Cyber Resilience

Cyber security is a growing IT priority.

Threat analysis, detection, and protection are an ongoing part of IT and Information Security Officers’ work. However, although implementing the best tools for doing so is necessary, you must also prepare for the worst-case scenario – i.e., that your data becomes corrupted. Because no matter how much you invest in protection, there will probably always be breaches of some kind, as well as people who are just waiting to abuse them. That’s why every organization should have a data recovery plan in place, in case of an attack.
Cyber resiliency plays a significant role in a company’s recovery plan. Unfortunately, most legacy backup and recovery tools are not strong enough contenders. At Datagon Systems, we are experts in mapping the organization’s databases, assisting the IT teams in identifying possible relevant threats, and predefining an optimal recovery process. Because businesses cannot function without their data.
In today’s modern world, cyber and ransomware attacks are prevalent and serious threats. Recovering from such incidents is not trivial. If you’re lucky, you will be able to recover the data without paying the ransom after days of hard work, while the company endures significant downtime. But data recovery is a very complex process. First, you have to identify when the attack began. Unlike data loss or corruption due to human error or bugs, with cyber attacks it is very difficult to pinpoint the date and time this crisis began. And if you recover the incorrect data time, you may cause even greater harm rather that rectifying the situation. Don’t waste time running antivirus software – cyber attacks tend to corrupt data copy rather than infect them with a virus.

Cyber attacks often turn into real horror shows, with corrupted databases, destroyed backup servers, and even corrupted backup images. However, with our predefined and pre-implemented recovery plan, our customers benefit from an optimal recovery process comprised of the ability to examine multiple points-in-time for the same dataset, test data integrity, identify the best restoring point, and provide the production applications with clean datasets.