Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

It’s Friday afternoon, and you need a quick team update. Five minutes, tops, right? You ping Slack. A few people reply, a few don’t. So, you schedule a “quick sync” to get everyone on the same page. Two hours later, you’ve spent your afternoon chasing updates instead of doing actual work. And you’ll do it all over again next week. Now picture this. You’re collecting product demo videos for an agency.

It’s 2 a.m. and the SOC has a Tier 3 page. A customer-service agent on the production cluster has just wired refund payments to seven addresses outside the approved disbursement list. The runbook is unambiguous: isolate the pod, image the disk, image the memory, root-cause within 48 hours.

Every AI workload security PoC reaches the same conversation. Platform engineering pushes back: the AI team won’t accept extra latency on inference. The security engineer hunts for benchmarks and finds a contradiction. Langfuse publishes 15% overhead. AgentOps publishes 12%. The security vendor quotes 1–2.5%. None is lying. They measure different layers.

Most “hardening” advice for AI agents is a checklist of things to configure before the agent runs. CIS Kubernetes Benchmark gates. Pod Security Standards baselines. NetworkPolicy templates. None of it’s wrong — it’s just one of four phases, the one your stack already covers. The other three are Observe, Enforce, and Reconcile. They’re where AI agents actually get breached, and they’re where most stacks have nothing.

Artificial intelligence is rapidly becoming embedded across manufacturing environments, from engineering and design to supply chain optimisation and operations. What was once experimental is now being applied in day-to-day workflows, often driven by the need for speed, efficiency, and competitive advantage. Recent research shows that 73% of manufacturing organisations report rapid AI adoption, with 90% ranking AI as a top security priority for 2026. The direction of travel is clear.