As agentic AI systems evolve through protocols like MCP and A2A, traditional security practices must be adapted to address new risks such as goal misalignment and tool instruction abuse. This article explores practical threat modeling strategies, including goal alignment cascades and distinguishing between parameter-only vs. instruction-enabled tool calls.

SAS Blog

23.05.2025

Lag selection in dynamic demand models using PROC TSSELECTLAG in SAS Viya - The SAS Data Science Blog

This article explains how to set up and analyze a dataset for forecasting in SAS Viya with a particular emphasis on selecting lags for dynamic models.

SAS Blog

23.05.2025

Detecting lags in nonlinear time series with PROC TSSELECTLAG in SAS Viya - The SAS Data Science Blog

Accurately identifying lag structures between related time series is essential in public health forecasting, particularly during epidemics where delays between infections and hospitalizations affect planning. Using a simulated SEIR model and SAS Viya’s PROC TSSELECTLAG, distance correlation is shown to outperform Pearson correlation by correctly identifying nonlinear lag relationships—such as the true seven-day lag between new infections and hospital admissions.

SAS Blog

31.05.2025

Automatic Linting of SAS Code with SASjs - The SAS Data Science Blog

Demonstrate how to use the SASjs library in conjunction with GitHub actions to ensure consistent SAS code quality throughout a repository

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Threat modeling for agentic systems - The SAS Data Science Blog

Lag selection in dynamic demand models using PROC TSSELECTLAG in SAS Viya - The SAS Data Science Blog

Detecting lags in nonlinear time series with PROC TSSELECTLAG in SAS Viya - The SAS Data Science Blog

Automatic Linting of SAS Code with SASjs - The SAS Data Science Blog

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