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AI-driven Kubernetes in Action: Exploring AI-Assisted Kubernetes Operations

Discover how AI is transforming Kubernetes from reactive troubleshooting to proactive, intelligent automation. Learn about essential AIOps tools, resource optimization strategies, and the challenges of managing AI-enabled container orchestration at scale ...

Joydip Kanjilal | April 20, 2026 | AI-powered Kubernetes, anomaly detection, autonomous Kubernetes, cloud native AI, container orchestration, K8sGPT, kubectl-ai, Kubeflow, Kubernetes AIOps, resource optimization

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AppSec in 2026: Security at Machine Speed — or Not at All

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AppSec in 2026: Security at Machine Speed — or Not at All

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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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