Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security teams face a flood of noisy logs that arrive in many formats, and often without the context teams need to detect and prioritize threats. But even normalized and contextually enriched log data is only as useful as the speed at which teams can act on it. Any step that requires manual effort can become a bottleneck that makes it difficult to keep pace with the volume.

Build-time allowlists help teams preserve readable Real User Monitoring (RUM) action names while reducing the risk of exposing runtime-generated sensitive data. RUM action names turn user interactions into descriptions that engineers can use to understand application behavior, but that readability can also create risk. Datadog provides a build-time privacy approach that preserves readable action names when their contents are known to be static while masking text constructed at runtime.

AI agents comprise models, instructions, data, and tools, so thoroughly investigating potential security risks requires evidence from several components. As Datadog teams build AI agents for internal workflows, we use Datadog AI Guard to monitor how they handle each component during a session. We’ve found that application logs may capture an agent’s final API call without showing which prompt, retrieved content, or tool result led to the action.

Security and engineering teams contend with a constant stream of signals about vulnerabilities, incidents, misconfigurations, identity risks, control gaps, and other findings across their environments. But an individual finding’s severity does not always reflect its potential organizational impact.

AI coding tools help developers build and deploy LLM applications quickly, but this speed comes with new security risks. Traditional static application security testing (SAST) tools that are pattern based weren’t designed to detect LLM-specific issues such as prompt injection sinks and exposed system prompts. These vulnerabilities often don’t become apparent until applications are already running in production, when remediation is more difficult and expensive.

AI-accelerated attacks are redefining the threat landscape, but many of them still rely on one of the oldest tactics in the book: exploiting known vulnerabilities. The difference today is speed. Vulnerabilities that once took skilled hackers months or weeks to exploit can now be weaponized in hours or minutes. This acceleration is forcing organizations to rethink how they identify and remediate risk.

Many observability platforms authenticate to Microsoft Azure by using client secrets. Teams must create, store, and periodically rotate these secrets to keep receiving the telemetry data that they need. This recurring maintenance adds operational overhead and increases the risk of ingestion outages that occur when secrets expire.

On June 1, 2026, the Kubernetes Security Response Committee updated the records for four older CVEs that remain unfixed. The corrections may cause vulnerability scanners to report these CVEs in clusters where they weren’t previously detected. But an affected version doesn’t necessarily mean that a cluster is exposed. Each unfixed Kubernetes CVE depends on a particular combination of permissions, cluster features, and network access.

Software that runs inside customer-managed infrastructure creates a particular challenge at the FedRAMP High baseline. It still has to meet the applicable security and compliance requirements, even though the vendor does not control the surrounding operating system, libraries, network configuration, or maintenance practices. For Datadog, that challenge centers on the Datadog Agent, which runs directly on customer-managed hosts to collect logs, metrics, traces, and security signals.

Google Security Operations (SecOps) is Google Cloud’s security operations platform for detecting, investigating, and responding to threats across large volumes of security telemetry. To make that telemetry useful across sources, Google SecOps uses the Unified Data Model (UDM), a common event schema that provides a consistent structure for security logs. But logs from firewalls, endpoints, identity providers, and other sources all describe and format security events differently.