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Performance

The Empirical Foundation model is designed to be trusted the same way you would trust any operational measurement system: it makes a clear, testable claim, it is evaluated against real outcomes, and it is monitored continuously as conditions change.

EMPIRICAL FOUNDATION MODEL BENCHMARKS

Jan 1, 2026 – May 31, 2026

9/10

Focusing on just the top 2% of the Empirical Foundation model, 9 out of every 10 prioritized vulnerabilities showed exploitation activity after scoring (covering 75% of all exploited vulnerabilities).

90%

Focusing on just the top 7% of the Empirical Foundation model, remediates 90% of observed exploited vulnerabilities (within 30 days after scoring).

19,000+

Our models are trained on over 19,000 known exploited CVEs.

Jan 1, 2026 – May 31, 2026

Empirical Foundation
Model Benchmarks

01 · EFFICIENCY VS COVERAGE

The Efficiency View

The efficiency plot compares the tradeoff between how efficiently resources are allocated against complete coverage of all observed exploited vulnerabilities. As the threshold for remediation is lowered, coverage improves, but at the cost of efficiency. Better models push up and to the right. Clearly, not all scoring systems are created equal.

Focusing on just the top 2% of the Empirical Foundation model, 9 out of every 10 prioritized vulnerabilities showed exploitation activity after scoring (covering 75% of all exploited vulnerabilities).

EFFICIENCY VS COVERAGE

0%20%40%60%80%100%0%20%40%60%80%100%COVERAGEEFFICIENCY0.970.960.90.80.70.60.50.40.30.20.10.0110987657.2Empirical FoundationCVSS

CVSS Top 2% limits to everything scored at 10, 1 out of every 10 prioritized vulnerabilities showed exploitation activity after scoring (covering 8% of all exploited vulnerabilities).

02 · COVERAGE VS EFFORT

The Capacity View

The Coverage vs Effort curve is the most intuitive chart for practitioners because it answers, “If we can only work on X% of the backlog, how much of the observed exploitation activity do we cover?” This chart will help you understand the tradeoffs inherent in any prioritization strategy.

Focusing on just the top 7% of the Empirical Foundation model, remediates 90% of observed exploited vulnerabilities (within 30 days after scoring).

COVERAGE VS EFFORT

0%20%40%60%80%100%0%5%10%15%20%25%30%35%40%45%50%EFFORT (% VULNERABILITIES PRIORITIZED)COVERAGE0.970.80.50.10.0110987.2Empirical FoundationCVSS

Because CVSS is coarse, you’re stuck between:

10% effort (CVSS 9.8 and above), 30% coverage of all exploited vulns (within 30 days of scoring)

2.3% effort (CVSS 9.9 and above), 9.5% coverage of all exploited vulns (within 30 days of scoring)

MODEL TERMINOLOGY

Effort, Efficiency, and Coverage

ALL PUBLISHED VULNS (CVES WE COULD PRIORITIZE) EFFORTEFFICIENCYCOVERAGE
Correctly Ignored Incorrectly
Prioritized
Incorrectly
Ignored
Correctly Prioritized FALSE POSITIVES FALSE NEGATIVES TRUE POSITIVES
NUMERATOR DENOMINATOR NOT COUNTED

METHODOLOGY

Our models are trained on over 19,000 known exploited CVEs

Our local models are trained directly on your organization's vulnerability and remediation data, learning the patterns, priorities, and risk tolerances unique to you. The result is data you can defend in a board meeting, one grounded in your own evidence rather than industry averages.

10x

It would take more than 10x people and resources to prioritize potential threats using CVSS than Empirical.

PUBLISHED CVESPRIORITIZED CVESEXPLOITED CVES

CVSS 9+ (Critical)

Threshold: 0.9
EFFORT14.4%
COVERAGE40.4%
EFFICIENCY9.5%

Empirical Radiant (Same Effort)

BETTER COVERAGE & EFFICIENCY

Threshold: 0.028
EFFORT14.4%
COVERAGE95.4%
EFFICIENCY22.4%

6x

You will be 6x more efficient at remediating actual threats using Empirical than using CVSS.

PUBLISHED CVESPRIORITIZED CVESEXPLOITED CVES

CVSS 7+ (High to Critical)

Threshold: 0.7
EFFORT50.5%
COVERAGE76.8%
EFFICIENCY5.1%

Empirical Radiant (Same Coverage)

LESS EFFORT, MORE EFFICIENCY

Threshold: 0.968
EFFORT2.6%
COVERAGE76.9%
EFFICIENCY99.3%

Local model results may vary depending on your security dataset.

See how your model would differ.

Try our models with your own local data and discover their impact on your cybersecurity environment.

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