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Introduction: The Vulnerability Flood Has Become a Security Problem
Artificial intelligence is changing cybersecurity at breathtaking speed. Security researchers can now use AI to analyze code, identify suspicious behavior, discover weaknesses, and accelerate vulnerability research in ways that would have seemed impossible only a few years ago.
But there is an uncomfortable question hiding behind that progress: what happens when we become dramatically better at discovering vulnerabilities than we are at understanding and fixing them?
That is the warning at the heart of this analysis by Gene Moody, Field CTO at Action1. The problem is no longer simply that organizations have too many vulnerabilities. The deeper problem is that the infrastructure used to describe, enrich, prioritize, and distribute vulnerability information is itself under enormous pressure.
The National Vulnerability Database (NVD), maintained by NIST, has historically served as one of the most important sources of standardized vulnerability intelligence. Yet the explosive growth in CVEs has created a scale problem. According to the original article, NIST has responded by changing how it processes and enriches vulnerability records, including moving roughly 30,000 vulnerabilities published before March 1, 2026, into a “Not Scheduled” status.
That decision may be understandable from an operational perspective. No organization can process an infinite stream of data with finite resources.
But for defenders, it creates a troubling new reality.
A vulnerability can exist publicly, have a vendor advisory, potentially have exploitation information available elsewhere, and still lack the complete standardized context that security teams depend on to determine whether they are exposed.
And attackers do not wait for the database to catch up.
The Real Problem Is No Longer Just the Number of CVEs
Cybersecurity teams have been dealing with vulnerability overload for years. Every month brings new CVEs, security advisories, patches, exploit discussions, proof-of-concept code, and threat intelligence reports.
But the scale is becoming increasingly difficult to manage.
The original Action1 report cited in the article found that disclosed vulnerabilities across the enterprise software categories it analyzed increased 92% in 2025 compared with 2024.
Even more concerning, critical and high-severity vulnerabilities reportedly increased by 103% each, while vulnerabilities enabling remote code execution increased by 128%.
These numbers describe more than an inconvenient increase in paperwork.
They represent a fundamental change in the operating environment for security teams.
When vulnerability discovery accelerates, every downstream process must accelerate as well: validation, enrichment, asset matching, prioritization, patch testing, deployment, verification, and continuous monitoring.
If only discovery becomes faster, the result is not necessarily better security.
It can instead create a larger and more dangerous backlog.
AI Is Accelerating Discovery — But Remediation Is Still Human-Speed
AI introduces an especially important twist into this problem.
Modern AI systems can assist researchers with source-code analysis, vulnerability discovery, exploit development, malware analysis, and security investigations.
Recent security research has already demonstrated how AI-assisted systems can identify sophisticated weaknesses and automate parts of offensive security research.
That means the traditional assumption that vulnerability discovery is relatively slow may no longer be safe.
Imagine a pipeline where an AI system identifies hundreds of potential weaknesses in software repositories, researchers validate them, vendors publish advisories, and CVEs are created.
Now imagine the defensive infrastructure receiving all of that information.
The database has to understand each vulnerability.
Security teams have to determine whether they are affected.
Asset inventories have to be matched against vulnerable versions.
Risk has to be calculated.
Patches have to be tested.
And finally, remediation has to happen.
The weakest link determines how quickly the entire security process moves.
NVD’s Scaling Challenge Is a Symptom of a Larger Industry Problem
The NVD is not the only system experiencing this pressure.
The entire vulnerability-management ecosystem was designed around a world where security teams could reasonably expect centralized vulnerability databases to provide a relatively complete picture.
That assumption is becoming harder to maintain.
The modern security environment is distributed across thousands of vendors, cloud services, open-source projects, package repositories, endpoint platforms, security researchers, threat intelligence providers, and government agencies.
Information can appear in one place before it appears somewhere else.
A vendor may publish a security advisory before a CVE receives complete enrichment.
A researcher may disclose exploitation details independently.
CISA may later add a vulnerability to its Known Exploited Vulnerabilities catalog.
A security vendor may identify active exploitation before the vulnerability has accumulated all of its standardized metadata.
The result is a fragmented information environment.
What Not Scheduled Really Means for Defenders
The phrase Not Scheduled may sound harmless.
Operationally, however, it raises an important question: when will the missing information become available, and what happens to defenders until then?
If a vulnerability lacks complete enrichment, security teams may have difficulty determining exactly which products and configurations are affected.
They may have to rely on vendor advisories.
They may need to search security-research publications.
They may have to inspect release notes.
They may need to correlate package versions manually.
And in mature security organizations, they may need to compare everything against their own asset inventory.
That creates additional work at precisely the moment when security teams are already overwhelmed.
Attackers
This is perhaps the most important point in the entire discussion.
Attackers do not need a vulnerability database to tell them that a vulnerability is dangerous.
They can collect information from vendor advisories, GitHub repositories, exploit-development communities, security researchers, malware campaigns, leaked technical documentation, internet-facing systems, and their own research.
If a vendor publishes a patch, an attacker can potentially reverse-engineer the difference between vulnerable and fixed versions.
If exploitation details become public, they can analyze them directly.
If researchers demonstrate a proof of concept, attackers can study it.
In other words, attackers can construct their own vulnerability intelligence pipeline.
Defenders need to do the same.
Enrichment Is Not Cosmetic Metadata
It is easy to think of vulnerability enrichment as administrative information attached to a CVE.
It is much more important than that.
Security teams need structured information to answer practical questions.
Is this vulnerability present in our environment?
Which operating systems are affected?
Which software versions are vulnerable?
Does the vulnerability require a specific configuration?
Is exploitation possible remotely?
Is public exploit code available?
Is exploitation occurring in the wild?
Has ransomware been associated with it?
Has CISA listed it as actively exploited?
Is a patch available?
Which machines need to be remediated first?
Without reliable answers, a CVE number alone does not provide enough operational intelligence.
The False-Positive Problem Can Become Just as Dangerous
Incomplete vulnerability data creates another challenge: false positives.
Suppose a vulnerability is broadly associated with a software product but the affected version range is unclear.
A security scanner could flag thousands of machines.
The security team then investigates.
Some systems may not actually be vulnerable.
Others may be protected by configuration.
Still others may already have compensating controls.
Every unnecessary alert consumes analyst time.
Eventually, this produces alert fatigue.
And alert fatigue is not merely an efficiency problem.
When teams are overwhelmed by low-value findings, genuinely dangerous vulnerabilities can disappear inside the noise.
False Negatives Are Even More Dangerous
The opposite problem is potentially worse.
If vulnerability information is incomplete, an organization might incorrectly determine that a system is safe.
That is a false negative.
A false positive wastes time.
A false negative can leave an attacker an open door.
This is why vulnerability management cannot simply be about collecting more CVEs.
It has to be about accurately connecting vulnerabilities to real assets and real risk.
The Backlog Problem Could Become Permanent
Another concern raised by the article is the possibility of a rolling backlog.
A temporary backlog is normal.
A permanent backlog is different.
If new vulnerabilities continuously arrive faster than older vulnerabilities are enriched, the system may never completely catch up.
The result is an expanding gap between what exists and what has been fully processed.
That creates uncertainty.
Security teams may not know whether a particular vulnerability has been fully evaluated, partially enriched, or effectively waiting in a queue.
The uncertainty itself becomes operational risk.
Vulnerability Management Is Becoming a Correlation Problem
The old model was relatively simple:
Find CVE → look it up in NVD → determine severity → patch it.
The modern model increasingly looks like this:
CVE + vendor advisory + asset inventory + software version + exploit intelligence + CISA KEV + threat intelligence + business criticality + exposure + remediation status.
That is much more complicated.
But it is also much closer to the way real-world risk actually works.
A CVSS score cannot tell an organization everything it needs to know.
A vulnerability rated Critical may be irrelevant if the affected software does not exist in the environment.
A Medium vulnerability could become extremely dangerous if it affects an internet-facing business-critical application and active exploitation is underway.
Context changes everything.
The CISA KEV Catalog Has Become Increasingly Important
One particularly valuable source of context is the Cybersecurity and Infrastructure Security Agency Known Exploited Vulnerabilities catalog.
The KEV catalog focuses specifically on vulnerabilities known to have been exploited in the wild.
That distinction matters.
A theoretical vulnerability and an actively exploited vulnerability should not necessarily receive identical treatment.
If attackers are already exploiting a vulnerability, organizations need to know immediately.
This is one reason vulnerability-management programs increasingly need to correlate NVD data with exploitation intelligence rather than relying on a single severity number.
Why CVSS Alone Is Not Enough
CVSS remains useful.
But CVSS primarily describes characteristics of a vulnerability.
It does not automatically describe how important that vulnerability is to a specific organization.
Consider two vulnerabilities with identical CVSS scores.
One affects an obsolete application installed on three isolated laboratory systems.
The other affects a remote-access service exposed to the public internet and used throughout the company.
Their practical risk is obviously different.
Effective vulnerability prioritization therefore requires environmental context.
Asset Intelligence Is the Missing Half of the Equation
Vulnerability intelligence tells you what could be vulnerable.
Asset intelligence tells you what actually exists.
Neither is sufficient by itself.
An organization may have excellent vulnerability feeds but a terrible software inventory.
Another may have excellent endpoint visibility but weak threat intelligence.
The strongest programs connect the two.
When a vulnerability is published, the system should be able to determine which assets are affected, how important those assets are, whether exploitation is occurring, whether a patch exists, and whether remediation has already happened.
That turns vulnerability management from a database exercise into an operational security process.
The New Security Question: “Does This Affect Us?”
The most important shift is therefore conceptual.
Security teams should stop asking only:
How severe is this CVE?
They should also ask:
Does this vulnerability affect our environment?
Then:
Is exploitation occurring?
Then:
How exposed is the affected system?
And finally:
How quickly can we reduce that exposure?
Those questions lead directly to actionable security decisions.
Why Organizations Need Multiple Intelligence Sources
The article argues that organizations should increasingly correlate information from multiple sources.
That does not mean security teams should subscribe to every possible feed.
More information can actually make the problem worse.
The objective should be high-quality correlation, not maximum data collection.
Useful sources can include:
NVD
CISA KEV
Vendor security advisories
Microsoft security intelligence
Independent vulnerability intelligence
Threat intelligence platforms
Security research
Endpoint inventories
Software asset management systems
Internal incident data
The key is connecting them.
Deep Analysis: Building a Faster Vulnerability Intelligence Pipeline
Start by Pulling Vulnerability Intelligence
Organizations can begin by retrieving structured vulnerability information rather than relying exclusively on manual web searches.
For example, teams working with public vulnerability APIs can use commands such as:
curl -s "https://services.nvd.nist.gov/rest/json/cves/2.0?keywordSearch=openssl" \n| jq '.vulnerabilities[] | .cve.id'
This provides a basic starting point for collecting CVE identifiers.
Search for Specific CVEs
Once a CVE becomes relevant, security teams can retrieve additional information.
curl -s \n"https://services.nvd.nist.gov/rest/json/cves/2.0?cveId=CVE-2026-XXXX" \n| jq '.vulnerabilities[0].cve'
The exact CVE should then be correlated with vendor information and internal asset data.
Query the Local Software Inventory
Linux environments can begin with package inventories:
dpkg-query -W -f='${Package} ${Version}
' > installed-packages.txt
On RPM-based systems:
rpm -qa > installed-packages.txt
For a specific package:
rpm -q openssl
These commands alone do not establish vulnerability, but they provide the asset-side information needed for correlation.
Identify Internet-Exposed Services
A basic local listening-port check can help identify potentially exposed services:
ss -tulpn
For organizations with centralized endpoint management, this information should ideally be collected automatically rather than manually.
Compare Software Versions Programmatically
A simple workflow might look like:
CVE discovered
↓
Vendor advisory collected
↓
Affected versions identified
↓
Internal software inventory queried
↓
Affected endpoints identified
↓
Exploit intelligence evaluated
↓
Business criticality evaluated
↓
Priority assigned
↓
Patch/remediation deployed
↓
Exposure verified
The important point is that vulnerability discovery should not be the end of the workflow.
It should trigger the workflow.
Automate the Evidence Collection
Security teams can also automate evidence collection around critical vulnerabilities.
For example:
grep -Ei 'openssl|apache|nginx|kernel' installed-packages.txt
Or, when endpoint-management software is available, query the central inventory rather than depending on individual machines.
Automation reduces the time between intelligence acquisition and an actual remediation decision.
Don’t Automatically Patch Everything
Automation should not mean blindly installing every available update immediately.
Production systems may have compatibility requirements.
Critical applications may require testing.
Some vulnerabilities may be irrelevant to the organization.
Others may require configuration changes rather than software replacement.
The objective is automated decision support followed by controlled remediation, not uncontrolled patching.
Verify Remediation After the Patch
One of the most overlooked steps is verification.
A successful patch command does not necessarily mean the organization is safe.
After remediation, teams should verify:
dpkg-query -W -f='${Package} ${Version}
' | grep openssl
or:
rpm -q openssl
Then the resulting version should be compared against the vendor’s fixed version.
The vulnerability-management system should ultimately report the endpoint as remediated only after the change has been verified.
Vulnerability Management Must Become Continuous
The traditional patch cycle often looks like this:
Discover → assess → schedule → patch → repeat.
The modern threat environment increasingly demands:
Discover → correlate → prioritize → remediate → verify → monitor → repeat continuously.
That is a major philosophical change.
Security teams cannot afford to treat vulnerability management as a monthly administrative exercise when exploitation can begin within hours.
What Undercode Say:
The Vulnerability Database Was Never the Whole Security Program
The NVD remains extremely valuable, but it was never designed to replace an organization’s entire vulnerability-management process.
It provides standardized intelligence.
It does not know which laptop belongs to the finance department.
It does not know which server is exposed to the internet.
It does not know which application is mission-critical.
And it cannot independently determine how much risk a vulnerability represents inside a specific business.
The Real Danger Is the Gap Between Discovery and Action
The cybersecurity industry has become exceptionally good at discovering problems.
The next challenge is reducing the distance between discovery and remediation.
If AI continues accelerating vulnerability research, this gap could become dramatically larger.
The industry therefore needs to think of vulnerability management as an end-to-end latency problem.
How many minutes pass between disclosure and detection?
How many hours pass before affected assets are identified?
How long before the vulnerability reaches the top of the remediation queue?
How long before the patch is deployed?
How long before remediation is verified?
Those are increasingly meaningful security metrics.
Severity Is Losing Its Position as the Primary Metric
CVSS remains important, but it should not be treated as the final answer.
Real-world risk is multidimensional.
Exploitation status matters.
Internet exposure matters.
Asset importance matters.
Attack complexity matters.
Existing controls matter.
Whether ransomware groups are actively using a vulnerability matters.
Whether remediation is available matters.
The strongest vulnerability programs combine these factors rather than relying on a single number.
AI Could Make the Problem Better or Worse
AI is not automatically the solution.
If AI generates more vulnerability findings without improving prioritization and remediation, organizations could simply receive more alerts faster.
That would be an expensive version of the same old problem.
AI becomes truly valuable when it helps transform raw findings into decisions.
Which vulnerability matters?
Why does it matter?
Which machines are affected?
What evidence supports the conclusion?
What should be fixed first?
What can safely be automated?
That is where AI could produce a genuine defensive advantage.
Attackers Already Think in Correlated Intelligence
Modern attackers rarely operate from a single source.
They combine public research, stolen credentials, exposed infrastructure, vulnerability disclosures, malware telemetry, cloud information, and their own reconnaissance.
Defenders increasingly need comparable information fusion.
The difference is that defenders must use that intelligence responsibly and at enormous organizational scale.
The Future Belongs to Real-Time Vulnerability Management
A vulnerability database updated periodically is useful.
A vulnerability-management system that continuously understands assets, software versions, exploit activity, exposure, and remediation status is much more powerful.
That is the direction the industry appears to be moving toward.
The central unit of security is no longer simply the CVE.
It is the CVE-to-asset relationship.
The Backlog Should Become a Risk Signal
Instead of treating vulnerability backlog as a simple number, organizations should analyze it.
How old are unresolved vulnerabilities?
How many are actively exploited?
How many affect internet-facing systems?
How many have available patches?
How many remain unresolved because of business constraints?
How many are false positives?
Those measurements reveal much more about security posture than simply counting open CVEs.
Organizations Need Resilient Intelligence Pipelines
A mature vulnerability program should continue functioning even when one intelligence source is incomplete or delayed.
That means building redundancy.
If NVD enrichment is delayed, vendor intelligence should still be available.
If a vendor advisory is incomplete, threat intelligence may provide additional evidence.
If external information is uncertain, asset telemetry can determine whether the affected software actually exists.
Resilience should exist at the information layer as well as the infrastructure layer.
Automation Must Connect Directly to Remediation
Another major lesson is that finding a vulnerability is only half the job.
If analysts discover a critical flaw and then have to export a spreadsheet, email another team, open a ticket, wait for approval, schedule maintenance, and manually confirm the patch, the organization has created an enormous amount of avoidable latency.
The shorter the path from detection to remediation, the smaller the attacker’s window of opportunity.
Vulnerability Management Is Becoming an Engineering Discipline
Modern vulnerability management increasingly resembles data engineering.
Teams need ingestion pipelines.
They need normalization.
They need correlation.
They need asset identity resolution.
They need risk scoring.
They need automation.
They need feedback loops.
And they need continuous validation.
The security analyst is increasingly becoming a consumer of an intelligence pipeline rather than someone manually searching databases all day.
The Human Analyst Still Matters
Despite the rise of automation, humans remain essential.
Not every vulnerability can be reduced to a simple algorithm.
Business context matters.
Compensating controls matter.
Architecture matters.
Operational constraints matter.
Security professionals need to remain capable of challenging automated recommendations rather than blindly accepting them.
The best future is therefore not humans versus AI.
It is humans directing automated systems that can process vastly more information than humans can handle alone.
The Most Dangerous Vulnerability May Be the One Nobody Prioritized
A CVE does not become dangerous because it has a number.
It becomes dangerous when attackers can exploit it against something that matters.
That distinction should shape every modern vulnerability-management program.
The goal is not to eliminate every CVE simultaneously.
That is impossible.
The goal is to eliminate the most dangerous exposure first.
Enterprise Security Needs Speed Without Losing Accuracy
There is an obvious temptation to respond to the vulnerability explosion by automating everything.
That would be a mistake.
Speed without accuracy creates chaos.
Accuracy without speed creates exposure.
Modern vulnerability management needs both.
Organizations need systems capable of rapidly identifying likely exposure while preserving enough context for security professionals to make reliable decisions.
The NVD Debate Is Really About the Future of Security Intelligence
The controversy surrounding vulnerability enrichment is therefore bigger than a database.
It represents a broader transition.
Cybersecurity is moving from a world of centralized, relatively static intelligence toward a world of distributed, continuously changing information.
No single database can realistically know everything.
No single vendor can see every attack.
No single scanner can understand every business environment.
Security teams need systems that can synthesize all of those signals.
The Biggest Advantage Will Be Shorter Time-to-Remediation
The ultimate metric should be simple.
How quickly can an organization reduce real exposure?
Not how many CVEs did it collect.
Not how many dashboards did it build.
Not how many vulnerability reports did it generate.
The meaningful result is how quickly dangerous exposure disappears.
That is where vulnerability management becomes actual risk reduction.
The AI Vulnerability Era Has Arrived
AI will almost certainly continue accelerating vulnerability discovery.
Researchers will find flaws faster.
Attackers will analyze patches faster.
Exploit development may become faster.
Security teams will receive more information than ever before.
That makes vulnerability-management architecture strategically important.
The organizations that succeed will not necessarily be those that discover vulnerabilities first.
They will be the organizations that can understand, prioritize, remediate, and verify them before attackers can turn them into access.
✅ Vulnerability Volume Is Increasing Dramatically
The
The specific Action1 percentages — including the reported 92% increase in enterprise-software vulnerabilities — should be understood as findings from Action1’s own 2026 report rather than universal measurements for every software category.
The underlying trend is nevertheless clear: defenders are dealing with substantially more vulnerability information than previous generations of security programs were designed to process.
✅ NVD Has Faced Significant Scaling Pressure
NIST has publicly acknowledged changes to NVD operations as the volume and complexity of vulnerability data have increased.
The article correctly identifies this as a structural scaling challenge rather than evidence that vulnerability databases have become useless.
The important distinction is that NVD remains valuable while organizations may increasingly need complementary sources.
✅ Attackers Do Not Depend on NVD Enrichment
This is fundamentally correct.
Attackers can use vendor advisories, patch diffs, security research, exploit repositories, malware telemetry, scanning, and independent analysis.
Therefore, delays in standardized enrichment do not necessarily create equivalent delays for attackers.
❌ A Backlog Does Not Automatically Mean Vulnerabilities Are Being Ignored
A vulnerability categorized as “Not Scheduled” should not automatically be interpreted as abandoned forever.
Prioritization systems exist because organizations cannot process every record simultaneously.
The real concern is whether delayed enrichment creates unacceptable uncertainty for defenders and whether alternative intelligence sources can compensate.
❌ CVSS Alone Cannot Reliably Determine Organizational Risk
A high CVSS score does not automatically mean a vulnerability is the most urgent issue in every environment.
Risk depends heavily on exposure, affected assets, exploit availability, business criticality, compensating controls, and active exploitation.
That makes contextual prioritization essential.
Prediction
(+1) Vulnerability Management Will Become More Automated, Correlated, and Real-Time
The next phase of vulnerability management is likely to move away from the idea of a single authoritative vulnerability list.
Security platforms will increasingly combine CVE databases, vendor advisories, exploit intelligence, endpoint inventories, cloud assets, threat intelligence, and business context.
AI will likely become an important layer in that process, helping security teams determine which findings deserve immediate attention.
The most valuable systems will not simply tell organizations what vulnerabilities exist.
They will tell them which vulnerabilities matter right now, which systems are exposed, why they matter, and what action should happen next.
The result could be a much more responsive defensive ecosystem — but only if remediation technology advances as quickly as vulnerability discovery.
The central race in cybersecurity is therefore changing.
It is no longer simply AI versus attackers.
It is increasingly discovery speed versus remediation speed.
And in that race, the organizations that close the gap fastest may be the ones that survive the next generation of vulnerability-driven attacks.
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