Historically, a closed network approach was used to minimise enterprise cybersecurity risk. Due to the heavy dependence of modern enterprises on technology, constant interaction with the internet, and the ever-growing array of productivity and task-specific software becoming available, that approach is no longer feasible. Allowing end-users the freedom to define their own workflow is crucial for productivity in today's competitive environment. However, working environments such as this makes measurement of exposure to cyber risk challenging.
Increasingly, enterprises are looking for ways to grant their users autonomy while maintaining visibility of their inherent level of risk and a high level of protection against nefarious activity. At the same time, growing awareness and concern around cyber crime has resulted in greater pressure being placed on enterprises and individuals to understand, measure, and monitor their cyber risks.
Given the size of the challenge, enterprises need a detailed and multipronged approach to include:
- Behavioural and activity analytics to accurately and efficiently detect notable deviations from normal patterns of behaviour
- Artificial intelligence for anomaly detection
- Machine learning to detect previously unseen threats and threat types hidden in the noise