I think at the core you might have understood the purpose of Outlier detection.
You essentially balance two things: detect all outliers/ anamolies, have as little as possible detection of outliers/anamolies that are "just" random noise.
Depending on for what you need it it might be that you want to detect all outliers (if there impact is very negative for example) -- even at the cost of false alarms.
But maybe "small" outliers are fine for you and you want only to detect the really big deviations. This is what you can use your standard deviations for.
In the end you need to optimize the "cost" of the expected false positives and false negatives for your specific scenario. A classical example for this is Fraud detection: You can avoid all Fraud by rejecting all Customers or you can avoid any proper Customer being rejected by allowing all Fraudsters - for most businesses the truth lies somewhere in the middle -- so they essentially tune the parameters of their outlier detection by comparing the costs of added additional Fraudsters with the lost Revenue of additional rejected proper Customers.