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Istio Weaves ‘Future-Ready’ Service Mesh for AI

Istio Weaves ‘Future-Ready’ Service Mesh for AI

At KubeCon + CNC 2026, Istio unveils Ambient Multicluster and the Gateway API Inference Extension to simplify AI infrastructure. Learn how sidecar-less mesh and agentgateway secure agentic workloads and boost deployment velocity ...

Adrian Bridgwater | March 27, 2026 | agentgateway, AI infrastructure, AI Workloads, Ambient Multi-cluster, cloud native, cncf, data plane, Gateway API Inference Extension, generative AI, Istio, KubeCon 2026, kubernetes, microservices, Node Proxy, observability, platform engineering, service mesh, Sidecar-less Mesh, traffic management, Waypoint Proxy

cloud architect architecture Diamanti storage networking

Diamanti Unifies Storage and Networking Across Kubernetes Clusters

Diamanti today launched a data plane management platform for hybrid cloud computing environments that marries storage management to a container networking overlay. Jenny Fong, vice president of marketing for Diamanti, says Diamanti ...

Mike Vizard | October 22, 2020 | containers, data plane, hybrid cloud, kubernetes

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Multi-Million-Dollar Lessons: What FinOps Maturity Actually Looks Like

14 May 2026

Multi-Million-Dollar Lessons: What FinOps Maturity Actually Looks Like

Analyze Terraform using Agentic GenAI workflows

14 May 2026

Analyze Terraform using Agentic GenAI workflows

Stress‑Testing AWS Infrastructure with Terraform: A Hands‑On Technical Challenge-apj

11 May 2026

Stress‑Testing AWS Infrastructure with Terraform: A Hands‑On Technical Challenge-apj

From Prompt to Exploit: How LLMs Are Changing API Attacks

13 May 2026

From Prompt to Exploit: How LLMs Are Changing API Attacks

Data is the Differentiator for Exposure Management

6 May 2026

Data is the Differentiator for Exposure Management

The Context Engine: Why Consolidation is the Natural Future of AppSec

5 May 2026

The Context Engine: Why Consolidation is the Natural Future of AppSec

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ThreatHunter.ai Halts Hundreds of Attacks in the past 48 hours: Combating Ransomware and Nation-State Cyber Threats Head-On

ThreatHunter.ai Halts Hundreds of Attacks in the past 48 hours: Combating Ransomware and Nation-State Cyber Threats Head-On

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Deloitte Partners with Memcyco to Combat ATO and Other Online Attacks with Real-Time Digital Impersonation Protection Solutions

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The State of Incident Response and Observability

Step 1 of 7

14%

Which of the following best describes your involvement with observability or incident response in your organization?(Required)

I actively use observability tools and respond to incidents

I select, manage, or make decisions about observability tools and workflows

I do both—use observability tools for incidents and make tooling/workflow decisions

None of the above

What are your biggest pain points in incident response today? (Select up to 4)(Required)

Too many alerts / alert noise

Slow root cause analysis

Lack of cross-tool visibility

Manual correlation across logs, metrics, and traces

Too many people required for incidents

Lack of automation

Incomplete or missing data

Too much data

Multiplying data sources

Which of the following best describes your team’s current use of AI in observability and operations?(Required)

Not using any AI for operations, including AI incorporated into our tools

AI assists investigations (e.g., suggests related signals or docs)

AI automatically triages incidents (e.g., classifies, routes, prioritizes)

AI takes remediation actions (e.g., executes runbooks or rollbacks)

When your team uses or has evaluated AI-assisted observability, how well does the AI include the full context of an incident—including relationships across services, infrastructure, recent changes, and team knowledge?(Required)

AI has rich, cross-system context and surfaces relevant relationships automatically

AI has some context but frequently misses critical signals from other tools or systems

AI operates mostly on isolated data from a single tool with little cross-system awareness

AI provides generic or irrelevant suggestions that show little understanding of our environment

We have not used or evaluated AI in observability

If an AI agent could investigate incidents and identify root cause in minutes, what would you most want it to do next?(Required)

Recommend next steps for a human to execute

Automatically remediate with human approval

Fully automate resolution without human involvement

Not ready to trust AI for any of these yet

What are the two biggest risks to your production environment today? (Select no more than 2)(Required)

Configuration changes/drift

Untested code deployments

Underlying infrastructure issues

Database issues

Staffing constraints / key-person dependency

Skills gaps

Lack of visibility across systems

What percentage of your team’s operational time is spent on reactive incident response versus proactive prevention and improvement?(Required)

Mostly proactive (~75% or more on PREVENTION)

Balanced (roughly 50–50)

Mostly reactive (~75% or more on RESPONSE)

Almost entirely reactive

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