Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most teams adopting AI in their workflows understand that LLMs do not behave like traditional software. The same input does not always produce the same output, and even when it does, the model can be wrong, manipulated, or misled. Hallucinations happen even without adversarial input. Air Canada learned this in 2024 when a tribunal ordered the airline to honor a bereavement-fare refund policy its support chatbot had invented out of thin air.

On May 5, 2026, at roughly 19:30 UTC, DENIC, the registry operator for the.de country-code top-level domain (TLD), started publishing incorrect DNSSEC signatures for the.de zone. Any validating DNS resolver receiving these signatures was required by the DNSSEC specification to reject them and return SERVFAIL to clients, including 1.1.1.1, the public DNS resolver operated by Cloudflare. The country-code top-level domain for Germany, .de, is one of the largest on the Internet.

Anyone who's stood up a SIEM from scratch knows the feeling: weeks of infrastructure work, integration headaches, and a services team alongside for the whole process. That experience shaped how people think about adopting anything new in security ops. The instinct is to treat AI the same way: budget for it, plan for it, bring in specialists. This instinct is costing teams real time. Traditional infrastructure takes great effort to stand up. Infrastructure-as-code happens in seconds.

There is a lot of panic around Claude Mythos. Some people are saying it will hack every system, that the sky is falling, and that there is no stopping it. That fear is dangerous because it makes teams freeze. Claude Mythos is genuinely powerful. AI systems like this can find security issues in minutes that even experienced penetration testers might take weeks to identify and exploit. That part is real. But here is the important point: AI is still exploiting what is already there.

If you ask any MSP what they use to protect their clients’ cloud environments, you will get one of two answers. Either they’ll point to the native security tools built into platforms like Microsoft 365 or Google Workspace. Or they’ll describe a patchwork of different products stitched together to cover identity, configuration, and SaaS visibility. Neither approach is ideal. But both reflect the reality MSPs are working with today.

MSPs are managing more customers, more environments, and more tools than ever before. At the same time, customer expectations keep rising -- faster response times, clearer reporting, and consistent service across every client. All of that pressure lands on already‑lean teams. That’s the reality Rai was built for.

In May 2025, a developer using Claude with the GitHub MCP server asked their AI assistant to do something entirely routine: review the open issues in a public repository. The repository contained a malicious GitHub issue planted by a researcher demonstrating a security vulnerability. The issue contained hidden instructions. The AI read them, followed them, accessed the developer's private repositories, and posted the contents in a publicly visible pull request. No credentials were stolen.