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With thousands of penetration tests conducted and incidents and vulnerabilities pursued, we've seen firsthand how traditional, one-off security assessments fall short in today's dynamic environment. That's why we're passionate advocates for continuous penetration testing—a proactive, ongoing approach that transforms cybersecurity from a reactive checklist item into a strategic asset.

In the ever-evolving world of cybersecurity, staying ahead of malicious campaigns is crucial for developers. Recently, security firm Koi released eye-opening research on a sophisticated malware operation they've named "PhantomRaven." This campaign exploits "Remote Dynamic Dependencies" (RDDs) in NPM packages to hide and deploy malware invisibly.

Just look at the numbers – IBM's latest report pegs the average data breach cost at a whopping $4.44 million worldwide, with U.S. companies getting slammed the hardest. And it's not just about the money; throw in downtime, lawsuits, and hefty fines, and you've got a recipe for disaster.

As software and artificial intelligence (AI) systems become foundational to national infrastructure, the security of their supply chains has emerged as a critical concern.

Traditional RMF is too slow for today's threats. NR Labs' modular, AI-driven approach enables continuous cyber resilience through automation, real-time monitoring, and seamless integration—proven in federal deployments.

While tools are critical, over-reliance on them without addressing underlying issues is like putting a bandage on a broken bone. In this post, we’ll share insights from our collective experience implementing robust security strategies for cloud and AI systems, emphasizing the need to fix root causes over leaning solely on tools.