Datadog announced expanded security and observability support for AWS serverless applications built on AWS Lambda and Step Functions services.
The functionality helps AWS Lambda and Step Functions users detect security threats, get a high-level overview of how their state machine is performing at a single point in time and monitor services instrumented with OpenTelemetry.
Serverless applications eliminate the need to provision and manage infrastructure components, including servers, databases, queues and containers, so teams can focus on writing code while minimizing their operational overhead. These applications also come with unique challenges as they need to be monitored and secured in different ways than traditional applications. Datadog already provides out-of-the-box observability for AWS serverless applications and today expanded these capabilities to include:
- Support for W3C Trace Context Propagation Across All Lambda Runtimes: Teams can now view complete distributed traces across upstream and downstream services that have been instrumented by various OpenTelemetry-compatible instrumentation libraries. This provides teams with improved visibility into their serverless applications so they can efficiently troubleshoot any issues.
- AWS Lambda OpenTelemetry API Compatibility Custom Instrumentation: Developers can now use vendor-neutral code instrumentation to submit custom OpenTelemetry spans from Lambda applications to Datadog in Node.JS and Python runtimes, allowing them to adhere to open source standards.
- Threat Detection for Serverless Applications Deployed on AWS Lambda Functions: The new support enables both DevOps and security engineers to detect and protect against attacks targeting their applications running on AWS Lambda functions.
- Open Source Vulnerability Detection for AWS Lambda Functions: Available in public beta, this capability provides engineers with real-time, continuous vulnerability detection in third-party libraries that are being run in their AWS Lambda applications.
- AWS Step Function Execution Visualization on State Machine Maps: To make troubleshooting issues within AWS Step Functions easier, developers can see the exact path of a Step Function execution, drill into anomalous executions and identify problematic states with ease.
"Datadog helps us detect attacks against our serverless applications and triggers an automated response to block those attempts as they happen," said Micha Katz, CISO at Yellow Card. "Application Security Management was simple to enable and further configure to meet our needs. It provides an informative, well-organized UI, where we can drill into attack details, trigger additional actions using predefined workflows, and gain important insights that help us calibrate and optimize our detection rules. Additionally, with vulnerability detection and contextualized severity ratings, we can better prioritize our remediation efforts within our service layers."
"Securing serverless applications can be a unique challenge because they are highly distributed and comprise several ephemeral, stateless components," said Vikram Varakantam, Senior Director of Product Management at Datadog. "By providing full visibility into serverless applications on AWS Lambda, Datadog Application Security Management's threat detection capability helps DevOps and security teams understand and prioritize the risks and attacks associated with their serverless applications so that they can work to resolve and mitigate any potential breach."
The new features are available now.
The Latest
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...
According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...
2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...
Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...