Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

When Anthropic announced Claude Code Security on February 20th—a tool that scans codebases for vulnerabilities and suggests patches for human review—the reaction from markets was swift and brutal. Major cybersecurity names watched their stock prices fall by double digits within days. The implied thesis behind the selling: AI can now do what these companies do, so why pay for them? It's a compelling fear and an inaccurate conclusion at the same time. The DLP space is a clear example of why.

Microsoft is making a fundamental change to how outbound internet connectivity works for virtual machines within Azure Virtual Networks. From March 2026, default outbound access will be retired for new virtual networks, requiring organisations to explicitly design and configure outbound connectivity for their workloads.

TeraBox is a Japanese cloud storage company, offering the most free cloud storage available, at 1TB. While this may seem like an attractive offer, in exchange for this storage, your free plan includes promotional advertising, speed restrictions, and privacy concerns that may cause you to reconsider for a more private alternative. To help you understand more about Terabox, what it offers, its restrictions, and potential alternatives, we will cover the following throughout this article.

Organizations adopt multi-cloud architectures for many reasons, including compliance requirements, business strategy, and resiliency. Regardless of the cloud provider, the security challenges remain the same: Identify the most critical risks, prioritize them with business context, and remediate them before they are exploited by a bad actor.

Every cloud security vendor now has an AI-SPM dashboard. Strip away the branding, though, and most of these dashboards are doing the same thing: checking IAM configurations, scanning for misconfigured network access, inventorying AI models across cloud accounts, and flagging compliance gaps. It’s cloud security posture management with an AI label applied. That’s a problem, because AI workloads don’t behave like other cloud workloads.

Your security stack was built for workloads that follow predictable code paths. AI agents don’t. They interpret prompts, generate code on the fly, invoke tools dynamically, and escalate privileges in ways no developer anticipated — all as part of normal operation. The signals that indicate a compromise in a traditional container are indistinguishable from an AI agent doing its job. And most detection tools can’t tell the difference. This isn’t a theoretical gap.

Your CISO just got word that engineering is deploying AI agents into production Kubernetes clusters next quarter. Not chatbots—autonomous agents that generate and execute code, call external APIs through MCP tool runtimes, access internal databases, and make decisions without human review. The question lands on your security team: “How are we securing these?”