REPORT ACCURACY
Since its inaugural publication in 2014, the Annual Vulgarabilities Report has consistently demonstrated strong predictive accuracy, even as the global vulnerability landscape has expanded and diversified. Over the past decade, year-end analyses have shown that the report’s multi-year projections reliably track with actual CVE publication trends, typically falling within a 3–8% margin of error for one-year forecasts and a 6–12% margin of error for three-year forecasts. These results significantly outperform simple linear trend forecasting and underscore the value of domain-specific modeling.
Over time, refinements to the model have further improved predictive stability. Correlation studies have revealed several reliable relationships between vulnerability publication trends and external drivers, such as technology adoption cycles, imports, research community output, and historical domain volatility, which the model now incorporates to enhance precision. These correlation adjustments help the report maintain accuracy even during periods of atypical industry behavior.
Although no predictive model can fully anticipate outlier events (for example, the suppressed reporting patterns observed during the COVID-19 period), the Annual Vulgarabilities Report has maintained exceptional directional fidelity and consistently narrow error bands. Its long-term performance reinforces its role as a dependable forecasting tool for organizations seeking to plan staffing, tooling, and remediation capacity years in advance.
THE SILVER MODEL
We evaluated multiple forecasting models built on causal, lagged drivers of vulnerability growth. Candidate signals included market and purchase indicators (e.g. consumer technology confidence, enterprise IT investment), expansion indicators (e.g. cloud adoption, device proliferation, software ecosystem growth), and business growth indicators (e.g. venture activity, digital-transformation intensity). For each driver, we computed cross-correlation functions (CCF) against CVE time series to identify lead–lag relationships, tested statistical significance, and performed out-of-sample validation.
Composite findings. Growth indicators consistently lead CVE volume by ~12–18 months; broader economic activity shows a moderate 6–12 month lead. Improvement indicators (defensive and operational investments) exhibit negative correlations that modestly suppress vulnerability growth after ~1 year. We incorporate a Q4 seasonality adjustment to account for holiday-period dynamics (e.g. retail peak and year-end patch/disclosure cycles).
Model form
SILVER is a lagged, multivariate regression over a small set of proprietary lead indicators:
CVEt = α + ∑iβi⋅Li(t−ℓi) + γ⋅Seasonalityt + εt\text{CVE}_t \;=\; \alpha \;+\; \sum_{i} \beta_i \cdot L_i(t-\ell_i) \;+\; \gamma \cdot \text{Seasonality}_t \;+\; \varepsilon_tCVEt=α+i∑βi⋅Li(t−ℓi)+γ⋅Seasonalityt+εt
where LiL_iLi are selected leading signals with empirically determined lags ℓi\ell_iℓi (typically 6–18 months), and Seasonalityt\text{Seasonality}_tSeasonalityt encodes the Q4 effect. Parameterization, variable list, and weights are maintained privately to preserve forecast integrity.
Validation. The model demonstrates high explanatory power on historical data with stable lead–lag structure, low out-of-sample error, and materially improved Q4 fit after seasonality adjustment.