Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

What data are your employees feeding into unapproved AI tools? If you can't answer that question, then you might have shadow AI security risks that you don't know about. The Netwrix Cybersecurity Trends Report 2025 found that 37% of organizations have already had to adjust their security strategies due to AI-driven threats, while 30% haven't started AI implementation at all. That gap between how fast AI threats are evolving and how slowly organizations are responding is where shadow AI thrives.

In a content collaboration and governance market that often gets swept up in hype, it’s refreshing when an analyst focuses on what actually works.

IIf you’re using AI to generate code, you’re likely moving faster than ever. You’ve probably felt that surge of productivity when a complex logic problem gets solved in seconds or boilerplate code appears instantly. But here is the problem: speed without guardrails creates security debt, and with AI, that debt accumulates at a terrifying rate. Recent data paints a concerning picture.

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.