All Disaster-Recovery-as-a-Service (DRaaS) providers do the same basic thing: They manage the recovery of data and applications following an outage or cyberattack. The growing frequency of cyber-attacks highlights the importance of these services. Cloudflare, for example, mitigated $6.9 million DDoS attacks in Q4 2024, a 16% increase quarter-over-quarter and an 83% rise year-over-year.
While cybersecurity risk and exposure may be unavoidable in our present threat environment, proactive measures can significantly reduce the likelihood and impact of cyber attacks. Understanding these risks and implementing strong security controls ensures safer digital environments for individuals and organizations alike.
AI is transforming productivity across every industry—from marketing and design to legal and engineering. But while employees rush to embrace tools like ChatGPT, Gemini, and Microsoft Copilot, many are using other tools without oversight from IT or security. As this grassroots usage grows, so does the volume—and sensitivity—of data flowing into AI tools.
Data Loss Prevention (DLP) tools are crucial for protecting sensitive information as it moves within and outside an organization. They help prevent data leaks and unauthorized access by allowing organizations to monitor, control, and respond to potential data transfer risks. In this article, let’s learn more about how DLP tools play a vital role in secure data transfers.
During an Advanced Continual Threat Hunt (ACTH) investigation conducted in early March 2025, Trustwave SpiderLabs identified a notable resurgence in malicious campaigns exploiting deceptive CAPTCHA verifications. These campaigns trick users into executing NodeJS-based backdoors, subsequently deploying sophisticated NodeJS Remote Access Trojans (RATs) similar to traditional PE structured legacy RATs.
This week on the podcast, we bring in Adam Winston, former CSO of ActZero and current Field CTO for Managed Services at WatchGuard to discuss automating the SOC with AI. We cover the history of AI in SecOps, the good and bad applications of AI and Machine Learning, what the future looks like, and how compliance might impact our ability to get there.
AI Took Off. We’re Launching the Controls. Discover how Cyberhaven is rewriting the rules of AI data security. Our newest innovation is too big to call a feature — it’s a new frontier. AI changed everything... fast. Productivity soared, but so did risk. Employees embraced AI, and data raced across tools without oversight. The question isn’t if your organization is using AI — it’s how much risk it’s exposing in the process.
This tutorial provides a complete guide to deploying Astra Traffic Collector inside a Linux-based VM across cloud platforms like AWS, GCP, Azure, or DigitalOcean. It includes all necessary installation steps and configuration best practices for traffic monitoring in VM environments. In this video, we cover: This guide is designed for DevOps, security engineers, or platform teams looking to integrate deep traffic visibility into their environments with minimal overhead.
This tutorial provides a complete walkthrough for setting up AWS VPC Traffic Mirroring to enable packet-level monitoring with Astra’s API Security platform. You'll learn how to configure a scalable and production-ready environment using Network Load Balancers, Nitro-based EC2 instances, and properly filtered traffic sessions. The guide includes: It also covers key assumptions, unsupported configurations (like classic/Xen instances), and best practices for a secure and efficient deployment.