How can I turn user audit data into behavior clusters?
Scaling

Standardize you data by $(x - mean(x)) / std(x)$

Most K-mean implementation by default uses Euclidean distance which assumes the equal importance of all features. This requires proper scaling to prevent one action dominates the others. 

Dimensionality

K-mean is not robust against the curse of dimensionality (see this post). So as always it good to reduce you dimensionality before feed it to the algorithm. 

Do some feature engineering first. For example, login and page load can be group together as a measure of passive engagement, page edit and page created could be considered as a single activate engagement feature.

Also, you can try using some standard dimensionality reduction algorithm like PCA