In this part, we are going to introduce automation to effectively monitor our deployed detections. By setting up automations at this phase we adopt a proactive approach towards maintenance, allowing our team to take action before a blowout of alerts or an untuned detection is escalated by the SOC.
Key Findings Lunar Spider has expanded its initial access methods by compromising vulnerable websites, particularly in Europe, using Cross-Origin Resource Sharing (CORS) vulnerabilities. These websites are then injected with a FakeCaptcha framework. The FakeCaptcha framework is introduced via a JavaScript script that generates an iframe, overlaying the original site's content with the attacker's FakeCaptcha page. … Continue reading Lunar Spider Expands their Web via FakeCaptcha
This multipart blog series is focused on the real-world lessons learned while securing Microsoft Entra ID. Based on hands-on experience across various environments and organizations, we’ll explore the practical, high-impact strategies that work and more importantly, the common misconfigurations, overlooked settings, and pitfalls that can expose your identity perimeter. Throughout the series, we’ll cover both … Continue reading Securing Microsoft Entra ID: Lessons from the Field – Part 1
The deployment phase is one of the most challenging steps in the Detection Development Life Cycle due to its implementation complexity. In this part, we will explore the principles and practices of deploying rules to target platforms. Additionally, we will go through some of the challenges encountered when designing and implementing a deployment pipeline, along with suggestions on how to overcome them, to ensure that our Continuous Deployment pipeline operates smoothly.
Versioning in the detection library is crucial for maintaining traceability and tracking changes to individual detections and content packs. It enables us to pinpoint the exact state of specific detections at a given point in time, provides a clear history of updates, and facilitates troubleshooting and debugging by identifying which version introduced particular changes.
Sufficiently documenting our detections is essential in detection engineering as it provides context around the the purpose, detection logic, and expected behaviour of each detection rule. Just as important as documenting individual detections is tracking how the overall detection library evolves. In this part we are looking into how we can tackle both of those issues.
