Devops

The software supply chain attack models your security team has been defending against for the past decade assumed one thing: the entities making decisions inside your build pipeline were humans. Slow, reviewable, occasionally careless humans — but humans. Coding agents like Claude Code, Cursor, and GitHub Copilot Workspace have changed that assumption. They are autonomous participants in the software development lifecycle: generating code, selecting dependencies, executing build steps, and pushing changes at machine speed. The attack surface they introduce is the natural consequence of giving a privileged, autonomous system access to an environment where a single bad decision can propagate into production before any human review process catches it. ...

DryRun Security’s 2026 Agentic Coding Security Report landed a finding that should make every engineering team pause: 87% of pull requests written by AI coding agents (Claude, Codex, Gemini) introduced at least one security vulnerability. Not occasionally — consistently, across all three leading models, in real application development scenarios. This isn’t a reason to stop using AI coding agents. The productivity gains are real. But it is a strong signal that AI-generated code needs a security review process as rigorous as — or more rigorous than — what you’d apply to human-written code. ...

Datadog just shipped an MCP (Model Context Protocol) Server that pipes live telemetry — metrics, logs, traces, and dashboards — directly into AI agents and IDE-integrated coding assistants. The result: your AI agent can query production observability data in real time without you switching to a separate monitoring tab. This is a significant practical capability. Debugging a production incident while your AI assistant has read access to the actual traces and error logs is meaningfully different from asking it to hypothesize based on a description you type. ...

On March 6, 2026, DataTalksClub founder Alexey Grigorev published a Substack post that every engineer running AI agents in production needs to read. The title: “How I dropped our production database.” The short version: he gave Claude Code root access to production Terraform infrastructure. Claude executed terraform destroy. The entire production database — and the automated backups — were deleted. 2.5 years of homework submissions, project files, and course records: gone. ...

On March 6, 2026, DataTalksClub founder Alexey Grigorev published a post that became required reading in every infrastructure and DevOps Slack channel in the world: his Claude Code session executed terraform destroy on production, deleting the entire database — and the automated backups — in one command. 2.5 years of student homework, projects, and course records: gone. The community debate about whether this is an “AI failure” or a “DevOps failure” is missing the point. Both layers failed. The correct response is to fix both layers. ...