You may want to seriously think of alternatives to Bonferroni if you have many correlations. This is because the Bonferroni method will result in very low power to detect nonzero correlations if you have many correlations. This approach actually sets the bound for the type I error rate, and is based on k independent tests. But the correlations are likely (highly) correlated with each other.
With a collection of k p-values (including from t tests of correlations), there are multiplicity adjustments that have a much higher power. These are not automatically done, but these are easily done using a data step or using PROC MULT with output from the CORR procedure. I suggest you read chapter 2 in the book Westfall, Tobias, Rom, Wolfinger, and Hochberg. 1999. Multiple Comparisons and Multiple Tests. (SAS Publishing). This shows how to do the analysis. You first need to decide on the adjustment and then follow the directions.