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.
Security and DevOps engineers often spend a lot of time and effort creating and managing complex, repetitive workflows, such as incident response, honeypotting, recovery and remediation, and more. Blink is a no-code security platform that enables users to create workflow automations, triggers, and self-service apps to streamline processes, better enforce guardrails, and eliminate operational bottlenecks.
In complex cloud environments where the speed of development is accelerated, managing infrastructure and resource configurations can be an overwhelming task—particularly when certifications and compliance frameworks like PCI, HIPAA, and SOC 2 present a lengthy list of requirements. DevOps and engineering teams need to ship code updates at a rapid pace, making it easy for them to accidentally overlook misconfigurations.
Ensuring your threat detection rules work as intended and provide sufficient coverage for major threats is a critical component of a security program. Red Canary’s Atomic Red Team—an open source library of detection tests that help teams validate the effectiveness of their security measures—has historically been the tool of choice for detection testing.
