Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Managing dynamic cloud environments is an ongoing challenge for organizations as they scale and innovate. Protecting assets, data, and reputations is more important than ever, yet detecting insider threats, compromised accounts, and unusual behavior in an environment remains complex. Traditional SIEM solutions often focus on reactive, event-driven insights, but to meet today’s evolving needs, many teams are embracing proactive approaches like user and entity behavior analytics (UEBA).

With the alarming number of data breaches and vulnerabilities today, security is now a primary concern for organizations and their customers, but knowing how to efficiently develop and scale secure applications is still a problem. Tackling this challenge requires considering the potential security risks of a new feature or service much earlier in the development cycle, an idea that is foundational to the Secure by Design approach.

The Domain Name System (DNS) translates domain names (e.g., datadoghq.com) into IP addresses via a process called DNS resolution. This translation facilitates all kinds of network communication, from enabling web browsers to connect to a desired page without requiring users to remember IP addresses, to internal communication across private infrastructure, such as Kubernetes environments.

AWS WAF is a managed web application firewall that monitors network traffic to your AWS applications and resources. As a perimeter-based firewall, AWS WAF is designed to secure the boundaries between your applications and the public internet. This means that it’s capable of protecting all of the various elements of your AWS architecture, including Amazon API Gateways, load balancers, and Amazon CloudFront distributions.

In Part 1 of this series, we looked at how AWS WAF helps you monitor network traffic to AWS resources, as well as key metrics and logs for detecting WAF misconfigurations and malicious activity. In this post, we’ll walk through using AWS’s native tooling to query that data.

In Part 2 of this series, we looked at Amazon’s built-in monitoring services for AWS WAF activity and audit logs. In this post, we’ll demonstrate how Datadog complements your WAF’s existing protection and extends its capabilities to not only offer protection at the perimeter but also to the APIs and services within your network.

We recently released the State of DevSecOps study, in which we analyzed tens of thousands of applications and cloud environments to assess adoption of best practices that are at the core of DevSecOps today. In particular, we found that: In this post, we provide key recommendations based on these findings, and we explain how you can leverage Datadog Application Security Management (ASM) and Cloud Security Management (CSM) to improve your security posture.

As application architectures shift to the cloud and the velocity of software delivery accelerates, organizations are seeking more powerful capabilities to identify security vulnerabilities within their production applications. Traditional static application security testing (SAST) tools, by themselves, are insufficient.

Security teams require complete visibility into their hosts, containers, and functions in order to detect, prioritize, and remediate their most pressing security risks. The Datadog Agent helps you achieve this visibility by collecting deep insights in your environment through logs, distributed traces, infrastructure metrics, and other key telemetry.

Software Composition Analysis (SCA) is the practice of identifying the open source libraries your code depends on. By using SCA, you can analyze these dependencies and determine whether they are affected by any known vulnerabilities, contain malicious code, introduce licensing risk, or are poorly maintained. SCA helps teams understand their software’s dependencies and the security implications of using them so that they can safely build on and innovate with open source code.