t cht lk operational challenges like identifying humans and bots , recognising attacks , and predicting imminent outages .
t cht lk operational challenges like identifying humans and bots , recognising attacks , and predicting imminent outages .
An unexplored area is app infrastructure protection ( AIP ). For example , F5 Distributed Cloud AIP uses Machine Learning to understand how operators and admins interact with critical systems and immediately notices when an interaction deviates from the norm .
This is useful for detecting attackers attempting to access directories they should not or when intruders invoke commands with parameters outside normal usage . move to adopt more automation , they need to simultaneously consider the accidental or intentional ramifications of its use . From here , it is necessary to consider how to protect it against the inevitable fat finger or malicious keystroke .
Automation is a force multiplier ; it is useful for both intended and malicious use cases which highlights a need to protect it . Machine Learning may be one way to integrate AI with ops to protect the infrastructure that remains a vital component of any digital business . p
Detecting anomalous parameters or attempting to execute an unusual command means this technology could easily be applied to IT automation to catch human errors or malicious commands .
Final takeaway
Assuming the right level of access to target systems , such a Machine Learning solution could certainly offer a path to protecting systems against bad parameters , lateral communication attempts and other attacks .
Infrastructure for apps , app delivery and automation are still attractive attack vectors . As organisations
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