Operations | Monitoring | ITSM | DevOps | Cloud

FinOps thrives on clarity, and clarity is built on metrics. Metrics give engineering and finance a shared language to understand costs, evaluate trade-offs, and guide innovation. The most impactful metrics go beyond “how much are we spending?” and help us answer: When we measure these things, we stretch beyond tracking progress to fueling it.

(Almost) everyone is using generative AI, and just as many aren’t seeing any benefits. Research firm Gartner calls it the “gen AI paradox” — nearly 80% of companies say they’ve invested in generative solutions, and the same number report no benefits to their bottom line. What’s more, 90% of projects are stuck in pilot mode; ready to take off, but just can’t get up to speed.

Learn what IPsec is, how it works, key protocols and VPN types, and how to combine IPsec with NaaS for secure, scalable network connectivity.

Harness makes software delivery in AWS faster, safer, and more delightful. Harness, the AI Platform for Everything After Code, offers CI/CD, infrastructure-as-code management, and cloud cost management capabilities tailored to the AWS environment. Harness has come a long way since its 2019 debut on the AWS Marketplace. Back then, over half of Harness customers were already running on AWS, and Harness focused on delivering Continuous Delivery as a Service for AWS applications.

Harness Database DevOps was born from a simple question - how can database delivery be as seamless and safe as application delivery? Through deep collaboration with design partners, open-source learnings, and relentless iteration, the team built a platform that unites developers, DBAs, and DevOps under a single, automated workflow. At its core, it’s a story of empathy-driven engineering - transforming database change management into a faster, more reliable, and collaborative experience.

Shai-Hulud 2.0 shows how quickly a compromised maintainer account can result in thousands of infected NPM packages and repositories within hours. Harness SCS provides end-to-end SBOM visibility, policy enforcement to block compromised NPM packages, and complete traceability to detect malicious components early and prevent them from entering your pipelines.

Every year, KubeCon offers a candid look at where the cloud-native community stands — the tools gaining traction, the pain points teams share, and the big gaps still holding organizations back. After a week of deep conversations, session hopping, and talking to dozens of platform teams, one theme became impossible to ignore: Platform engineering still isn’t doing enough testing. And even more surprising: many teams don’t think testing is their responsibility.

As security data volumes grow, so do the costs of processing and storing them. Microsoft Sentinel and other SIEM platforms charge based on data ingestion, which makes every decision about normalization rules critical and every duplicate log a direct expense. Enterprise-scale security data pipelines face a persistent problem: data duplication across normalization tiers. As logs move through multiple transformation stages, it’s often impossible to know in advance which version will succeed.

The holiday shopping season is a familiar ritual for many. We spend hours researching the best deals, comparing features, and reading reviews to make sure we’re investing in the right things. As we all come to grips with the fact that 2026 is right around the corner, engineering leaders are doing the same thing, but largely in response to the explosion of AI developer tools.