Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your SOC gets an alert from the CNAPP: an outbound connection from a pod in the ai-prod namespace to . The destination is in the allowlist. The payload size is 28 kilobytes — well under the DLP threshold. The agent’s service account has permission to invoke the email tool. By every check your stack runs, the traffic is normal. Forty minutes later, a customer support lead notices that an email went out containing a summary of 2,400 customer records that the agent had no business querying.

A developer clones a repository and opens it in VS Code at 10:47 a.m. Before their cursor blinks, six different configuration file formats on disk have a chance to execute shell commands on the host. A.vscode/tasks.json with runOn: folderOpen. A.devcontainer/devcontainer.json with initializeCommand. A post-checkout hook already sitting in.git/hooks/. A postinstall line waiting in package.json for the next dependency install. A.envrc in the project root.

Accessing modern infrastructure requires more than a network-level foothold. As services spread across clouds, clusters, and regions, the question of who can reach what stops being a network question and becomes an identity question. Reverse proxies are the component that answers it. A reverse proxy sits between clients and backend services, validating identity and enforcing authorization on every inbound request before any application is touched.

AI agents are likely already running inside your infrastructure. They triage alerts, remediate incidents, provision resources, and make decisions without waiting for a human to approve each step. For teams aligned to NIST’s Cybersecurity Framework (CSF) 2.0, this creates a problem: the framework assumes human actors, human-speed decisions, and human-readable audit trails. Autonomous systems break all three assumptions. The good news is that CSF 2.0 was designed to be adapted.

A CISO running AI agents on GKE has watched three Google product launches in eighteen months — Model Armor, expanded Security Command Center coverage for AI workloads, additions to Chronicle’s curated detection content — and is being asked whether the GCP-native stack is now sufficient. The vendor demos and the Google Cloud blog say yes. The 2 AM analyst experience says something different.

SAST, or Static Application Security Testing, is a method of analyzing source code to find vulnerabilities before the application is deployed. It's a type of white box testing that scans the code without executing it, looking for weaknesses that could be exploited. SAST helps developers identify and fix security issues early in the Software Development Life Cycle (SDLC), potentially reducing costs and improving the overall security posture of the application.

Two big things in this release, a remote-updating CertKit agent Google Trust Store CA issuer support.

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.