Each learning suggestion is assigned a percentage value which reflects the confidence the learning engine has for accepting the suggestion. This percentage value is called the Learning Score. For example, a score of 100 means that accepting the learning suggestion will not break the application or degrade the user experience due to a policy modification. For each request, ASM tracks various data points, including but not limited to the originating IP address, the time the HTTP session was opened, how many requests have been made, any violation ratings that have been assigned, and numerous proprietary rules of varying tolerance. The staging status of any entities or violation items is also considered for calculating the learning score. High-rated illegal requests will lower the score and slow down the acceptance of the respective suggestions induced by those requests, while speeding up and raising the score for suggestions induced by low-rated requests.