AI may help attackers automate and scale vulnerability-related work, increasing pressure on security teams—but current trend figures do not prove that AI alone caused a particular rise in exploit activity. The practical challenge is that a severity score or spreadsheet cannot prioritize work by itself: teams need current asset, exposure, exploitation, impact, and remediation information connected in a process that stays up to date.
How is AI changing vulnerability management?
AI can make some threat activity more efficient or easier to scale. In its August 26, 2026 vulnerability review, the Cybersecurity and Infrastructure Security Agency (CISA) says: “Emerging technology, such as AI, introduces efficiencies threat actors can leverage to automate and scale threat activity.” That is a reason to prepare for faster, more scalable activity—not proof that AI caused each newly disclosed flaw or exploit.
Recent figures reported by ITPro on October 1, 2026, attributing them to Google Threat Intelligence Group (GTIG), show increases across several measures. They indicate growing operational pressure, but the comparisons do not establish AI as the sole cause.
| Measure | Reported figure | How to read it |
|---|---|---|
| Monthly vulnerability disclosures | 10,740 in August 2026 | GTIG figure as reported by ITPro; a monthly disclosure count, not a count of confirmed exploitable flaws. |
| Exploited vulnerabilities | Average of 10.5 per month in 2025; 18 per month from January through August 2026 | GTIG figures as reported by ITPro. The periods differ in length, and the comparison does not identify a cause. |
| Zero-day exploitation | Average of 8 cases per month in 2025; 11 per month from January through August 2026 | GTIG figures as reported by ITPro; these period averages do not by themselves show that AI produced the increase. |
CISA’s August 2026 review describes a baseline before AI-enabled vulnerability discovery becomes more widespread; it is not a measurement of AI’s causal effect. Traditional problems still matter: CISA also highlights simple known vulnerabilities, poor patching, and continued use of end-of-support technology. AI does not make those basic sources of exposure disappear.
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Why a spreadsheet can fall behind
A spreadsheet is a record-keeping format, not a live vulnerability-management system. It can work as one part of a controlled process, especially for a small, stable environment. The risk is relying on a manually maintained sheet as if it automatically knows which software is deployed, whether an asset is reachable, whether exploitation evidence has changed, or whether a fix has actually been applied.
- Inventory gaps: a vulnerability record is useful only if the organization can identify affected products and versions in its own environment.
- Stale signals: advisories, known-exploitation status, and predictive estimates can change; a snapshot does not update itself.
- Missing context: severity alone does not show whether the affected system is exposed, business-critical, or protected by a compensating control.
- Unowned work: a list without an assigned owner, deadline, mitigation status, verification, and exception record can track findings without driving remediation.
- Scale and coordination: as assets and findings grow, manual matching and duplicate entry make it harder to see the most urgent work across teams.
The test is capability, not whether the organization uses spreadsheets. Any process needs trustworthy inventory, version matching, repeatable updates, and a way to assign and verify work. Automation can ingest and correlate signals; people still need to validate asset context, operational impact, and whether a compensating control is effective.
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Which vulnerability signals should drive priority?
No single score answers every question. CISA’s 2026 framework considers whether an asset is exposed, whether the vulnerability is in the Known Exploited Vulnerabilities (KEV) catalog, whether exploitation could be automated, and the technical impact. These factors help turn a finding into an organization-specific decision rather than a queue sorted only by severity.
| Signal | What it tells you | Important limit |
|---|---|---|
| CVSS or severity | Technical severity and potential seriousness of the vulnerability. | Does not by itself establish exposure, exploitation, or business impact in your environment. |
| CISA KEV | Evidence that a vulnerability is known to have been exploited; a strong operational signal for remediation. | The catalog has a defined scope. Absence from KEV is not evidence that exploitation has not occurred. |
| EPSS | A predictive probability signal for exploitation in the next 30 days. | It is not a record of past exploitation. NIST cautions that EPSS does not use past exploitation as a model input, so a previously exploited vulnerability could receive a low score. |
| LEV | NIST’s proposed estimate of the probability that a vulnerability has been observed exploited at some point in the past; it may also help assess KEV comprehensiveness. | It is a proposal, not ground truth. NIST reports an unknown margin of error and says public exploitation data is insufficient for thorough performance testing. |
| Exposure and technical/business impact | Whether the affected asset is reachable and how damaging compromise would be for this organization. | Requires accurate asset and operational context; it cannot be inferred from a vulnerability identifier alone. |
NIST’s May 2025 CSWP 41 distinguishes these questions: KEV captures known exploitation evidence, EPSS predicts exploitation within a future 30-day window, and LEV proposes an estimate of past observed exploitation. Treating them as interchangeable can mislead prioritization.
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The same paper gives a dated illustration of KEV’s scope: in December 2024, the catalog contained 1,228 vulnerabilities against roughly 260,000 CVEs, or 0.5%. That is a comparison of catalog coverage at that time, not a current KEV count and not evidence that only 0.5% of vulnerabilities are exploited.
What should a usable remediation process track?
For each finding, connect the vulnerability record to the affected asset and the work needed to reduce risk. A practical minimum view includes:
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- Asset identity, owner, software product, and deployed version.
- Whether the affected asset is exposed, plus its business criticality and technical impact.
- Current KEV status and, where used, EPSS or LEV with their distinct meanings preserved.
- Whether exploitation can be automated, where that information is available.
- A remediation owner and deadline, with patch, mitigation, and verification status.
- Any exception, its rationale, approver, and review date.
- When the data was last refreshed, and visible gaps or uncertain product/version matches.
Keep the underlying signals separate in the record. For example, “not in KEV,” “low EPSS,” “internet-exposed,” and “patch pending” describe different facts; collapsing them into one unexplained risk number hides why a finding was prioritized.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When is a spreadsheet enough—and when is automation warranted?
A spreadsheet may be adequate when the asset set is small and stable, product/version matching is reliable, updates happen on a defined cadence, and a named person checks that assignments and fixes are completed. It becomes harder to rely on as inventories, data sources, and ownership boundaries expand. Automation is useful when it can reduce manual imports and matching without obscuring uncertainty or replacing accountable remediation decisions.
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| Capability to compare | What adequate support looks like |
|---|---|
| Inventory and product/version coverage | Can it account for relevant assets and map findings to deployed products and versions? Are unmatched or missing assets visible? |
| Update frequency and imports | Can it ingest machine-readable data and show when advisories and exploitation signals were last refreshed? |
| Risk context | Can teams see exposure, KEV, EPSS or LEV where used, exploitation automation potential, and impact without confusing one signal for another? |
| Remediation workflow | Can the process assign owners, record deadlines, track patches and mitigations, verify fixes, and manage exceptions? |
| Integration | Can it connect to the organization’s asset and ticket systems so findings and ownership do not depend on repeated manual entry? |
| Data quality transparency | Does it expose coverage gaps, uncertain matches, stale inputs, and false positives for human review? |
Before replacing a sheet or adding tooling, walk a representative finding through the entire process: identify the affected version, confirm the asset and exposure, review exploitation signals, assign the work, record a mitigation or patch, and verify the result. If a step depends on an undocumented manual check, that is a process gap to address—regardless of the format used.
Quick Recap
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