In today’s complex cloud environments, security and engineering teams need to manage vulnerabilities and misconfigurations across multiple layers of the stack, including cloud resources, clusters, containers, and applications. Often, this results in a lengthy list of problems that lacks prioritization and is daunting for users to address.
We recently released the State of Cloud Security study, where we analyzed the security posture of thousands of organizations using AWS, Azure, and Google Cloud. In particular, we found that: In this post, we provide key recommendations based on these findings, and we explain how you can leverage Datadog Cloud Security Management (CSM) to improve your security posture.
The modern application landscape is rapidly evolving, creating new tools, technologies, and processes that allow organizations to deploy production code faster. But risks to application security have also changed significantly, requiring the security discipline to evolve in order to adapt to new types of attacks.
For customers using Azure Key Vault—which helps them safeguard sensitive keys and secrets used by applications and services hosted on Azure—it can be challenging to determine when the resources in their Key Vault(s) are about to expire. Invalid keys and secrets can disrupt your day-to-day workflows by causing application downtime, holding up incident investigations, invalidating compliance, slowing down the development of new features, and more.
As the internet continues to evolve, cybersecurity threats—particularly Distributed Denial of Service (DDoS) attacks—are an increasingly significant concern for organizations. In this post, we’ll look at how you can use Datadog to collect Google Cloud Armor (GCA) logs and detect and respond to potential DDoS attacks in real-time. But first, we’ll briefly cover what DDoS attacks are and how they work.
Modern cloud applications are made up of thousands of distributed services and resources that support an equally large volume of concurrent requests. This level of scale makes it more challenging for engineers to identify system failures before they lead to costly outages. System failures are often difficult to predict in cloud environments, and security threats add another layer of complexity.
In Part 1 of this series, we talked about some challenges with building sufficient coverage for detecting security threats. We also discussed how telemetry sources like logs are invaluable for detecting potential threats to your environment because they provide crucial details about who is accessing service resources, why they are accessing them, and whether any changes have been made.
In the ever-changing world of cybersecurity, Security Operations Centers (SOCs) are responsible for building comprehensive threat detection strategies for their environments. A key indicator of success for any SOC team is their level of security coverage, which correlates with the breadth, depth, and accuracy of their threat detection tools and workflows.
Datadog Cloud SIEM helps customers protect their cloud environment and SaaS applications against threats with built-in threat detection rules, interactive dashboards, workflow blueprints, and in-depth support resources. These capabilities provide valuable insights into your security posture, so you can respond promptly to emerging threats. In order to generate these insights, Cloud SIEM analyzes log data, which users can start sending to Datadog by enabling one of our out-of-the-box integrations.
Cloud infrastructures can comprise thousands of interconnected and dynamic resources. This complexity introduces unique challenges to monitoring and securing these architectures. Understanding where user activity originates—and what actions constitute security threats—is a complex task when you’re dealing with the huge volume of logs, metrics, and other telemetry that highly distributed cloud environments generate each day.
