anomaly detection

Runtime Visibility & AI-powered Security in Cloud-Native Environments
Kubernetes and cloud-native platforms have transformed software delivery — but also redefined the attack surface. As threats shift to runtime, visibility and real-time response have become the new security frontline. AI-driven anomaly ...
Alan Shimel | | AI copilot, AI governance, AI in cybersecurity, anomaly detection, automated response, CI/CD security, cloud native security, cloud security, cloud-native defense, container security, DevSecOps, explainable AI, kubernetes, LLMs in security, observability, platform engineering, runtime protection, runtime security, runtime visibility, security automation, security telemetry, service mesh, threat detection, zero-trust

Bridging Observability & Security in Kubernetes: Beyond Just Metrics
Kubernetes has expanded agility but also the attack surface. Alan argues that observability and security can no longer live in silos — metrics, logs, and traces already hold critical security signals, while ...
Alan Shimel | | anomaly detection, C2 traffic, cloud native security, convergence, cross-training, crypto-mining, devops, kubernetes, lateral movement, logs, metrics, observability, observability-driven security, OpenTelemetry, organizational silos, platform engineering, runtime security, security, SRE, tool sprawl, traces

From Observability to Actionability: Why Metrics Alone Aren’t Enough
Observability has plateaued. The next step is actionable observability—using AI, automation, and SLOs to turn telemetry into reliable outcomes ...
Alan Shimel | | actionable observability, AIOps, anomaly detection, auto-remediation, cloud native, continuous verification, devops, ELK stack, golden paths, internal developer platforms, metrics logs traces, observability, OpenTelemetry, platform engineering, SLO-driven operations, SRE, telemetry automation

Rethinking Anomaly Detection in Cloud-Native Applications
From microservices to multi-cloud, modern application architectures have evolved significantly and created new challenges that are drowning engineers and DevOps teams in data and increasing the number of tools they are being ...