Understanding Is Predictability: A Better Model for Vulnerability Management

Summary

The question is no longer, “Do you know all the vulnerabilities in your product?”

It is, “If one appeared tomorrow in this part of the product, do you already know how it would be rated, and can you justify why?”

Understanding is Predictability.

The question is no longer, “Do you know every vulnerability in your product?”
It is, “If one appeared tomorrow in this part of the system, do you already know how it would be rated—and can you defend why?”

If the answer is no, vulnerability management is still reactive. And without understandability, predictability is impossible.

Understanding Is Predictability

To understand something means that if this happens here, that will happen there.
Understanding is predictability.

In cybersecurity, this does not mean predicting which vulnerability will appear next. That expectation is unrealistic. We can safely assume vulnerabilities will exist. What matters is whether their severity would come as a surprise.

If a product’s security is truly understood, its behavior should not be surprising—whether the system is operating as intended or failing in unexpected ways.

What “Understanding Security” Actually Means

When manufacturers say they “understand the security” of their product, what does that really mean?

A well-understood system has boundaries. It has known attack surfaces. It has clear relationships between components, interfaces, data flows, and clinical or essential functions. In such a system, severity outcomes are constrained by structure.

If a vulnerability appears in a low-impact component with no access to safety-relevant assets, a HIGH or CRITICAL rating should be structurally impossible. If a vulnerability appears in a safety-critical pathway, elevated severity should be expected.

If severity outcomes feel random, the system is not yet understood.

The Gap in Today’s Vulnerability Management

The industry is very good at explaining vulnerabilities after they are discovered. What is far less common is the ability to show that a vulnerability’s rating could have been predicted before it existed.

This is the real signal-to-noise problem.

Knowing where HIGH or CRITICAL ratings are structurally possible allows manufacturers to focus engineering effort, monitoring, and regulatory attention where it actually matters—rather than reacting equally to every finding.

This is not about speculative threat scenarios or abstract risk discussions. It is about CVSS ratings.

A vulnerability rated below HIGH is unlikely to represent uncontrolled patient safety risk and can almost always be addressed through planned remediation. The real challenge is knowing, in advance, where HIGH or CRITICAL ratings are even possible.

Predicting Severity Without Predicting the Vulnerability

No organization can predict tomorrow’s vulnerability. But organizations can predict how a system would be rated if a vulnerability were to appear in a given location.

In practice, the specific weakness often matters less than its context. Whether the issue is missing authentication, weak encryption, hardcoded credentials, or injection is secondary.

Severity is driven by:

• Where the vulnerability exists
• What it can access
• What data flows through it
• What safety, clinical, or essential functions depend on it

If those attributes are understood, the resulting CVSS severity should not be a surprise.

Why Ratings Are Still Hard to Defend

All the ingredients already exist:

• A standardized scoring system in CVSS
• FDA guidance through the FDA–MITRE CVSS rubric
• Threat models that identify attack surfaces
• Architecture documentation describing components, interfaces, and data flows

Yet vulnerability ratings remain inconsistent and difficult to defend.

The issue is not frameworks. It is execution.

Applying these inputs consistently, at scale, and without bias is hard. Ratings are often shaped by partial system knowledge, individual experience, or competing incentives. Third-party researchers may inflate scores. Manufacturers may minimize them. Both positions are understandable—but neither produces defensible outcomes.

When auditors ask why a vulnerability was rated a certain way, “this is what we thought made sense” is no longer sufficient.

Removing Opinion Through Understandability

The solution is not better judgment. It is less judgment.

Predictability comes from understandability, and understandability comes from decomposing a product into objective elements:

• Components
• Interfaces
• Data flows
• Assets
• Software and hardware materials
• Trust boundaries
• Attack surfaces

These are observable facts, not interpretations.

When a product is modeled this way, it reflects how the system actually exists—not how it is remembered during a meeting. From that level of understanding, predictability follows.

Why the Rating Matters More Than the Finding

Vulnerability ratings drive everything downstream: patch urgency, regulatory response, field actions, risk acceptance, and engineering prioritization.

If manufacturers can predict severity based on system context alone, they can:

• Apply controls where they matter most
• Reduce noise in postmarket monitoring
• Make better premarket design decisions
• Defend those decisions during audits

In many cases, severity outcomes can be evaluated before vulnerabilities exist by applying known scoring rules to known system attributes.

Where ELTON Fits

This level of predictability is not achievable manually.

ELTON operationalizes the shift from reactive scoring to predictive severity. By combining deep product understanding with standardized vulnerability rating logic and FDA-recognized CVSS decision criteria, ELTON enables manufacturers to determine severity based on system context—not opinion.

The outcome is not perfect certainty. It is defensible consistency. Not opinion-proof, but opinion-resistant—which is exactly what regulators expect.

The Real Question

The question is no longer, “Do you know all the vulnerabilities in your product?”
It is, “If one appeared tomorrow in this part of the system, do you already know how it would be rated—and can you justify why?”

If the answer is no, vulnerability management is still reactive.

And without understandability, predictability is impossible.