IoT security gaps are widening as devices outpace controls

At a glance

What this is: This is an IoT security analysis arguing that rapidly growing device fleets, shadow devices, and weak lifecycle controls create expanding attack paths for ransomware, remote exploitation, and supply chain compromise.

Why it matters: It matters to IAM and security teams because IoT devices behave like non-human identities in practice, and unmanaged device authentication, access, and offboarding can become the weakest trust boundary in the environment.

By the numbers:

• The IoT market with blockchain is expected to exceed 2,400 dollars in 2026.

👉 Read GlobalSign's analysis of IoT security risks and device identity controls

Context

IoT security fails when organisations treat connected devices as passive infrastructure instead of distributed entry points with their own authentication, firmware, update, and lifecycle risks. As device populations grow, the problem becomes governance as much as technical defence, especially where shadow IoT and weak access control create untracked trust relationships.

That matters to identity and access teams because many IoT devices function like machine identities: they authenticate, exchange data, and persist beyond the visibility of traditional user access reviews. In that sense, IoT risk overlaps with workload identity, secrets handling, device trust, and offboarding, even though the article is framed as a broader cyber security analysis.

Key questions

Q: How should security teams govern IoT device access in large environments?

A: Security teams should govern IoT access with the same discipline used for other non-human identities: assign unique device identities, segment networks, log ownership, and require explicit approval before devices join production. Access should be conditional on purpose, firmware state, and lifecycle status, not just on whether the device can technically connect.

Q: Why do shadow IoT devices create more risk than known managed devices?

A: Shadow IoT creates more risk because unmanaged devices sit outside inventory, patching, and revocation processes. If a device is not visible, it cannot be trusted, monitored, or retired properly. That means attackers can exploit its blind spot without triggering the controls that normally protect managed assets.

Q: What breaks when IoT devices are not segmented from core systems?

A: When IoT devices are not segmented, compromise of one weak endpoint can become a bridge into business systems, identity services, or sensitive data stores. Segmentation limits lateral movement, contains malware, and reduces the blast radius when a device is exposed through default credentials, weak firmware, or a remote-execution flaw.

Q: Who is accountable when IoT device data is accessed improperly?

A: Accountability should rest with the team that owns the device lifecycle and the permissions behind it, not with the hardware alone. If access is broad, unreviewed, or poorly monitored, the problem is governance as much as technology. Frameworks such as NIST Cybersecurity Framework support that accountability by tying asset management, access control, and recovery together.

Technical breakdown

How IoT devices become entry points for attackers

Connected devices expand the attack surface because each exposed service, default credential, outdated firmware image, or remote-execution flaw can become an initial access path. IoT fleets are especially attractive because defenders often cannot inventory every endpoint, let alone patch them on a consistent schedule. Once attackers land on a weak device, they can use it as a foothold for malware installation, data access, or pivoting into internal systems. The practical issue is not that IoT is uniquely vulnerable, but that it is frequently deployed faster than authentication, segmentation, and update discipline can be enforced.

Practical implication: segment IoT networks, inventory every device, and treat exposed management interfaces as high-risk attack surfaces.

Why shadow IoT breaks trust and visibility models

Shadow IoT refers to connected devices that are deployed, connected, or maintained outside security oversight. These devices may bypass procurement review, miss configuration baselines, or remain unpatched because no one clearly owns them. From a governance perspective, that creates an identity problem as well as an asset problem: if a device cannot be reliably identified, authorised, and retired, it cannot be trusted. Shadow IoT also undermines monitoring because defenders lose the baseline needed to distinguish normal device behaviour from anomalous activity.

Practical implication: require device registration, ownership, and lifecycle tracking before any IoT device is allowed onto production networks.

How blockchain and generative AI fit into IoT security

The article argues that generative AI can help detect behavioural anomalies and support authentication decisions, while blockchain can create tamper-evident records and decentralised trust for device interactions. The security value is not magic automation, but improved integrity and verification at scale. Generative AI is most useful when it learns normal telemetry patterns and flags deviation quickly. Blockchain is most relevant when multiple parties need assurance that device transactions or data exchanges have not been altered. Neither technology replaces basic controls such as patching, segmentation, or identity governance.

Practical implication: use AI and blockchain selectively as control enhancers, not substitutes for device hardening and access enforcement.

Threat narrative

Attacker objective: The attacker objective is to turn a weak connected device into a durable foothold for disruption, data theft, or wider network compromise.

1. Entry occurs when attackers exploit weak IoT devices, remote-execution flaws, or exposed services to gain an initial foothold on the device itself.
2. Escalation happens when the compromised device is used to install malware such as Mirai or to stage ransomware movement into the wider IT environment.
3. Impact follows when attackers steal data, disrupt operations, or use the compromised device as a supply chain insertion point for broader compromise.

NHI Mgmt Group analysis

IoT security is a machine identity problem as much as a device security problem. The article focuses on exposed devices, but the governance issue is that each device is effectively a non-human identity that authenticates, transacts, and persists. That places IoT squarely alongside workload identity, secrets management, and lifecycle control. Teams that only think in terms of endpoint patching miss the access governance layer entirely.

Shadow IoT creates the same visibility failure that weak non-human identity programmes create elsewhere. If an organisation cannot enumerate and own every connected device, it cannot verify trust, revoke access cleanly, or distinguish sanctioned telemetry from unmanaged behaviour. That is why visibility is a prerequisite for control, not a reporting nicety. The practical conclusion is that IoT governance belongs in the same conversation as identity inventory and offboarding.

Blockchain and generative AI are control enhancers, not compensating controls for poor governance. The article is right to note that both can improve integrity and anomaly detection, but neither closes the basic gap created by weak authentication, poor segmentation, and unmanaged lifecycles. In mature programmes, these technologies sit on top of identity and policy controls, not in place of them. Practitioners should treat them as force multipliers only after baseline control hygiene is in place.

IoT risk compounds when access decisions are separated from device lifecycle management. Devices can stay connected long after their business purpose changes, which creates standing trust and stale credentials. The named concept here is device trust drift: the gap between a device's original approval and its current security posture. Practitioners should make lifecycle status a live access condition, not an administrative afterthought.

The article signals a broader convergence between IoT governance and identity governance. As connected devices grow into the tens of billions, the question is no longer whether they need security, but whether organisations can govern their identities, privileges, and retirement paths with the same discipline applied to human access. That should push security teams toward shared ownership between IAM, infrastructure, and IoT operations.

What this signals

IoT programmes are converging with identity governance because devices now behave like durable non-human actors that must be inventoried, authenticated, segmented, and retired. In practice, that means IAM and infrastructure teams need shared policy ownership, not separate checklists. The control question is whether device trust can be revoked as cleanly as user access.

Device trust drift: this is the gap between the approval a connected device received at onboarding and the security state it has today. It grows when firmware, network placement, and business purpose change faster than governance records. Organisations should treat drift detection as a standing control, not a periodic audit task.

The broader signal is that connected-device risk will keep expanding unless lifecycle status becomes part of access enforcement. That makes lifecycle management, certificate hygiene, and segmentation more important than AI-assisted detection alone. For identity teams, the lesson is clear: machine identity and device identity are now operationally linked.

For practitioners

• Inventory all connected devices continuously Build a single authoritative register for IoT assets, including owner, purpose, firmware state, network location, and retirement date. Do not allow devices to bypass registration before they can receive trust or network access.
• Treat device authentication as a policy control Require unique device identities, enforce certificate-based authentication where possible, and remove shared credentials from embedded systems and administrative workflows.
• Segment IoT traffic from core systems Place IoT devices into tightly scoped network zones with explicit allowlists, so compromise of one device does not automatically create lateral movement into business systems.
• Tie lifecycle status to access revocation Revoke or quarantine devices that are no longer supported, no longer owned, or no longer needed. Access should end when business purpose ends, not when a replacement project eventually completes.
• Use anomaly detection only after baselining normal behaviour Train detection models on expected device telemetry, then investigate unusual authentication patterns, traffic destinations, or update behaviour before trusting AI-assisted alerts.

Key takeaways

• IoT devices behave like machine identities, so weak onboarding, authentication, and retirement controls create the same governance gaps seen in other non-human identity programmes.
• The article's evidence points to a rapidly expanding attack surface where remote execution, ransomware, and supply chain compromise all become easier as device fleets grow.
• The most effective response is to combine inventory, segmentation, lifecycle revocation, and conditional trust so that device access ends when device purpose ends.

Key terms

• Shadow IoT: Shadow IoT is the set of connected devices that exist or operate outside security oversight. These devices are often unregistered, poorly patched, and unmanaged by a clear owner, which makes them difficult to trust, monitor, or retire safely.
• Identity Trust Drift: The gap between the access model an organisation thinks it operates and the access reality created by constant change. It shows up when ownership, entitlements, and business context fall out of sync, leaving identity controls technically present but operationally stale.
• Machine Identity: The digital identity of a machine, device, or workload — such as a server, container, or VM — used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
• IoT Lifecycle Management: IoT lifecycle management is the process of tracking connected devices from acquisition to retirement. It includes onboarding, patching, monitoring, ownership, replacement, and secure decommissioning, all of which determine whether a device remains a controlled asset or becomes a lingering risk.

What's in the full article

GlobalSign's full article covers the operational detail this post intentionally leaves for the source:

• Specific examples of IoT device hardening measures and how they map to connected-device risk.
• More detail on generative AI and blockchain use cases for anomaly detection and tamper-evident trust.
• The article's discussion of DSPM and zero trust as part of broader IoT security design.
• Additional context on lifecycle management, employee awareness, and device replacement practices.

👉 GlobalSign's full post covers the threat patterns, AI and blockchain angles, and IoT lifecycle measures in more detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, secrets management, and identity lifecycle control. It is designed for practitioners who need a stronger governance model for connected systems and non-human access.