The cloud stories enterprise IT leaders actually read.
Volume 342, Issue 2 · 6 articles from 2 issues
Analysis of 3,000 organizations found AWS, Azure and Google Cloud risk profiles have almost nothing in common.
Intruder's 2026 Cloud Security Index analyzed 3,000 organizations across AWS, Azure and Google Cloud and found their risk profiles have almost nothing in common.
The practical consequence is that a unified cloud security checklist, applied identically across providers, will over-invest in controls that matter on one platform and miss the dominant risks on another.
The report breaks down which exposure categories dominate on each provider. For teams running genuinely multi-cloud estates, it makes a case for provider-specific baselines rather than a single abstracted policy.
Enterprise AI is settling onto the cloud native stack, visible in ecosystem standardization rather than vendor roadmaps.
Alan Shimel argues that enterprise AI is settling onto the cloud native stack rather than displacing it, and that the evidence is in ecosystem standardization rather than vendor roadmaps.
CNCF project graduations covering AI and multicluster orchestration, plus the appearance of AI inference tracks at KubeCon, indicate where the workloads are actually landing.
The piece pushes back on the narrative that AI requires a separate infrastructure paradigm. For platform teams the practical takeaway is that existing Kubernetes investment and skills transfer to AI workloads more directly than vendor messaging implies.
Moving AI workloads to public cloud requires assessment, modernization, careful data planning, phased rollout and ongoing governance.
Local AI deployments are costly to build and power and can limit scalability, pushing companies to host LLMs and AI platforms in public clouds.
TechTarget's Stephen J. Bigelow warns that lift-and-shift migrations rarely work well for AI and can degrade performance.
Teams should map every model, agent, pipeline and dependency, and assess data constraints such as PII or intellectual property that may require local handling.
Components should be modernized by refactoring, rearchitecting or rebuilding for cloud-native infrastructure, followed by a well-structured data pipeline, phased deployment and continuous management and governance.
Resilient watch cache init, stable KYAML and beta scale-to-zero are the v1.37 changes with operational consequences.
Kubernetes v1.37, codenamed Garhwal, contains 67 enhancements, and the article filters them for operators. The changes that matter most are resilient watch cache initialization, which prevents a class of control plane outage, stable KYAML for reducing YAML type coercion errors, HorizontalPodAutoscaler scale-to-zero reaching beta, and manifest-based admission control.
Alongside the release, CNCF graduated Kubeflow, Karmada and Cloud Native Buildpacks.
The piece also argues security deserves a place on day-zero checklists alongside networking and storage, particularly identity integration with external providers.
Top Cloud Technology Trends to Watch in 2027
Cloud infrastructure is evolving beyond basic scalability toward intelligence, security and sustainability. AI will be deeply embedded with specialized processors for demanding workloads, while autonomous agents could automate cloud operations and incident response.
Edge computing will process latency-sensitive tasks closer to data sources, serverless architectures will expand for event-driven applications, and zero-trust security models will continuously verify users and workloads.
Data sovereignty is emerging as critical for regulated industries, with Gartner forecasting USD 80 billion in sovereign-cloud spending by 2026.
Sustainability metrics and energy efficiency will increasingly influence infrastructure decisions as AI data centers demand more compute.
Misconfiguration patterns barely overlap across the three major clouds, fragmenting how security teams understand risk.
Analysis of misconfiguration data from 3,000 organizations over the 12 months to July 2026 finds that the issues dominating AWS look almost nothing like those on Google Cloud, with Azure different again.
On AWS the most widespread findings are S3 not enforcing HTTPS at 87%, permissive ingress to sensitive ports at 84%, overly permissive network ACLs at 83% and IAM policies allowing privilege escalation at 83%.
Azure is led by storage account key rotation not enabled at 67%, access keys enabled at 66%, public network access at 61% and Entra users without MFA at 55%. The practical concern is that divergence fragments risk understanding and slows remediation.