Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Summary Organizations often assume their Microsoft 365 assets are fully protected by native backup tools. However, while Microsoft does offer advanced built-in backup and recovery features, relying solely on a native approach is not a substitute for an independent, enterprise-grade backup strategy. The risks of relying on a single ecosystem for backups are escalating.

Regulation is doing more to shape backup strategy right now than almost anything else. NIS2 requires organizations to document their risk management measures, keep an incident response plan on file, and report breaches within 24 hours, with fines that can reach €10 million or 2% of global turnover. DORA goes further for financial entities, requiring documented recovery objectives, regular resilience testing, and an audit trail that holds up to a regulator’s questions.

GHE.com introduces migration, identity, and integration changes.– Some GitHub data may still be processed outside the chosen region.– ta residency still needs a separate backup and recovery strategy. For many enterprises, GitHub is now one of the most important parts of the DevOps ecosystem, where teams manage repositories, pull requests, issues, workflows, GitHub Actions, security alerts, project data, and other metadata that keeps software delivery moving.

Shadow AI is the use of generative AI tools by employees without the knowledge, approval or oversight of an organization's IT or security teams.

Your source code sits on GitHub, GitLab, Bitbucket, or Azure DevOps, along with your pipeline configurations, issue history, permissions, and metadata. Most teams assume it’s safe there because it works. But is it? Financial institutions, technology companies, and software houses all build the systems they run on. Code stopped being a byproduct of the business and became the business. And as it turns out, it needs protection of its own.

To write an AI usage policy, define its scope, list approved and prohibited tools, name the data types banned from prompts, write clear rules, align to recognized frameworks and set enforcement. A written policy is the decision layer; pairing it with technical controls is what turns those decisions into protection.

Information technology and operational technology are increasingly connected, but they are not interchangeable. IT is built around information and business services; OT is built around physical processes, equipment and safe operation. That difference shapes their security priorities, asset lifecycles, maintenance practices, network architecture and recovery requirements.

AI productivity tools are creating a prompt-level data leakage problem: 77% of employees paste data into generative AI tools, and 82% of that activity comes from unmanaged accounts, according to the LayerX Enterprise AI and SaaS Data Security Report 2025. Every paste into ChatGPT, Copilot or another GenAI tool is a potential exposure of sensitive data that traditional file-focused controls were never built to see.

Kubernetes trusts everything inside the cluster by default. Any pod can reach any other pod, IPs change with every rolling deployment, and one compromised container gives an attacker a path across your entire environment. Kubernetes zero trust replaces that default with a simple rule: Never trust, always verify, regardless of where a request comes from.