Engineering Africa

Redstor expands malware detection to M365 and Google Workspace


With South Africa having the third-highest number of cybercrime victims in the world, and the country being the worst effected on the continent when it comes to targeted ransomware attacks, the availability of the Redstor Malware Detection solution provides businesses across industry sectors with an effective way of strengthening their cybersecurity posture. It does so by automatically scanning corporate backup data and identifying potentially infected files.

Redstor Malware Detection uses machine learning to protect data from files containing malware. It quarantines infected files considered to be malicious and lets users either mark them as safe or have them revert to a previous, safe version. The solution can also be used for cloud-based platforms such as Microsoft 365, Google Workspace, OneDrive, Teams, Gmail, and so on. This provides technology decision-makers with the peace of mind that both their on-premises and software-as-a-service environments are safeguarded against malicious attacks.

“The solution provides South African businesses with an additional layer of protection, essential in today’s digitally-driven environment. The average time to detect a breach is 206 days meaning cybercriminals have ample time to move around inside the organisation’s network while also compromising the integrity of backups. The longer a compromise is left undetected, the worse the damage to enterprise systems will become,” says Justin Parker, Sales Specialist for Modern Workloads at Redstor.

For example, even if a business recovers from an attack, it can quickly be compromised again if its backups contain a malicious file that gets restored without the company being aware of it.

Once a company activates the malware detection add-on, Redstor will automatically scan backup data each time a backup completes, looking for indicators that malware is present within all files that have been added or changed within that backup.

Redstor Malware Detection can detect a variety of malicious payloads, including flooder attacks (malicious programmes that overload the network traffic), coin miners (malicious programmes that use system resources to generate cryptocurrencies), adware, trojans, backdoors, worms, and ransomware.

Users can see a breakdown of suspicious files categorised by severity. When clicking on a suspicious file, a certainty score is provided which indicates how certain the Redstor machine learning model is that the file is suspicious

“Even though anti-virus protects data, it offers no safety for backup files. Anti-malware focuses on new threats with its rules being updated faster than anti-virus solutions. The Redstor machine learning model is constantly evolving based on user behaviour and gets updated with new definitions weekly. Redstor Malware Detection is an ideal solution for companies looking to benefit from cloud-based platforms and do not want to risk their data being compromised,” says Parker.

Redstor Malware Detection.

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