Managing sensitive information in your telemetry data poses many challenges to governance, risk management, and compliance (GRC) teams and overall security. Organizations in healthcare, finance, insurance, and other fields must carefully adhere to strict compliance requirements. But sensitive data comes in many forms and moves between many endpoints, and as a result, it can easily become exposed in telemetry data.

Identity and access management (IAM) systems are necessary for authenticating and authorizing access to your environment. However, their mismanagement is one of the leading causes of breaches and insider threats today. Engineering teams must rapidly provision identities and permissions to keep pace with infrastructure growth—consequently, the ratio of non-human or machine identities to every human identity is also increasing at a substantial rate.

Cloud environments comprise hundreds of thousands of individual components, from infrastructure-level containers and hosts to access-level user and cloud accounts. With this level of complexity, continuous and end-to-end visibility into your environment is vital for detecting, prioritizing, and fixing vulnerabilities before attackers can take advantage of them.

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.