Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Software composition analysis (SCA) tools have become essential in modern security programs. They continuously scan software supply chains and match component fingerprints against Common Vulnerabilities and Exposures (CVE) databases to surface vulnerabilities in dependencies. SCA tools are effective at scale, but they introduce a persistent challenge: Not every flagged vulnerability actually presents a risk.

Security operations teams manage a high volume of signals, often across multiple tools. Analysts may triage detections in one system, document progress in another, and coordinate remediation elsewhere. As context becomes fragmented, response times slow and the risk of missed threats increases.

In the latest episode of This Month in Datadog, Jeremy shares how to run autonomous Cloud SIEM investigations, remediate vulnerabilities with auto-generated fixes, and use natural language to explore Datadog. Later, Sumedha Mehta spotlights the Datadog MCP Server, which gives AI agents real-time access to Datadog’s observability data. Then, Chetan Sharma walks through Datadog Experiments, which measures how product changes impact the user journey.

Cloud environments create a flood of security signals, often reaching tens of thousands per day depending on the organization’s size. Security engineers and analysts spend a disproportionate share of their time triaging these signals instead of acting on legitimate threats. But the time-intensive parts of that work, such as identifying related signals and building a timeline, can be handled systematically, leaving teams free to focus on what actually requires human judgment.

As AI adoption accelerates on Google Cloud, the challenge for most teams today is no longer just building AI-powered applications. It’s also managing the full AI stack from end to end, including data pipelines, infrastructure, release process, and security operations. Many teams are monitoring these layers with different tools, creating complexity, fragmenting visibility, and slowing decisions on what to do next.

In March 2026, a GitHub account called hackerbot-claw, describing itself as an “autonomous security research agent powered by claude-opus-4-5,” began systematically targeting open source repositories—including one from Datadog. Over a week, it opened many pull requests designed to exploit misconfigurations in GitHub Actions workflows.

Python AWS Lambda functions are ephemeral and highly distributed, which creates security visibility gaps that traditional perimeter defenses and proxy-based controls struggle to fill. Techniques such as credential stuffing, SQL injection, and server-side request forgery (SSRF) can look like legitimate application traffic, making them difficult to identify without visibility inside the application itself.

Static application security testing (SAST) tools help developers quickly catch potential vulnerabilities as they code. However, these tools rely on inflexible rules that often generate a high number of false positives, reducing trust in their accuracy and slowing adoption. To help developers access context-aware vulnerability detection, we’ve released an open source AI-native SAST solution. This tool scans code changes incrementally and surfaces security issues in real time.

Source code management (SCM) and CI/CD pipelines have become the industry standard for automating software delivery. But from the time a code change enters your SCM until it’s deployed, it’s susceptible to changes and reconfigurations that can go so far as to modify the pipeline itself. If you’re not proactively securing your CI/CD system, attackers can use it to grant themselves permissions, access secrets, and ship malicious code.

In Part 1 of this series, we discussed the CI/CD security boundary, mapped out potential attack vectors with a CI/CD threat matrix, and introduced a simple threat model focused on ideating detection workflows. In this post, we’ll apply these principles to a real-world source code management (SCM) tool example that every developer is familiar with: GitHub. In addition to threat modeling, we’ll also be taking a closer look at historical attacks on GitHub and GitHub Actions ecosystems.