In the early days of cloud security, like in the early days of endpoint, the focus was on prevention. This makes sense: preventative measures are an essential way to reduce risk. Blocking known threats and attack paths makes sense as a way to harden an organization’s cloud estate. For many organizations, a prevention-only strategy in the cloud might seem completely sufficient for reducing risk – and it is to an extent. But prevention alone can only go so far.

Attack surfaces in the cloud are expanding at a breakneck pace. Cloud security has reached an unprecedented level of complexity — ranging from misconfigurations and vulnerabilities to advanced threats and compliance challenges, all while malicious actors are increasingly using generative AI to target your cloud infrastructure.

For security leaders, staying vigilant and prepared is like wielding a well-crafted spellbook. OWASP, MITRE ATT&CK, and threat research are the critical chapters in this spellbook that leaders need to leverage to anticipate and counter emerging threats effectively, because you can’t afford for your organization to be ensnared by threats that could have been foreseen.

Containers are a key building block for cloud workloads, offering flexibility, scalability, and speed for deploying applications. But as organizations adopt more and more containers, they encounter a new set of security challenges. Developer, DevOps, platform, and security teams often find themselves struggling to keep up with vulnerabilities, misconfigurations, and threats. This is where runtime insights come in, offering key visibility and intelligence to help detect real risk and cut through noise.

Multi-step reasoning is a concept that is taught in grade school math class, but it applies far beyond mathematical calculations and word problems. It is the process of solving a problem requiring multiple individual calculations or steps in order to reach the final answer. Multi-step reasoning requires sequencing, logic, and sometimes prior knowledge or inference.

Adversaries often use complex, multi-stage cloud attacks that evade traditional security measures, which struggle to fully visualize, prioritize, and respond to threats. Multi-domain correlation addresses this by analyzing data across diverse domains — including networks, applications, databases, and storage — to uncover potential weaknesses and attack paths across interconnected resources.

The cybersecurity landscape is sadly brimming with tools that address narrow, specific problems, leading to a phenomenon known as “Point Solutions.” While these tools can offer precise capabilities, they have significant drawbacks in the modern, cloud-native world. A glut of isolated tools contributes to operational complexity, wasted resources, and missed opportunities for cohesive, unified defense strategies.

To address Sysdig’s 5/5/5 Benchmark, rapid troubleshooting and deep forensic investigation are crucial when a security breach or performance issue arises. While Falco excels at real-time threat detection based on system call activity, Sysdig serves as the go-to tool for post-incident analysis.

In the late 1990s, the rapid expansion of computer networks highlighted the need for affordable network visibility tools. The Berkeley Packet Filter (BPF) emerged as a significant advancement, enabling packet capture and filtering within the BSD operating system. BPF is the precursor of today’s widely used eBPF, and was originally released together with an accompanying library, libpcap.

One of the big advantages of running your workloads on a managed Kubernetes service like Google Kubernetes Engine (GKE) is that Google ensures your clusters are being deployed and managed following industry best practices. While GKE clusters are incredibly secure and reliable, there is always room for improvement. In this blog, we’re going to describe how you can enhance GKE’s already great security by adding runtime threat detection with Falco.