What No One Tells You About Passwordless Authentication—and Why It’s Riskier Than You Think: cyber-physical asset inventory data quality for vulnerability management

Passwordless authentication is often sold as a security win: no more password reuse, fewer credential leaks, and smoother user experiences. But in cyber-physical environments—where IT identity systems meet OT/ICS realities—passwordless can become a dangerous decoy. The hidden risk isn’t that passwordless is “inherently insecure.” It’s that organizations frequently pair it with weak visibility and unreliable asset records. When vulnerability management depends on context, cyber-physical asset inventory data quality for vulnerability management becomes the real bottleneck, and passwordless can make that bottleneck harder to detect.
In practice, authentication changes who can log in, but poor asset data determines what security actions can correctly target. If those two pieces don’t align, you can speed up access while slowing down accurate remediation. That tradeoff is usually the opposite of what leadership expects.
Think of it like this: passwordless is a faster front door, but your asset inventory is the map. If the map is wrong, you can still arrive quickly—just to the wrong building. Another analogy: passwordless is a smoke detector with a refined alarm tone, while inventory data quality is whether you know which room the smoke is in; if you don’t, the alert won’t translate into the right response. Or consider a GPS app with better routing (passwordless) but outdated road names (inventory data): your “confidence” rises while your accuracy drops.
This article explains why the overlooked interaction between passwordless security and asset context is risky—especially for critical infrastructure—using practical, governance-first guidance.

Why passwordless breaks asset visibility and raises risk

The term “passwordless” typically refers to authentication methods like FIDO2/WebAuthn, passkeys, or certificate-based access. These technologies reduce credential theft risk, but they also change operational flows:
– Access decisions may shift from user/password context to device-bound or session-bound signals.
– More activity can happen “quietly” via automated clients, service accounts, or device identities.
– Alerting often focuses on login success/failure rather than downstream device targeting.
In IT, that’s usually acceptable because your asset records are comparatively complete and normalized. In OT/ICS and heterogeneous CPS environments, however, the security workflow hinges on mapping: which device is affected, which firmware/app version is in use, and which CVE advisories truly apply.
When passwordless is introduced without strengthening asset context, you can inadvertently create a pattern like this:
1. Authentication becomes easier (and sometimes more automated).
2. Security tooling sends more urgent notifications because privileged access or connectivity improves.
3. Vulnerability management still relies on asset inventories that lack the right product identifiers, OS details, or model/vendor mapping.
4. Teams respond by guessing, manually reconciling, or delaying patches—because the data to target correctly isn’t there.
That’s where the risk compounds. Passwordless reduces one class of exposure (credential compromise) while the organization remains exposed to another class: mis-targeted or delayed remediation caused by poor asset-to-vulnerability association.
A useful mental model is to separate identity from context:
– Passwordless answers: “Who are you / what session is this?”
– Inventory quality answers: “What device is this relevant to, and what exactly should we patch?”
If your context layer is wrong or incomplete, passwordless can even accelerate the operational churn—more alerts, more access attempts, more “security work,” but without improved certainty.
In critical environments, this is more than an operational inconvenience. It can influence incident outcomes, because attackers and defenders both rely on device identification. Attackers want a reliable path from a vulnerability to a target. Defenders need the same path from a vulnerability to a device.

Cyber-physical asset inventory data quality for vulnerability management basics

Before fixing anything, you need a shared baseline. Vulnerability management in CPS environments isn’t just scanning—it’s translation: from advisories to affected devices, and from affected devices to prioritized, actionable remediation.
To do that translation reliably, you need inventory fields that consistently support mapping.
Cyber-physical asset inventory data quality is the degree to which your asset records are accurate, complete, and standardized enough to support security decisions. For vulnerability management, that generally means:
– Identity fields that map to vendors/OEM catalogs: product code, model, hardware identifiers, and sometimes serial numbers.
– Platform fields for matching: OS name/version (where applicable), firmware versions, and component versions.
– Timeliness and change control: updates when devices are replaced, re-flashed, or upgraded.
– Relationship fields: how assets relate to processes, dependencies, and business/operational impact.
If those elements are missing or inconsistent, automation breaks. Manual processes can “work” short-term, but they don’t scale, and during incidents they degrade into guesswork.
You can think of inventory data quality as a three-part system:
1. Completeness: do you have the fields required for matching?
2. Accuracy: do those fields actually match the device’s real-world identity?
3. Consistency: can you normalize them across vendors and reporting formats?
When any one of these fails, vulnerability management accuracy suffers. And in a passwordless rollout, you’re often increasing the volume of authentication-driven activity without fixing the underlying translation accuracy.
Programmable Logic Controllers (PLCs) and other OT devices often look “simple” on paper: a vendor, a model, maybe a firmware version. But in real deployments—especially multi-site, multi-vendor estates—PLC product code identification frequently fails. Even if your network discovery detects a device, discovery does not guarantee that the product identity needed for matching is correct.
A key failure mode is mismatch between what your inventory records contain and what the vendor’s catalog expects.
In large datasets of CPS assets, product codes commonly fail exact mapping to vendor records. One research finding reported that 88% failed to transmit an exact product code, and many systems sent codes that didn’t match the vendor’s own record.
Here’s what that means operationally: you can know a PLC exists (and even know it’s “a PLC”), but you can’t reliably determine which firmware or CVE statements apply. That mismatch turns vulnerability management into a probability game—close enough might be patched, but the wrong device might be targeted.
Analogy: it’s like trying to order parts using a SKU number that’s been partially truncated. The warehouse may still send a box, but it might not solve the maintenance problem—and sometimes it creates a new one.
Even when product code identification partially works, automated CVE matching often breaks because OS and platform data are missing. In some environments, a large portion of devices do not provide OS version information, or even OS name.
CVE advisories rely on specific affected versions. If the device doesn’t supply those version signals—or if your inventory doesn’t capture them—the system cannot confirm applicability.
The result is either:
– False negatives (you miss vulnerable devices), or
– False positives (you treat devices as affected when they aren’t), or
– Operational paralysis (teams delay because they can’t prove whether an advisory applies).
Together, product identity mismatch and missing platform data create CVE-to-device mapping failures—a critical weakness that passwordless can indirectly worsen by increasing the operational frequency of security workflows.
PLC product code identification is the process of determining the exact PLC hardware/product code (and related model identifiers) for a device in order to map it to vendor/OEM catalogs. In vulnerability management, correct product code identification enables reliable matching between device identity and CVE advisories reliability gaps—because without correct codes, you can’t confidently determine which vulnerabilities or firmware updates apply.

Trend: Attackers and alerts outpace unreliable inventories

As passwordless authentication spreads, privileged operations can become more seamless. Meanwhile, vulnerability discovery and alerting continue to accelerate. The gap emerges when the pace of alerts surpasses the ability of teams to validate device applicability.
That’s especially true in critical infrastructure, where OT assets are diverse and often poorly documented in centralized inventory systems.
CVE advisories are only as reliable as the vendor and ecosystem data behind them. In practice, CVE advisories reliability gaps appear when:
– affected version lists are incomplete or inconsistent,
– vendor data arrives late or is hard to normalize,
– third-party reporting loses device-specific details.
In critical infrastructure, the impact is amplified. Devices are replaced less frequently, firmware change windows are strict, and downtime can be operationally costly.
If vendors publish advisories without full clarity on affected configuration permutations, a downstream tool cannot confidently map “affected” to “your asset.” This can produce advisories that are directionally helpful but operationally ambiguous.
A simple analogy: it’s like airline guidance that says a flight might have turbulence “somewhere in the region.” Without coordinates or severity by route, you can’t decide what the crew should do right now.
During incidents, teams often need answers immediately: “Is this device vulnerable, and what’s the remediation path?” Mapping breaks when inventory lacks:
– correct PLC product code identification
– firmware/OS version fields
– consistent normalization across vendors
Then, even with faster authentication (passwordless), remediation is delayed because the security system still can’t tie the advisory to a specific device instance with confidence.
In this sense, unreliable inventories turn incident response into a scavenger hunt—finding clues across systems that weren’t designed to agree.
The most practical path forward is to improve mapping reliability. AI-driven mapping for CPS devices can help reconcile inconsistent identifiers by inferring likely product code matches from partial signals (model patterns, observed capabilities, configuration hints, and vendor catalogue similarity).
This does not remove governance needs. It reduces the “unknown unknowns” that cause vulnerability management to stall.
Improved mapping isn’t the only dimension; prioritization determines which vulnerabilities matter first. Asset-to-process dependency prioritization links assets to operational workflows and downstream dependencies.
For example, a vulnerability on a low-impact or isolated PLC may be triaged later, while an issue on a PLC that directly controls a safety-critical process requires accelerated action.
Analogy: treat your vulnerability backlog like an emergency room triage—authentication tells you who arrived faster; dependency mapping tells you who needs a doctor first.
A reliable dependency model also reduces the risk of “wrong patching.” If mapping is uncertain, dependency prioritization can choose safer next steps (containment, compensating controls, or staged validation).
AI-driven mapping can raise the share of devices that receive correct vulnerability identification by:
1. improving product code identification reliability,
2. filling missing OS/firmware context where signals exist,
3. generating confidence scores for applicability decisions.
The key is to treat this mapping output as decision support with verification steps—not blind automation.

Insight: Passwordless can be risky when governance is weak

Passwordless itself doesn’t create a vulnerability. Weak governance does. Specifically, when governance fails to ensure continuous alignment between identity/access systems and the actual devices they act upon, “secure auth” becomes a means of operating on uncertain targets.
Think of governance as the conductor’s score. Passwordless is the orchestra’s tempo. If the score is wrong or outdated, the music may sound confident—but it won’t match the performance space.
In critical infrastructure, security governance must enforce security governance for critical infrastructure in a way that proves intent translates into effective access over time.
This includes:
– change control for device identities,
– validation that authentication policies map to real asset context,
– auditing evidence that privileged access targets the correct systems.
Your environment likely has a “control plane” made of firewalls, segmentation, identity policies, and enforcement layers. The control plane is supposed to translate business intent into access decisions.
If passwordless expands access paths or automates device identities without validating the underlying device mapping, you can create a mismatch between intent vs effective access. The system may allow the “right” authentication method to succeed, but it may still authorize sessions against the “wrong” device context in vulnerability management terms (and potentially in remediation workflows too).
Many organizations manage security policies via incremental change requests. Over time:
– rules accumulate,
– ownership becomes unclear,
– documentation becomes stale,
– continuous evidence of what’s actually effective degrades.
Passwordless adoption can intensify this because it reduces friction for access and can make policy drift harder to notice—security teams see fewer password-related events, but vulnerability targeting still relies on the inventory data that drifted long ago.
– Passwordless MFA (with strong inventory): faster authentication + reliable mapping → alerts trigger confirmed, prioritized remediation.
– Passwordless + weak inventory: faster authentication + uncertain mapping → alerts trigger scramble, manual guesses, and delayed patching.
Authentication speed and targeting accuracy are different metrics. Passwordless improves the first. Inventory quality determines the second.
If PLC product code identification is wrong (or OS/firmware context is missing), automated patch suggestions and vulnerability remediations can be misapplied. Even if the team “tries,” they may spend time patching the wrong versions—or postpone patching due to uncertainty.
That delay is often where the operational risk grows.

Forecast: Higher exploitability as OT device identification improves

As mapping improves—via AI-driven approaches and better data pipelines—attackers will also benefit. That sounds alarming, but it’s realistic: when device identification becomes more accurate, both defenders and adversaries can target vulnerabilities more precisely.
The security challenge becomes governance + verification + dependency-aware response, not just mapping accuracy.
When attackers gain reliable device context, exploitability increases because they can narrow target sets. In OT/ICS, this is particularly sensitive because many environments are segmented poorly for rapid remediation and downtime.
Paradoxically, incomplete mapping can sometimes frustrate attackers—but it also frustrates defenders. If defenders can’t confirm affected devices, attackers may still attempt opportunistic exploitation against exposed device families.
Better mapping reduces uncertainty. That can increase pressure as attackers learn which exact devices match a vulnerability.
In OT settings, exploitation attempts can cause not just compromise but operational disruption—such as remote code execution outcomes or denial-of-service behaviors. If the exact device context is uncertain, defenders might not anticipate worst-case impacts fast enough.
So the goal is not merely to improve mapping. The goal is to improve mapping within a robust governance and safety framework.
The future-facing approach is a pipeline that treats asset data quality as dynamic and decision-critical:
– normalize identity signals,
– reconcile vendor records against your catalogue,
– continually verify that identity remains accurate after upgrades/replacements.
A future-ready pipeline shifts inventory focus from “what devices exist” to “what each device does and impacts.” That aligns with asset-to-process dependency prioritization and supports faster, safer triage during vulnerability events.

Call to Action: Make inventory data quality a board-level risk

If you want passwordless to be safer in practice, treat inventory quality as a strategic risk—not background hygiene. In cyber-physical systems, cyber-physical asset inventory data quality for vulnerability management directly influences operational continuity and breach impact.
Leadership should understand that authentication improvements won’t compensate for wrong targeting in vulnerability response.
Start with a sprint that links assets to processes and dependencies:
1. Identify your most critical processes.
2. Map controlling assets (especially PLC product code identification targets).
3. Prioritize vulnerability remediation based on dependencies, not just exposure frequency.
Stop relying on partial or unreliable identifiers. Improve collection and reconciliation so PLC product code identification is correct enough to support vendor matching—especially for the devices that control safety or continuity-critical operations.
Don’t assume advisories are always perfectly actionable. Validate:
– whether your device versions match the advisory’s affected ranges,
– whether your mapping confidence scores align with observed behavior,
– whether compensating controls or patch paths are viable in your environment.
Governance should enforce ongoing proof that the environment is behaving as intended. This includes continuous evidence of effective access and change-driven verification.
Adopt processes that:
– capture inventory changes during maintenance windows,
– verify device identity after upgrades,
– re-check that authentication policies still align with correct device targeting.
This is how you prevent drift from turning passwordless into a faster path to misaligned decisions.
1. Identify missing fields (OS, product code, model).
2. Apply AI-driven mapping for CPS devices to infer likely catalogue matches.
3. Reconcile vendor records against your catalogue to reduce mismatch.
4. Prioritize by dependencies and asset criticality using asset-to-process dependency prioritization.
5. Measure accuracy, time-to-match, and remediation follow-through to prove improvement.

Conclusion: Passwordless needs trustworthy context to be safe

Passwordless authentication can improve security by reducing credential risk. But in cyber-physical environments, the real danger is that faster authentication can mask a slower, more error-prone vulnerability management pipeline.
Key takeaway: authentication strength can’t compensate for data-quality gaps. If PLC product code identification fails, OS/firmware context is missing, and CVE-to-device mapping is uncertain, then passwordless may lead to the worst combination: confident security signals with uncertain remediation outcomes.
Make inventory data quality—and governance of that quality—part of the risk posture. Then your passwordless program won’t just feel modern. It will actually be safe.