Operations | Monitoring | ITSM | DevOps | Cloud

Prior to working for a software company, I spent most of my career working for financial organizations. I have lots of friends who still do. Talking with one the other day, the question came up, what keeps you up at night? Her one word response was a little surprising: Fraud. Understand, she’s in charge of managing data at a bank. You’d expect maybe uptime, performance, high availability, any of the standard data management worries. Instead, it’s fraud.

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

Technical debt refers to the future costs and limitations incurred when organizations opt for short-term solutions over robust, long-term scalable architectures. For the middle mile, technical debt often manifests as equipment or network designs that restrict long-term flexibility, scalability, or interoperability.

In 2025, DevOps teams faced a pivotal moment. The era of treating security as an afterthought was over. Practically overnight, airtight protection became a non-negotiable requirement across every layer of the technology stack, whether on prem, in the cloud, or at the network’s edge. For many teams, this wasn’t just a technical hurdle; it was a daily source of stress.

Think about how airlines assign seats on a plane. Some have extra legroom. Some sit near exits. Some are cheaper, while others cost a premium. Certain passengers also have strict requirements, like families traveling together or travelers who paid for a specific class. Now imagine boarding everyone randomly. A passenger who paid for extra legroom (perhaps for health reasons) ends up squeezed into a middle seat. Families scatter across the cabin. Premium seats sit half empty while the back rows overflow.

Manual mTLS vs. Service Mesh: Which is right for your Kubernetes cluster? Explore the operational risks of manual setups and the benefits of node-based encryption.