FINAL WORD 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 shouldn ’ t or when intruders invoke commands with parameters outside normal usage .
Lori MacVittie , Distinguished Engineer at F5
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
‘ nearly 40 % of organisations have suffered a major outage caused by human error over the past three years .’ This is where AI and Machine Learning ( ML ) come in .
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 .
The use of Machine Learning to protect IT automation
Machine Learning is adept at uncovering patterns and relationships between data points . Today , most industries utilise Machine Learning to solve
security and 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
Infrastructure for apps , app delivery and automation are still attractive attack vectors . As organisations 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
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