AI Meets Kubernetes Security: Tigera CEO Reveals What Comes Next for Platform Teams

Kubernetes adoption is growing rapidly, but so are complexity and security risks.

Tigera CEO, Ratan Tipirneni, on Calico AI and the Push for Simpler, Unified Kubernetes Security

Platform teams are tasked with keeping clusters secure and observable while navigating a skills gap. At KubeCon + CloudNativeCon North America, The New Stack spoke with Ratan Tipirneni, President and CEO of Tigera, about the future of Kubernetes security, AI-driven operations, and emerging trends in enterprise networking. The highlights from that discussion are summarized below.

Portions of this article are adapted from a recorded interview between The New Stack’s Heather Joslin and Tigera CEO Ratan Tipirneni. You can watch the full conversation on The New Stack’s YouTube channel. Watch the full interview here

How Can Teams Better Manage the Kubernetes Blast Radius and Skills Gap?

Tipirneni emphasizes the importance of controlling risk in Kubernetes clusters. “You want to be able to microsegment your workloads so that if you do come under an attack, you can actually limit the blast radius,” he says.

Egress traffic is another area of concern. According to Tipirneni, identifying what leaves the cluster is critical for security and compliance. Platform engineers are often navigating complex configurations without decades of Continue reading

AWS, Google Build a Multicloud Bridge

Addressing a long-standing perceived roadblock in enabling systems to span multiple cloud services, Amazon Web Services and Google Cloud have jointly developed a standard for customers to easily bridge their cloud deployments with Layer 3 connectivity. The idea, according to both companies, is to make it easy for their customers with cloud operations in both clouds to network them together in a private network, reducing the burden of maintaining multicloud connectivity, and perhaps even dispelling fears of cloud lock-in. Such easy connectivity may even spur customers to create more multicloud applications, theConnection Coordinator API specification is built on OpenAPI 3.0 customized for easily provisioning dedicated bandwidth between two cloud providers. The two cloud giants want other cloud providers to use the API as well. AWS implemented the spec in Google Cloud’s Cross-Cloud Interconnect. Both companies pledge to “engage in continuous monitoring to proactively detect and resolve issues,” according to the AWS website. The private lines between Google and AWS will be built on

HS119: Securing 2026: How AI, Quantum, and the AI-Powered Browser are Driving Enterprise Defense (Sponsored)

Anand Oswal, Executive Vice President at Palo Alto Networks, joins Johna Johnson and John Burke for a wide-ranging exploration of two emerging focal points of enterprise risk: cryptographically relevant quantum computing, and browser-mediated agentic AI. The looming arrival of quantum computers that can break legacy encryption has already created the threat of “harvest now, decrypt... Read more »

NB554: AWS, Google Link Public Clouds; Trading Data Center Has Zero Chill

Take a Network Break! We start with listener follow-up on Fortinet’s vulnerability numbering, and sound a red alert about an authentication bypass vulnerability in ASUS’s AiCloud service. AWS and Google announce a joint cross-cloud interconnect offering (other cloud providers are invited to play), Microsoft and Ciena pitch a new design to boost optical network resiliency,... Read more »

Multi-Pod EVPN Troubleshooting (Part 3)

Last week, we fixed the mismatched route targets in our sample multi-pod EVPN fabric. With that fixed, every PE device should see every other PE device as a remote VTEP for ingress replication purposes. We got that to work on Site-A (AS 65001), but not on Site-B (AS 65002); let’s see what else is broken.

Note: This is the fifth blog post in the Multi-Pod EVPN series. If you stumbled upon it, start with the design overview and troubleshooting overview posts. More importantly, familiarize yourself with the topology we’ll be using; it’s described in the Multi-Pod EVPN Troubleshooting: Fixing Next Hops.

Ready? Let’s go. Here’s our network topology:

Read more …

Computer Network Design Complexity and Tradeoffs Training

I’m teaching a “one off” special event class over on O’Reilly’s platform (via Pearson) this coming Friday, the 5th of December. From the Description:

Join networking engineer and infrastructure expert Russ White for this exclusive, one-time event exploring the critical role of tradeoffs in network design. We’ll begin by unpacking how complexity shapes the decisions architects and designers must make, and how tradeoffs are often an unavoidable part of navigating that complexity. Through real-world examples, you’ll learn how different network design choices impact overall system complexity, and how to approach these decisions with greater clarity and confidence. We’ll wrap up with an in-depth discussion of unintended consequences—how they arise, how to anticipate them, and how they relate to designing in complex, adaptive environments.

As always, if you register for the course you can watch later.

Register here.

The Road To HPC And AI Profits Is Paved With Good Intentions

With a profitable PC business that has 25 percent of global shipments (thanks in large part to its acquisition of IBM’s PC business two decades ago) plus a respectable smartphone business (by virtue of its Motorola acquisition), the client device business at Lenovo is finally back to where it was during the peak of the coronavirus pandemic and is consistently delivering what are decent profits for this cut-throat part of the IT sector. …

The Road To HPC And AI Profits Is Paved With Good Intentions was written by Timothy Prickett Morgan at The Next Platform.

Tech Bytes: Bringing AI Reasoning to Infrastructure with Itential FlowAI (Sponsored)

Itential has announced FlowAI, a new offering that brings agentic AI to Itential’s network automation platform. On today’s Tech Bytes podcast Ethan Banks talks with Peter Sprygada, Chief Architect at Itential, about how FlowAI works, its components, and how Itential uses the Model Context Protocol (MCP). They also dig into how FlowAI supports AI-driven orchestration... Read more »

netlab as the Universal Configuration Translator

Dan Partelly, a heavy netlab user (and an active contributor), sent me this interesting perspective on how one might want to use netlab without ever building a lab with it. All I added was a bit of AI-assisted editing; my comments are on a grey background.

In all podcasts and interviews I listened to, netlab was referred to as a “lab management solution”. But this is misleading. It’s also a translator, due to its ability to abstract devices, and can easily generate perfectly usable configs for devices or technologies you have never worked on.

Read more …

Why Replicate is joining Cloudflare

We're happy to announce that as of today Replicate is officially part of Cloudflare.

When we started Replicate in 2019, OpenAI had just open sourced GPT-2, and few people outside of the machine learning community paid much attention to AI. But for those of us in the field, it felt like something big was about to happen. Remarkable models were being created in academic labs, but you needed a metaphorical lab coat to be able to run them.

We made it our mission to get research models out of the lab into the hands of developers. We wanted programmers to creatively bend and twist these models into products that the researchers would never have thought of.

We approached this as a tooling problem. Just like tools like Heroku made it possible to run websites without managing web servers, we wanted to build tools for running models without having to understand backpropagation or deal with CUDA errors.

The first tool we built was Cog: a standard packaging format for machine learning models. Then we built Replicate as the platform to run Cog models as API endpoints in the cloud. We abstracted away both the low-level machine learning, and the complicated Continue reading

Harga $8, Kritik Berlipat: McDonald’s Hadapi Badai Online di Tengah Krisis Harga

Raksasa makanan cepat saji McDonald’s mencoba menawarkan nilai. Namun, promosi terbarunya justru memicu badai. Perusahaan mempromosikan paket McNugget seharga $8. Tetapi, konsumen merasa harga itu tidak masuk akal. Insiden ini menunjukkan adanya masalah yang lebih dalam. Perusahaan berjuang mempertahankan citra keterjangkauannya. Akibatnya, kritik online menggema dengan sangat keras.

Promosi $8 yang Memicu Amarah Online

Awal bulan ini, McDonald’s mengumumkan promosi terbatas. Promosi itu berisi 10 potong McNugget, kentang, dan minuman. Perusahaan memposisikannya sebagai penawaran nilai yang bagus. Namun, respons di media sosial sangat negatif. Banyak orang mengeluh di bawah postingan perusahaan. Mereka menyuarakan ketidakpuasan mereka secara terbuka.

Sebagai contoh, satu komentar menanyakan nilai dari promosi tersebut. “Sejak kapan $8 adalah harga yang bagus untuk nugget?” tulis seorang komentator. Keluhan lain berfokus pada kualitas dan layanan. Waktu tunggu di drive-thru juga menjadi sorotan. Akibatnya, postingan itu dipenuhi ratusan ulasan negatif.

Perusahaan mencoba merespons keluhan tersebut. Mereka meminta pengguna untuk mengirim informasi kontak mereka. Tujuannya adalah untuk menyelesaikan masalah secara privat. Namun, usaha itu tidak meredakan amarah publik. Badai kritik online terus berlanjut tanpa henti. Situasi ini menunjukkan jarak antara persepsi Continue reading

KubeCon NA 2025: Three Core Kubernetes Trends and a Calico Feature You Should Use Now

The Tigera team recently returned from KubeCon + CloudNativeCon North America and CalicoCon 2025 in Atlanta, Georgia. It was great, as always, to attend these events, feel the energy of our community, and hold in-depth discussions at the booth and in our dedicated sessions that revealed specific, critical shifts shaping the future of cloud-native platforms.
We pulled together observations from our Tigera engineers and product experts in attendance to identify three key trends that are directly influencing how organizations manage Kubernetes today.

🤖 Trend 1: Kubernetes is Central to AI Workload Orchestration

A frequent and significant topic of conversation was the role of Kubernetes in supporting Artificial Intelligence and Machine Learning (AI/ML) infrastructure.
The consensus is clear: Kubernetes is becoming the standard orchestration layer for these specialized workloads. This requires careful consideration of networking and security policies tailored to high-demand environments. Observations from the Tigera team indicated a consistent focus on positioning Kubernetes as the essential orchestration layer for AI workloads. This trend underscores the need for robust, high-performance CNI solutions designed for the future of specialized computing.

🌐 Trend 2: Growth in Edge Deployments Increases Complexity

Conversations pointed to a growing and tangible expansion of Kubernetes beyond central data centers and Continue reading

Lab: IS-IS Route Redistribution

Route redistribution into IS-IS seems even easier than its OSPFv2/OSPFv3 counterparts. There are no additional LSAs/LSPs; the redistributed prefixes are included in the router LSP. Things get much more interesting once you start looking into the gory details and exploring how different implementations use (or do not) the various metric bits and TLVs.

You’ll find more details (and the opportunity to explore the LSP database contents in a safe environment) in the IS-IS Route Redistribution lab exercise.

Click here to start the lab in your browser using GitHub Codespaces (or set up your own lab infrastructure). After starting the lab environment, change the directory to feature/7-redistribute and execute netlab up.

NANOG 95

NANOG held its 95th meeting in Arlington, Texas in October of 2025. Here's my take on a few presentations that caught my attention through this three-day meeting.

UET Relative Addressing and Its Similarities to VXLAN

 Relative Addressing


As described in the previous section, applications use endpoint objects as their communication interfaces for data transfer. To write data from local memory to a target memory region on a remote GPU, the initiator must authorize the local UE-NIC to fetch data from local memory and describe where that data should be written on the remote side.

To route the packet to the correct Fabric Endpoint (FEP), the application and the UET provider must supply the FEP’s IP address (its Fabric Address, FA). To determine where in the remote process’s memory the received data belongs, the UE-NIC must also know:

  • Which job the communication belongs to
  • Which process within that job owns the target memory
  • Which Resource Index (RI) table should be used
  • Which entry in that table describes the exact memory location

This indirection model is called relative addressing.

How Relative Addressing Works

Figure 5-6 illustrates the concept. Two GPUs participate in distributed training. A process on GPU 0 with global rank 0 (PID 0) receives data from GPU 1 with global rank 1 (PID 1). The UE-NIC determines the target Fabric Endpoint (FEP) based on the destination IP address (FA = 10.0.1.11). This Continue reading

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