EDITOR ’ S QUESTION
LOURENS SANDERS , SENIOR SALES ENGINEER ,
COMMVAULT
Data has become arguably the most important asset a business holds , which means it is a valuable commodity for those with criminal intent . Sensitive or personal information is often the target workload of cybercriminals because of the value this data holds . However , attackers are not simply out to steal the information , they are also trying to prevent businesses from being able to recover , and so they no longer target only production data , but the data protection infrastructure as well .
Data protection driven by AI and ML gives you the visibility into your data to be able to understand it and protect it better .
The way attacks are perpetrated has also changed and is constantly evolving , as malicious actors use ever more sophisticated means to breach networks . Attackers make use of AI and ML not only to create the breach , but also to avoid detection , and once they are inside , they can learn and see the usual behaviour patterns and then mimic them , making threats even harder to detect . There is also a growing trend toward insider threats , both intentional and accidental , which needs to be mitigated .
With attackers leveraging AI and ML to breach networks and wreak havoc on businesses , it has become critical that data protection solutions also use these technologies to detect breaches and mitigate risks . However , data protection is not just about blocking external threats . It is also imperative to ensure that access to data is built on a foundation of zero trust principles , so that the right people have access to the right data for the right purpose .
Data protection driven by AI and ML gives you the visibility into your data to be able to understand it and protect it better . It can also offer root cause analysis – for example , if data is moved or deleted , it can help businesses to identify if this action is legitimate or a threat . It can flag potentially sensitive data as well as suspicious activities , alerting and reporting this so that necessary action can be taken .
Essentially , AI and MLautomate multiple complex processes , improving the visibility and actionability of massive volumes of data , which cannot be achieved manually . It can also be used to identify redundant data and optimise storage while facilitating compliance .
Saying all of this , you still need backup and recovery to protect your data . Best practices like isolated copies of data , and immutable copies of data to virtual air gapped areas are still important , as are flexible recovery options to recover to a place or point in time . It is also essential to have an effective data governance strategy so that critical resources are identified and can be recovered in order of priority .
Implementing data protection with AI and ML on top of this aids in developing a proactive approach with automated data protection , predictive analytics , intelligent workflows and automation and greater insight .
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