A recent IDC report commissioned by Druva found that 76% of respondents experienced reinfection following the initial cyberattack. This is most likely caused by the malware being backed up and restored with the data and highlights the need for detection tools that can find malware in backup data and prevent its reintroduction.
It’s easy to believe one organization is among the millions of potential targets of cybercriminals and therefore safe through the anonymity of numbers. Unfortunately, that is not true — it’s not always “the other guy.” 46% of the respondents have been successfully attacked by ransomware in the past three years. Today, attacks are so common that 33% of respondents had both primary and secondary data impacted (secondary data being data copies and backup sets). Unfortunately, attackers are now aware that compromising the backup raises the odds that companies will be forced to pay the ransom to recover the data as the report indicated that 55% of respondents had 25–50% of their data impacted by the breach.
With the rise in attacks, most organisations are taking efforts to transform digitally and migrating to cloud. Enterprises are increasingly looking for cloud-native solutions aligned with the rest of the modern IT stack moving to the cloud across business intelligence, applications, databases, data warehouses and infrastructure.
According to the report, 50% of respondents indicated cloud is set to play a major role in backup/recovery strategies, data resiliency, cyber-recovery, and data archive. While organisations are modernizing their data resilience systems, their top 2 expectations are – fully automated, nondisruptive infrastructure updates and automation and recovery orchestration from ransomware, specifically automation to find the most recent recovery point. Currently, most organizations are forced to curate the recovery manually by searching backups, snapshots, and other copies for the latest clean version of an object. (By “object,” we mean a specific file in the file system, database, or table.) Indeed, this is often the longest, most difficult part of cyber-recovery. However, automated or orchestrated curation assists organizations in finding the most recent clean version of any data object even when some objects were clean yesterday and other objects corrupted months ago. Automated curation reduces the manual effort of determining recovery points and can significantly reduce recovery time as well as reduces the manual effort of determining recovery points and reduces recovery time.
As these organizations modernize their data resilience, greater emphasis will be on cloud-based applications. Indeed, from this research, the top 4 workload categories anticipated by respondents were cloud related: data centre applications migrated to the cloud, new cloud-native applications, containerized applications, and SaaS applications. This emphasis on cloud applications indicates a need to adopt a more cloud-centric data resilience platform.
