. It also helps requesters follow the progress of their booking and see whether it is pending, approved, rejected, or cancelled.

Asset Activity Log

Every booking, approval, check-out, handover, return, and status change should be recorded in the asset’s activity log.

Instead of searching through Slack messages or calendar events, the operations team can review the asset record directly. They can see what happened, when it happened, and who was involved throughout the asset’s activity history.

4. How AssetLoom Helps Manage Shared Equipment

AssetLoom connects asset records, booking requests, custody tracking, and check-in processes in one place. Let’s explore how AssetLoom helps IT teams manage shared equipment from booking to return.

Step 1: Prepare Your Asset List For Booking

Before setting up shared equipment bookings, you need to make sure all available equipment is recorded in AssetLoom.

Instead of adding each asset manually, AssetLoom allows your teams to import asset data from a CSV file. This helps you quickly upload large numbers of assets and prepare your shared equipment inventory for booking.

After importing, you have a centralized asset list where you can identify which equipment can be shared, track its current status, and prepare it for future booking requests.

Step 2: Schedule Asset Booking With Form

Now, employees can submit booking requests directly through the asset booking form.

When selecting an asset, users only see equipment that is available for booking. Assets that are already assigned, checked out, or under maintenance will not appear as available options. This prevents employees from accidentally requesting equipment that cannot be used at that time.

Before creating a booking, users can check the asset’s existing schedule and availability. This helps prevent multiple employees from requesting the same equipment at the same periods.

Instead of managing requests through separate calendars, emails, or chats, AssetLoom keeps all booking information connected to the asset record. This gives both requesters and IT teams a clear view of upcoming bookings, current usage, and expected return dates.

Step 3: Review Booking Requests

After employees submit booking requests, your team can review all requests from a centralized reservation in AssetLoom.

You have a complete view of all upcoming and pending bookings in one place. Here, you can review requests, approve valid bookings, or reject requests when the equipment is unavailable.

Once a decision is made, AssetLoom automatically updates the booking status and notifies the requester of the result. Employees can know whether their request has been approved or declined without needing to follow up with the operations team.

Step 4: Remind Asset Check-in

Keeping track of its return date is essential to ensure shared equipment remains available for other employees. You can configure automated reminders before the expected check-in date.

These reminders help employees return equipment on time without requiring the operations team to manually follow up through emails or chat messages. They also reduce the risk of assets being unavailable for upcoming bookings because they are still held by previous users.

Step 5: Confirm Asset Status And Ownership

Shared equipment is often moved between employees, departments, or locations. Without regular custody verification, asset records can become outdated even if booking records are accurate.

AssetLoom helps teams verify asset custody by keeping track of the current user, verification status, condition, and verification due date. This allows teams to confirm whether the equipment is still with the assigned user or not.

During custody verification, teams can review important details such as:

Who currently has the asset?
Whether the asset is verified or not in possession?
The current condition of the equipment?
When the verification was completed?

Instead of relying on manual follow-ups or communication history, your teams have a clear view of custody records.

Step 6: Check In Asset To Inventory

When equipment is returned, the final step is to check the asset back into inventory and update its current status.

AssetLoom allows IT teams to confirm that the asset has been returned. They can update its availability and record relevant details, such as condition changes or issues found during the check-in process.

After checking in, the asset is returned to inventory and becomes available for other employees to book for future needs. This keeps shared equipment ready for reuse while ensuring the asset status always reflects its actual availability.

FAQs

1. What types of IT equipment should be managed through shared equipment booking?

Shared equipment booking is useful for assets that are needed temporarily by multiple employees rather than assigned permanently to one person. Common examples include loaner laptops, projectors, tablets, mobile hotspots, testing devices, conference equipment, and other portable accessories.

2. How is shared equipment booking different from asset assignment?

Asset assignment is used when an employee is responsible for an asset for an extended period, such as a company laptop assigned to a specific user. Shared equipment booking is designed for temporary usage, where multiple employees can request the same asset based on their needs while IT maintains visibility into availability and custody.

3. Can shared equipment booking help reduce unnecessary IT purchases?

Yes. Booking history provides visibility into how frequently equipment is used and whether existing assets are being fully utilized. IT teams can review demand patterns before purchasing additional equipment and make decisions based on actual usage data.

4. How should IT teams manage shared equipment across multiple locations?

For organizations with multiple offices, shared equipment should be tracked by both asset status and location. This helps employees identify where equipment is available and allows IT teams to manage transfers, availability, and ownership across different sites.

5. What information should IT teams track for shared equipment?

Beyond basic asset details, IT teams should track information such as booking history, current custodian, check-out and return dates, asset condition, maintenance records, and usage activity. This creates a complete view of how each shared asset is being used throughout its lifecycle.

Final Thoughts

Shared equipment management is more than reserving an asset. Without a connected workflow, shared equipment can lead to double bookings, overdue returns, and inaccurate asset records.

Start managing your shared IT equipment with AssetLoom today for better visibility and control.

Starting from July 2026, AssetLoom is open for early access. Try it for free and manage unlimited assets until the end of 2026.

From Estimates to Real Hardware Refresh Budget Forecasts

Khiem Phan — Thu, 16 Jul 2026 06:16:14 +0000

Hardware Refresh Budget Forecasting is usually challenging because the data behind the budget is scattered across teams and records.

Every year, someone asks “How much will we need to spend on new laptops next year?” And too often, the answer is still a guess. It is based on last year’s number, a feeling, or whichever spreadsheet still opens without errors.

This article explains what hardware refresh budget forecasting is, why it is often difficult, and how AssetLoom helps teams build a clearer refresh plan.

1. What Is Hardware Refresh Budget Forecasting?

Hardware refresh budget forecasting in ITAM is the process of using asset usage data to estimate which hardware assets should be replaced in the next budget cycle.

Instead of saying, “**We need to replace around 100 laptops next year,**” teams can build a forecast from real records. For example, they may find that 86 laptops will pass the company’s 4-year refresh policy. Among them, 60 are already fully depreciated, and 25 have warranties ending soon.

This gives team leaders a clearer way to plan future hardware spending. With the right asset data, teams can answer questions such as:

Which assets are due for refresh next year?
How much will replacement cost?
Which departments will need the largest budget?
Which devices can be extended, repaired, or delayed?
What is the expected value of assets reaching end-of-life?

2. Why Hardware Refresh Budgeting Is Often Guesswork

Refresh budgets are not usually wrong because teams do not plan. They are wrong because the data behind the plan is scattered, outdated, or incomplete.

Every Team Has Part of the Picture

In practice, bringing all of this data together is not always easy. Lifecycle status may live in one tool. Depreciation may be tracked in a finance spreadsheet. Warranty dates may sit in a vendor portal. Department ownership may come from HR records, or may not be clearly tracked at all.

When teams need to match this information by hand across hundreds of devices, refresh planning becomes slow and unreliable. This is often where the forecast starts to fall apart before the budget is even built.

The cost of this shows up later, when the budget is presented. IT may ask for funding to replace 100 laptops. But finance may see a different number in the depreciation records. At that point, nobody can say for sure which number is correct. Instead of approving the budget, leadership asks for more time to 'double-check the numbers.

Spreadsheets Only Tell Part Of The Story

Spreadsheets can help teams list assets, but they often miss the full context behind each device. A laptop may look old based on its purchase date, but it may still be under warranty, or not yet fully depreciated.

When teams only rely on spreadsheets, refresh planning becomes less accurate. The budget is often estimated from last year’s spending, device counts, or manual checks instead of real usage data.

This is how budgets end up too high or too low. A team may flag 100 laptops for replacement based on age alone. But 25 of those laptops may still be under warranty. That's budget spent on devices that didn't need it, meaning less left over for the ones that actually did.

3. Key Data Needed For Forecasting

So, what data should be used to forecast a hardware refresh budget more accurately?

To move beyond estimates, teams need data that shows the asset age, condition, financial value, and ownership on the same page.

Asset Lifecycle Stage

Lifecycle status shows where each asset currently stands. It helps teams understand which assets should be included in the refresh forecast and which should not.

For example, devices that are in use may need to be reviewed for replacement, while devices in stock may be reused before buying new ones. Assets in maintenance may require attention if repair costs are increasing, and retired assets should be excluded from future planning.

Purchase Date

Purchase date helps teams calculate how long each asset has been used. This is important for identifying devices that are approaching the company’s refresh policy. By reviewing asset age, teams can estimate which devices may need replacement in the next budget cycle and plan the refresh budget earlier.

Depreciation Value

Depreciation data shows how much financial value remains in each asset. A fully depreciated device is easier to justify replacing; one with significant remaining value may be worth repairing or delaying instead.

Warranty Coverage

Warranty information helps teams compare replacement costs with repair or support options. If a device is old and its warranty is ending soon, replacing it may be more cost-effective than extending support.

Department and Assignee Data

Department and assignee data shows who is using each asset and where the replacement cost will happen. This helps teams forecast the budget by department, location, or business unit instead of only seeing one total number.

4. How AssetLoom Helps With Hardware Refresh Budget Forecasting

When these data points are connected, teams can move from a rough replacement estimate to a refresh forecast that is easier to explain, review, and approve.

AssetLoom helps bring these details together so teams can monitor refresh signals and make final budget decisions from one place.

Step 1: Standardize Asset Models And Depreciation Rules

Before uploading the asset list, it is recommended to prepare the depreciation methods and asset models in AssetLoom first.

Depreciation methods define how asset value is calculated over time, such as a 5-year depreciation period. Asset models help standardize the details for each type of hardware, such as Dell XPS, Dell Vostro, etc.

In AssetLoom, depreciation is configured at the asset model level, not separately for each individual asset. This means teams need to create the depreciation rule first, then link that rule to the correct asset model.

By setting these up in advance, uploaded assets can be mapped to the correct model and depreciation rule from the beginning. This makes the imported asset data cleaner and helps teams calculate lifecycle value, remaining value, and refresh timing more accurately.

Step 2: Import Asset List

Other asset details, such as purchase date, warranty information, etc, can be filled in the CSV file and imported together with the asset list. During the import process, AssetLoom allows teams to map the columns in the CSV file to the correct asset fields.

All the necessary asset data is centralized in one place instead of being scattered across spreadsheets, records, and files. This makes it easier to track asset information and use it later for reports, dashboards, and refresh budget planning.

Step 3: Track Results With Reports And Dashboards

After the asset list is imported, you can use AssetLoom reports and dashboard widgets to monitor refresh needs across the hardware fleet.

AssetLoom provides report templates that support both financial tracking and asset usage review. You can make use of them to track assets reaching end of life, assets with expiring warranty, etc.

Teams can also use dashboard widgets to give leadership a quick view of important refresh indicators without opening separate reports. From one place, they can monitor asset age, warranty status, total asset value, assets under maintenance, and asset distribution.

By observing trends in the dashboard, you can quickly see where refresh costs may come from. For example, an asset age widget can show how many devices are moving into older age groups. A warranty overview can highlight devices that may soon lose coverage. A total asset value widget can also help compare purchase cost with current book value.

Step 4: Review and Finalize Decisions

After teams identify the assets that may need replacement, the next step is to review the forecast and decide what should happen next.

Not every old device needs to be replaced immediately. Some assets may still be in good condition, still under warranty, or suitable for lower-priority users. Others may need to be replaced sooner because they are fully depreciated, out of warranty, or frequently repaired.

By reviewing the data together, IT, finance, and leadership can decide which assets should be replaced, which can be repaired, and which can stay in use longer.

Final Thoughts

Hardware refresh planning should not rely on last year’s spending or rough device counts.

With AssetLoom, teams can centralize asset records, connect depreciation rules, import key t details, and track indicators through reports and dashboards.

FAQs

1. How often should a company refresh its hardware assets?

It depends on the asset type, usage level, and business needs. Many companies review laptops every 3 to 4 years, while monitors, desktops, and accessories may have longer lifecycles. The best approach is to set refresh rules by asset type instead of using one timeline for everything.

2. Should fully depreciated assets always be replaced?

Not always. A fully depreciated asset may be easier to justify for replacement, but it can still be usable. Teams should also consider condition, performance, warranty status, repair history, and whether the device still supports the user’s work.

3. What is the risk of delaying a hardware refresh?

Delaying refresh can save budget in the short term, but it may increase support costs later. Older devices may have more repairs, weaker performance, expired warranties, or security limitations. The goal is not to replace everything early, but to know which delays are safe and which create risk.

4. Who should be involved in hardware refresh planning?

IT should not plan the refresh budget alone. Finance should be involved to review depreciation and budget timing. Department leaders should help confirm business needs, hiring plans, and priority users. This makes the final budget easier to explain and approve.

5. What is the difference between hardware refresh planning and hardware refresh budgeting?

Hardware refresh planning focuses on which assets should be replaced and when. Hardware refresh budgeting turns that plan into estimated costs, budget owners, and approval decisions. A good process connects both, so replacement needs are tied to real financial planning.

Starting from July 2026, AssetLoom is open for early access. Try it for free and manage unlimited assets until the end of 2026.

QR Code Inventory Check-In/Check-Out For IT Assets

Khiem Phan — Mon, 13 Jul 2026 11:05:12 +0000

QR Code Inventory Check-In/Check-Out helps IT teams manage asset handovers faster and with fewer manual errors. Instead of searching spreadsheets, typing serial numbers, or updating records by hand, teams can simply scan a QR code to access the correct asset record instantly.

In this article, you can see how QR code inventory check-in/check-out works, the problems it solves, and how to implement it with AssetLoom.

1. How Teams Often Check In & Check Out Assets

For many teams, asset check-in and check-out still depend on a manual process. When assets are issued to an employee, the help desk first finds the asset record and confirms the serial number . Then, they need to update the assignee, change the asset status, and record the new location.

The same thing happens when an asset is returned. The team finds the record again, updates the asset back to inventory, notes its condition, and decides what happens next.

In smaller teams, this process may happen through spreadsheets, shared forms, emails, or help desk notes. It may work at first, but as more assets move between users, manual tracking becomes harder to keep accurate.

Discover: What is An Asset Check-In/Check-Out System?

2. Common Problems With The Manual Check-In/Check-Out Process

As this routine repeats across new hires, device returns, replacements, and repairs, small manual steps start to add up. What looks like a simple update can become a source of delay, confusion, and inaccurate records.

Slow Asset Handovers

When employees come to the help desk for assets, the team may need to find the device in storage. Then they need to match it with the spreadsheet record and assign it to the employee.

This process requires teams to manually compare details in spreadsheets, verify asset location, and update records one by one. Repeating these steps for every asset can create queues during busy periods such as new hire onboarding or device refreshes.

Higher Risk Of Data Entry Errors

Manual typing can lead to wrong serial numbers, incorrect assignees, duplicate records, or outdated asset statuses. For example, teams may assign the wrong laptop record or mark a returned device as available too early.

These small errors make asset records less reliable. Teams may need to double-check devices manually before every checkout, return, or inventory audit.

Limited Visibility Into Asset Ownership

Over time, it becomes harder to answer a simple question: which employee has which asset? Devices may be loaned out, reassigned, or returned without the update being recorded properly. Due to outdated records, teams may struggle to track missing devices, follow up on damaged assets, or recall for offboarding purposes.

Without clear ownership records, it becomes difficult to confirm responsibility when assets are missing, damaged, or not returned during offboarding. Teams may know the devices exist in the inventory, but not be sure who is currently responsible for them.

3. How QR Code Scanning Helps With Asset Check-in & Check-out

Instead of treating every asset movement as a manual data-entry task, QR code scanning gives teams a faster way to open the right record and update it at the point of action.

Reduce Manual Effort

With QR code scanning, teams can scan the asset label to open the correct record instantly. Instead of searching spreadsheets, typing serial numbers, or checking multiple fields by hand, the help desk can move directly to the check-in or check-out action.

In that way, it turns asset handovers from a record-searching task into a quick scan-and-update workflow. As a result, teams can move through checkouts and returns with less manual effort.

Improve Record Accuracy

QR code scanning helps teams work from the correct asset record from the start. By scanning the attached QR label, teams open the asset record linked to that specific physical item. That means the record teams see during a reassignment or repair reflects the asset's real status, not last quarter's spreadsheet entry.

When devices need to be reassigned or sent for repair, the record is more likely to show its real status. When they need to be recalled during offboarding or verified during an audit, teams can also see who owns them and where they should be.

Make Asset Information Easy to Access

QR code scanning makes it easier for teams to view asset details. Instead of searching or filtering by serial number, they can scan the QR label and open the asset information directly.

This is useful when teams need to check who owns the asset or where it is located. They can also confirm its current status, such as available, in use, returned, or waiting for maintenance.

4. How AssetLoom Supports QR Code Inventory Workflows

AssetLoom helps teams bring QR code scanning into their daily asset workflows. From generating QR codes to scanning assets for check-in, check-out, or record updates, teams can manage the process directly within a single ITAM platform.

Step 1: Prepare Your Asset List

First, you need a clean asset list in AssetLoom. This includes basic details such as asset name, category, serial number, asset tag, and other related information.

AssetLoom allows you to add assets manually or import them in bulk with a CSV file. This creates the foundation for a centralized platform where teams can manage asset details before moving into QR code tracking.

Asset tags are important because they act as the unique identifier for each asset. In AssetLoom, you can enable auto-generated asset tags when creating assets manually or importing them in bulk. This helps keep asset records consistent, avoids duplicate identifiers, and removes the risk of assigning the wrong device.

Step 2: Generate QR Codes

Once your asset list is ready, the next step is to generate QR codes for them. You can select one or multiple assets and generate QR codes in bulk, making it easy to prepare labels for an entire inventory.

AssetLoom also lets you customize the QR codes before exporting them. You can choose the QR code size, error correction level, and displayed information. Now, your team can create labels that fit both their physical assets and internal identification needs.

Step 3: Label QR Codes To Your Assets

Once the QR codes are printed, you can attach them to the corresponding physical assets. After every asset has its QR code attached, your inventory is ready for quick scanning and real-time updates in AssetLoom.

Step 4: Scan Assets To Check In, Check Out, Or Update Records

All the setup is complete. You can now scan the QR label to check assets in, check them out, or update asset records. The scan opens the asset information tab directly, so the help desk can quickly view and update details without the manual searching that slows down handovers.

Every change is automatically recorded in the asset's history, giving teams a complete audit trail of who made the update, what changed, and when it happened. Questions like 'who currently has this laptop' or 'was this device returned' are always answerable. You can maintain accurate records and provide full visibility into every asset movement throughout its lifecycle.

Final Thoughts

Manual asset check-in and check-out may work for small inventories, but it becomes increasingly difficult to manage as more devices move between employees, locations, and departments. QR code scanning simplifies the process by reducing manual effort, improving record accuracy, and making asset information instantly accessible.

Ready to simplify asset check-in and check-out? Start managing QR code inventory workflows with AssetLoom.

New Hire Onboarding Workflow: From Request to Ready-To-Use Packages

Khiem Phan — Thu, 09 Jul 2026 07:03:27 +0000

A new hire onboarding workflow helps teams prepare the resources a new employee needs before their first day. But when requests, approvals, records, and assignments are managed through scattered messages and spreadsheets, the process can quickly break down.

In this article, we’ll explore how AssetLoom onboarding kits help teams standardize and automate new hire preparation.

1. New Hire Onboarding Workflow

What Is A New Hire Onboarding Workflow?

A new hire onboarding workflow is the process of preparing resources for new employees before their first day, so they can start work smoothly. In ITAM, this refers to the journey from the initial request to the handover of IT assets, accessories, licenses, and other resources.

For example, when a new employee joins the company, the operations team may need to prepare and assign a laptop, headset, and software access before their first day.

A Typical Workflow In Most Workplaces

In many workplaces, the process begins like:

A manager/ leader sends a request through email, Teams, Slack, or chat. The message may include the employee’s name, start date, role, and the equipment they need.
The IT operations team checks to see which ones are available and whether the requested device can be assigned.
If approval is required, the request may be confirmed in a separate email thread, chat message, or meeting discussion.
Once approved, the team selects a suitable laptop or device and prepares it for the new hire.
After the handover, the asset assignment is manually updated in a spreadsheet or asset record.

2. Why The New Hire Onboarding Process Is Broken

Onboarding should be simple, but the current preparation flow depends too much on scattered communication and manual tracking. As a result, the workflow can easily break down when the requests scale.

Scattered Requests

New-hire requests may come from email, Slack, or direct messages, making them easy to miss. One request can be sent through chat, another may be via email, while approval may happen in a separate conversation.

As more requests come in, the onboarding workflow becomes difficult to prioritize and track. Teams may know that a new hire needs equipment, but they may not have a clear view of who requested it, whether it has been approved, or what still needs to be prepared.

Outdated Records

Many teams still manage asset stock in spreadsheets, where updates depend on manual input from different people. Over time, these records can become outdated and fail to reflect the latest asset status.

This creates a gap between what the record shows and what the operations team can actually use. A laptop may still be marked as available even though it has already been reserved for another new hire. Then, teams often spend extra time double-checking device availability before every onboarding request.

The process becomes even more complicated when onboarding packages include more than just core assets. A new hire may also need accessories and software licenses to start working properly. However, these items are often tracked separately from the main asset record. As a result, the workload for operations teams increases as the company scales.

Limited Automation

In a manual workflow, every step depends on someone remembering to take action, follow up, and update the right record. The operations team may need to check inventory, confirm approval, reserve resources, update spreadsheets, and record the final handover manually.

Over time, these repeated tasks can quickly create delays and inconsistencies. The impact becomes even clearer when teams may struggle to answer basic questions such as:

Who approved the request?
Which laptop was assigned?
When was it handed over?
What accessories were included?
Which software licenses were allocated?

3. What Can Be Enhanced For A Better Employee Onboarding Flow

So, what should a better employee onboarding flow look like?

A better onboarding flow starts with giving operations one clear place to manage the full process. Instead of separate sheets, teams should be able to see requests, available stock, and resource status in one centralized platform.

The onboarding package should also be managed as a complete setup, not just individual resource assignment. When the onboarding bundle is prepared together, teams can reduce repeated assignments and delays on the new hire’s first day.

Automation can further improve the process by removing repetitive manual tracking. For example, teams can reserve available resources, update status after assignment, and keep a clear record of the handover automatically.

4. AssetLoom Onboarding Kits

This is where AssetLoom onboarding kits can help. Teams can create predefined kits that group assets, accessories, consumables, and software licenses together, then assign the full setup to a new employee in a structured workflow.

Let’s explore how to set up the onboarding kits in AssetLoom:

Step 1: Add Resources to AssetLoom

First, you need to add all resources that may be included in a new hire onboarding package. This includes core assets such as laptops, tablets, or mobile phones, as well as accessories, consumables, and software licenses.

For teams that already manage inventory in spreadsheets, AssetLoom allows bulk import from CSV files. This makes it easier to migrate existing asset records into AssetLoom without recreating everything manually.

Once imported, teams can view each resource with details such as available quantity, current status, location, and ownership information. This creates a clear inventory foundation before onboarding kits are created.

Instead of checking spreadsheets to guess what might be available, you can see which resources are ready to be assigned.

Step 2: Create Predefined Kits

Next, you can create predefined onboarding kits for different roles, departments, or work setups. Each kit is created by adding existing assets, accessories, software licenses, and consumables into one reusable package.

For example, a New Hire Onboarding Kit may include a laptop, keyboard, mouse, Jira licenses, and some office stationery. You can also define the quantity of each item in the kit, so every new employee receives a consistent setup.

This helps you avoid preparing each item one by one for every new hire. Instead, you can simply select the right onboarding kit to check the availability of items and assign it to the new employees on their first day.

Step 3: Set Up Auto Checkout For Onboarding Kit

By default, you can check out a predefined kit to any user whenever you want. This is useful when you want to manually assign a specific kit to an employee on demand.

AssetLoom can also automate this process. By selecting a predefined kit as an onboarding kit, the resources inside the kit can be automatically assigned to a new member when they join the site.

Because the resources for new hires are already standardized in the onboarding kit, you no longer need to go back and forth with requesters to confirm every item. You only need to double-check available stock, and AssetLoom can handle the checkout process automatically.

Learn more: Automation in ITAM

Step 4: Invite New Onboarding Member

The final step is to invite the new employee to AssetLoom. Once the member joins the site, AssetLoom can automatically assign the onboarding kit that was selected in the previous step.

All resources included in the kit, such as assets, accessories, licenses, and consumables, are connected to the new member’s profile. This gives you a clear view of what has been assigned and when they are assigned to the employees.

Final Thoughts

New hire onboarding is more than assigning a single laptop. It requires clear requests, accurate stock records, complete resource packages, and reliable handover tracking.

With AssetLoom onboarding kits, teams can standardize what each new hire receives, automate checkout, and keep every resource under control.

Data Granularity: The Hidden Factor Behind AI Testing Quality

Khiem Phan — Mon, 15 Dec 2025 10:23:23 +0000

Data granularity plays a crucial role in how we understand, evaluate, and improve AI systems. In AI testing, granularity isn’t just a data feature but directly impacts how accurately we measure model performance.

In this article, we’ll explore why data granularity matters in AI testing, the different levels of granularity you can use, and how to choose the right level for each stage of development. We’ll also highlight the common mistakes and how to avoid them, helping you create more accurate AI test strategies.

1. Data Granularity In AI Testing

Data granularity refers to the level of detail contained within a dataset. It can range from detailed data (individual user actions, historical logs, etc) to general data (overall trends, summarized metrics, etc).

In traditional analytics, granularity determines how deeply you can analyze trends. But in AI testing, granularity plays an even more critical role. It defines how precisely you can evaluate AI behaviors, uncover edge cases, and trace failures back to their root cause.

2. How Important Data Granularity Is In AI Testing

When you're testing an AI system, the level of detail in your data doesn’t just influence the results. It also shapes how well you understand the AI’s behavior in real situations.

Model Comparisons

Two AI systems can only be compared fairly if they’re tested with data at the same level of detail. Otherwise, the comparison becomes biased. For example, you give two AI systems different sets of data and request them to generate new test cases. One is detailed with requirement descriptions, while another is only with a few bullet points and notes. As a result, two models generate different output quality. It is not because one model is smarter, but because it was given more information to work with. This is especially important in some AI testing techniques, such as Pairwise, to ensure the comparisons are objective and meaningful.

Catching Edge Cases

Many AI failures happen in the tiny details. When you only provide the AI with general, simplified information, many edge-case issues never appear during testing. They only surface later, when real users interact with the system in unpredictable ways. The absence of these issues in early tests doesn’t mean they don’t exist. It simply means your data was too oversimplified to reveal them. For example, imagine testing an AI that validates shipping addresses. If you only provide clean, well-formatted examples, the model may look perfectly accurate. But once you include real-world variations (missing apartment numbers, slightly reordered fields, etc), you will see where the model struggles. Detailed data helps expose these weaknesses early, before they become customer-facing issues.

Discover how Agiletest AI-Generator can help you create test cases with detailed test steps now

Efficient Testing

More granularity isn’t always better. Sometimes, highly detailed data creates unnecessary complexity and slows down your testing efforts. For instance, if you’re checking whether an AI can identify the defect trends in a testing report, you don’t need the entire article with every paragraph included. A short summary may be enough to verify the model’s understanding. This can help you save more time and resources in the AI testing process.

3. The Three Levels of Granularity

Now that we understand why granularity matters, the next step is choosing the right level for your testing goals. Not all granularity is the same; too much or too little detail can distort your results or slow down your process.

To make this easier, data granularity can be grouped into three levels: high, intermediate, and low. Each serves a different purpose in AI testing. In the next section, we’ll look at what each level means and when to use it.

High (Fine) Granularity

High (Fine) granularity means your data is extremely detailed. Every action, field, or element is captured and treated individually. For example, they can be the detailed requirement documents, user flows, acceptance criteria, etc. This level is useful when you need to understand exactly how an AI system behaves or where it fails. You can use these data for debugging model errors, testing edge cases, etc.

Intermediate Granularity

Intermediate granularity provides a balanced level of detail. The data isn’t overly complex, but it includes enough information for meaningful analysis. They can be requirement summaries, brief explanations, key notes, etc. This is the most commonly used level in AI testing because it offers clarity without overwhelming the system. Those data can be applied in comparing models, evaluating model performance, etc.

Low (Course) Granularity

Low (Course) granularity uses broad or summarized data. It removes fine details and focuses on the big picture. It comes in handy for quick checks or high-level evaluations where detail isn’t necessary. Some examples of these data are pass/fail results, testing outcomes documents, etc.

4. How To Choose The Proper Granularity

Choosing the right granularity is not about picking “more detail” or “less detail”. The key is selecting the level that aligns with your testing purpose. The ideal granularity depends on what you want to learn and how deeply you need to evaluate AI output.

Choose high granularity when you're in the early development stage, where understanding the AI’s behavior in detail is essential. At this stage, small mistakes have a big impact, and every step of the AI’s reasoning needs to be visible.

Intermediate granularity becomes most valuable during the pre-production stage, where you need reliable, consistent inputs to test how well the AI performs under typical real-world conditions. It gives the AI enough context to perform meaningful tasks without overwhelming it or slowing down the testing process.

You should select low granularity once the AI reaches the production stage, where you primarily want quick insights rather than deep analysis. At this point, you’re looking for broad patterns and indicators that tell you whether the system is generally healthy. This level is best for broad assessments rather than deep evaluation.

5. Common Mistakes In Practice

Even with a solid understanding of granularity, many teams struggle to apply it effectively in real AI testing workflows. There are some common mistakes teams encounter when testing AI.

Using One Granularity Level for Everything

A frequent mistake is using only a single level of granularity throughout the entire testing process. Teams often rely on whatever data they used at the start and apply it to every stage, from development to post-production.

Different stages require different levels of detail. Early development needs detailed data to uncover reasoning errors, while production monitoring benefits more from general summaries that highlight trends. Using one granularity everywhere results in either over-testing simple tasks or under-testing critical scenarios.

Ignoring Granularity When Investigating Failures

When an AI system produces an unexpected or incorrect output, teams often focus solely on the result. They overlook a key question: Was the input at the right level of detail?

Sometimes, it is not about the models you are using. Instead, there could be problems with the data you input for AI. In other words, investigating output without reviewing input is like troubleshooting a device without checking whether it was plugged in.

Assuming More Data Automatically Means Better Testing

Another common misconception is believing that providing more details will always improve testing outcomes. While detail is valuable in the right scenarios, it’s not a universal solution. Aforementioned, too much data could slow down the AI performance and testing process. In many cases, a concise and focused input produces a more reliable test result than a large, detailed one.

Final thoughts

Choosing the right level of data granularity is essential for meaningful and reliable AI testing. The level of detail you provide shapes how accurately you can evaluate model behavior and uncover potential issues. In the end, effective AI testing isn’t about using more data; it’s about using the right data at the right stage. By adjusting granularity thoughtfully, you can achieve clearer insights, faster testing cycles, and more dependable AI performance.

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