Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In a world where billions of devices now shape our connected reality, IoT security has evolved from an IT problem to a board-level priority. As the iot ecosystem grows—an interconnected network of devices, systems, and infrastructures—comprehensive iot security solutions have become essential to protect, manage, and scale these environments. Yet despite the growing awareness, one challenge remains stubbornly persistent — the management and protection of unmanaged devices.

As the Internet of Things (IoT) continues to expand across industries, risk management has become one of the most pressing challenges for security and compliance leaders. The convergence of AI and IoT (ai iot) is accelerating this transformation, introducing new opportunities but also creating a more complex risk landscape that requires advanced approaches to risk management.

Agentless network monitoring represents a fundamental shift in cloud security strategy. Rather than installing software agents across every cloud resource, this approach leverages existing infrastructure to gather comprehensive security intelligence remotely.

Data sovereignty means that data is subject to the laws and governance of the country where it is stored or processed. In simpler terms, if your AI system stores user data in Germany, you’re bound by EU’s GDPR rules — even if your company operates from the U.S. As AI and large language models (LLMs) become central to business operations, data sovereignty is no longer just a compliance checkbox.

AI agents aren’t waiting in the wings anymore. They’re approving payments, spinning up cloud resources, and pulling sensitive data at machine speed. Blink, and a swarm of them has already acted a thousand times before anyone can check the logs. But with all that speed and capability comes risk. For many teams, it’s the authentication model—not the tech—that’s breaking.

I have never been one to jump on most technology bandwagons early; I am very pragmatic about what technology can do rather than what it promises. This extends to generative AI. I was not the first to play with ChatGPT and Gemini when they came out in the early 2020s. Maybe it’s because I work in fields that use machine learning very effectively. Even though I was aware of the leap Google made in 2012, I wasn’t eager to dive into the new wave of AI when it first appeared.

In early September 2025, attackers used a phishing email to compromise one or more trusted maintainer accounts on npm. They used this to publish malicious releases of 18 widely used npm packages (for example chalk, debug, ansi-styles) that account for more than 2 billion downloads per week. Websites and applications that used these compromised packages were vulnerable to hackers stealing crypto assets (“crypto stealing” or “wallet draining”) from end users.