Operations | Monitoring | ITSM | DevOps | Cloud

I have been in networking for a long time, and I still consider packets to be a great tool for troubleshooting. When I need to understand what actually happened between two systems, the packets are often the best evidence available.

One of the most frustrating challenges I faced as a network engineer was troubleshooting problems that had already disappeared. A user would call and report that an application was performing terribly. A site would become unusable for several minutes. Voice calls would break up. File transfers would suddenly slow to a crawl. Then, by the time I started investigating, everything would be working normally again.

I remember the good old days, when troubleshooting application traffic often meant I could examine much of the conversation between a client and server in clear text. Not anymore. Today, nearly everything is encrypted. That is obviously a good thing for security and privacy, but it has also created a common misconception that packet analysis is no longer useful because engineers cannot see the application headers and payload.

Recent findings from Anthropic’s Project Glasswing offer a useful indication of where vulnerability discovery may be heading. Anthropic reported that it and its partners had used Claude Mythos Preview to identify more than 10,000 high- or critical-severity vulnerabilities across the software they reviewed. More significantly, Anthropic reported that the bottleneck had shifted from finding vulnerabilities to having the capacity to verify, disclose, and patch them.

What caught my attention in the recent OpenAI and Hugging Face incident wasn’t any one exploit. It was the way the models could keep progressing across systems, combining techniques and acting with a level of speed and persistence that changes how security teams need to operate. The incident emerged during internal cybersecurity evaluations in July 2026.

Financial-services organizations may already have multiple tools telling them what exists across their environment. The problem is that those tools do not always agree. An endpoint platform may recognize a device that a vulnerability scanner does not. A network system may identify an active device that is missing from another asset record. The same device may even appear under multiple names across different platforms. That creates a simple but important question: Which view can you trust?

Back when I was a network administrator, one of the most frustrating phrases I would hear was: “The application is slow.” Today, after years of working with customers, not much has changed. The technologies are different, the networks are faster, and the applications are increasingly distributed across data centers, cloud platforms, and SaaS providers, but the troubleshooting challenge is often the same. Users know something is slow. Application teams believe the servers are healthy.

When Palo Alto Networks announced its intention to acquire Embrace, my first thought wasn’t about Embrace itself. My first thought was that digital experience, including Digital Employee Experience (DEX), has officially become strategic. For years, DEX has often been viewed as something that sat on the edge of infrastructure monitoring, endpoint management, and service desk operations. Important? Absolutely. Strategic? Not always. This acquisition changes that conversation.

Most organizations have responded to the rapid growth of AI by creating written policies that define acceptable use. A clear AI policy can establish expectations, assign responsibilities, and help employees understand how AI should and shouldn’t be used. But policy alone can’t provide oversight and is almost impossible to enforce at scale without the right tools.

For the past couple of years, most conversations about AI in business have centered on adoption. Which tools should organizations deploy? Where can AI improve productivity? How quickly can it deliver value? Those questions still matter, but they are no longer the most difficult ones to answer. AI is now appearing across business applications, employee workflows, development environments, and customer-facing services.