Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

On December 3, 2025, immediately following the public disclosure of the critical, maximum-severity React2Shell vulnerability (CVE-2025-55182), the Cloudforce One Threat Intelligence team began monitoring for early signs of exploitation. Within hours, we observed scanning and active exploitation attempts, including traffic originating from infrastructure associated with Asian-nexus threat groups.

Let’s be honest: if your SOC is drowning in noise, you’re not “doing security.” You’re babysitting tools that cry wolf. And the wolves? They’re doing just fine. Every vendor claims their AI magically cuts false positives. Most don’t. Why?

This is a predictions blog. We know, we know; everyone does them, and they can get a bit same-y. Chances are, you’re already bored with reading them. So, we’ve decided to do things a little bit differently this year. Instead of bombarding you with just our own predictions, we’ve decided to cast the net far and wide. We’ve spoken to cybersecurity experts from around the world to answer what’s, for us, the most pressing question of all.

An API token is like a small digital key that tells a system that a user or an app is allowed to act in the system. When this key gets stolen, attackers act as real users and misuse the account. It’s called API token hijacking, and this issue has grown in the last few years. Most companies are not able to detect this problem in time. It’s important for IT/security teams to understand token theft to respond quickly and build stronger protection for future attacks.

When it comes to distributing PHP applications, discussions often swing between two extremes: fully open-source everything or lock all your code behind encryption/encoding. Critics of encoding often argue that open source is superior because users can still inspect and customise code. But the truth is far more nuanced, and the most successful software vendors already know it.

When Anthropic dropped the Model Context Protocol (MCP) in late 2024, it felt like the missing puzzle piece for AI tooling: a standard way for Large Language Models (LLMs) to talk to data sources, APIs, and pretty much anything else you can think of. Think of it as a USB-C port for AI, as the protocol’s creators like to say. But like most shiny new standards, the devil’s in the details.

In most startups building or using AI, privacy often gets treated like a checkbox that legal or security will “handle later.” That mindset quietly kills deals, scares off enterprise buyers, and limits your access to the very data your models need. Here is the truth that more founders and CTOs are embracing. Privacy makes your product easier to buy, models better to train, and business more valuable.