Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The shadow technology problem is getting worse. Over the past few years, organizations have scaled microservices, cloud-native apps, and partner integrations faster than corporate governance models could keep up, resulting in undocumented or shadow APIs. We’re now seeing this pattern all over again with AI systems. And, even worse, AI introduces non-deterministic behavior, autonomous actions, and machine-to-machine decision-making. Put simply, shadow AI is much, much riskier than shadow APIs.

When news broke that the Head of CISA uploaded sensitive data to ChatGPT, the response was predictable: panic, headlines, and renewed questions about AI safety. But this incident reveals more about confusion than actual risk. The real issue? Most organizations don’t understand what they’re actually risking when they use AI tools. Let’s fix that.

If you've been paying attention to the AI agent space over the past few months, you've probably noticed a pattern: every week brings a new story about an AI agent doing something it absolutely should not have done: reading private emails, exfiltrating credentials, or executing shell commands that a human would have never approved. The OpenClaw saga alone gave us exposed databases, command injection vulnerabilities, and a $16 million scam token, all in the span of about five days.

For most engineering teams, AI feels like a breakthrough years in the making. Code gets written faster, reviews move quicker, and releases that once took weeks now happen in days—or even hours. But as more of the software lifecycle becomes automated, a less comfortable reality is setting in: application security hasn’t kept pace, and AI-native security practices are often missing. When AppSec foundations are immature, AI doesn’t reduce risk—it scales it.

WordPress is entering a new phase in how websites are managed with the introduction of API Abilities and support for the Model Context Protocol (MCP). These updates allow WordPress core, plugins, and themes to clearly define the actions they support and how those actions should be executed. For the first time, WordPress can communicate its capabilities in a structured way that large language models can reliably understand.

As we embed AI agents into our lives and workflows, we’re learning the (sometimes surprising) ways in which they outperform human beings, and other ways in which they fall short. And occasionally, we find an example where agents, paradoxically, are both better and worse than their human users.