Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

When it comes to disaster recovery and backup plans, understanding the RPO and RTO is crucial as these two critical metrics help signal the level of your overall data security. In short, Recovery Time Objective (RTO) and Recovery Point Objective (RPO) play a critical role in determining how quickly and effectively an organization can bounce back from an IT disaster, safeguarding business continuity, and minimizing potential data and financial losses.

DevOps teams did not sign up to be security teams. But if you run repos, CI/CD, cloud roles, SaaS apps, integrations, or backups, you operate the systems attackers lean on. Most breaches are not flashy. They start with routine failures: a token left in a repo, MFA not enforced, an overprivileged API key that never expires, or backups that are deletable by the same admin identity. Attackers do not need to “break in” if they can log in.

To reduce the risk of data loss, organizations rely on proven backup strategies. In this guide, we explain what the 3-2-1 backup rule is, how it works, and how to apply it correctly. Topics discussed in this piece: click to expand What is the 3-2-1 backup rule? Three data copies Two different media One off-site copy Is the 3-2-1 backup rule still the industry best practice? Ever heard of the 3-2-1-1-0 backup rule? Alternative 4-3-2 strategy How do these compare against each other?

DevOps has become an important practice for companies looking to efficiently meet customers’ expectations, accelerate software delivery, and optimize resources. Companies that embrace the DevOps culture can smoothly merge their development and operations teams, resulting in faster releases and better development.

Choosing different IT services is like choosing a car. Apparently, all cars have four wheels, an engine, and they can be driven. But in the end, differences are much bigger, and the final choice depends on many factors. Of course, we always take into account the price, but not only. First of all, the choice depends on the purpose and usability. After all, we will not take a sports car to drive on muddy forest roads. Personal preferences and habits also matter.

Cyber crime gets more advanced, and threat actors increasingly target backups with ransomware attacks. If an organization is left with erased or corrupted data and has no access to backup copies, there is no chance of recovery. This allows the attackers to demand ransom payments averaging at over 10 million dollars as of 2025. That is why your backup and disaster recovery strategy should be reliable and secure to keep your environments protected and guarantee business continuity.

Every IT stack may look tidy on a diagram. If so, then it’s tempting to assume everything works fine. And yet, systems rarely fail as a whole. Usually, it’s a part or functionality. For instance, anyone who ever untangled a broken workflow in GitHub, GitLab, Bitbucket or Azure DevOps, or a corrupted field in Jira, knows it too well. And that’s the quiet tension (“to fix one little thing”) inside every modern backup strategy.

Modern organizations often use GitLab as a core version control system (VCS), making it one of the most essential systems for DevOps. Given the critical nature of the data stored here, thorough evaluation of risks and implementing data protection best practices are a must. According to the Shared Responsibility Model, GitLab provides security for the underlying infrastructure, while the user’s duty is to keep data protected. More about GitLab’s Shared Responsibility Model.

Artificial Intelligence is everywhere nowadays. It helps teams to be more productive, but at the same time, it can threaten your critical project management data. The introduction of AI into Jira opened up new paths for attackers to exploit, new vulnerabilities coming up internally, and human errors. So, in this article, let’s speak about AI data loss in Jira and what measures to take to protect your sensitive data in Jira Cloud.

Artificial Intelligence is everywhere nowadays. It helps teams to be more productive, but at the same time, it can threaten your critical project management data. The introduction of AI into Jira opened up new paths for attackers to exploit, new vulnerabilities coming up internally, and human errors. So, in this article, let’s speak about AI data loss in Jira and what measures to take to protect your sensitive data in Jira Cloud.