The cloud stories enterprise IT leaders actually read.
Volume 343, Issue 2 · 6 articles
Misconfigured storage, stolen credentials and excessive permissions are among seven recurring causes of cloud breaches.
Kratikal notes that moving to the cloud doesn't make services secure, and most cloud breaches stem from the same recurring lapses.
The seven patterns include misconfigured storage buckets with public access, weak policies or missing encryption; stolen or exposed credentials such as API keys, shared admin accounts and accounts without MFA; and excessive permissions that let attackers go deeper once inside.
Other patterns cover vulnerable workloads, insecure APIs, poor logging and third-party exposure.
Studying real incidents helps organizations spot the same threats in their own environment, and the article recommends regular cloud security assessments and access reviews.
A primer on cloud architecture, service and deployment models, security and migration as AI reshapes cloud choices.
Analytics Insight's guide explains how cloud computing has become the foundation for AI, analytics and enterprise software, noting that Gartner forecasts Indian public cloud spending will reach $17.5 billion in 2026, up 28.1%.
It covers architecture components such as compute, storage, networking, identity and monitoring; VMs, containers and serverless; and how AI workloads drive demand for processing power and fast data access.
It also explains IaaS, PaaS and SaaS, public, private and hybrid deployment, security responsibilities and migration strategy.
The guide stresses that higher spend doesn't guarantee productivity without good design and cost management.
A successful multi-arch build does not mean a working ARM container; native dependencies are where migrations fail.
Adding a platform flag to docker buildx produces two separately built images behind a manifest list, and nothing checks that the arm64 variant actually works.
As a result, failures appear at runtime, often first in production.
Common culprits include hardcoded x86 binary downloads in RUN lines, incompatible shared libraries and architecture-specific Java packages that cause crashes or silent slowdowns.
Before moving workloads for compute savings, the author recommends inspecting what is behind the tag, auditing dependencies, using native ARM CI runners and adopting architecture-aware Kubernetes scheduling.
A practical guide to choosing migration strategies, controlling cloud costs and measuring results after the move.
Analytics Insight cites Flexera's 2026 State of the Cloud report, which found organizations estimate 29% of cloud infrastructure spend is wasted, the first rise in five years, driven partly by AI workloads.
The guide covers rehosting, replatforming, refactoring and retiring applications.
It warns about idle services, overprovisioning, data transfer fees, hidden data dependencies and access rights that don't fit the new environment.
Best practices include starting with a low-risk pilot, setting clear downtime and recovery objectives, naming one migration manager, monitoring costs monthly and judging success against predefined criteria.
Independently deployed microservices make static mocks drift from reality, so service virtualization must refresh automatically.
Sancharini Panda explains that service virtualization tools were built for predictable release schedules, but cloud native services deploy independently through separate pipelines.
Each upstream deployment can quietly make a virtual service inaccurate while downstream tests keep passing.
The article compares traffic-based recording, API contract testing such as Pact, and real containerized dependencies via Testcontainers, along with tools like WireMock and Keploy.
Requirements for cloud native testing include automatic configuration refreshes when upstream services change, clear behavioral diffs and language-independent interception, for example using eBPF.
Five hyperscalers plan $660 to $690 billion in 2026 capex, nearly double 2025, mostly on AI compute and power.
Microsoft, Alphabet, Amazon, Meta and Oracle have committed $660 billion to $690 billion in 2026 capital spending.
Amazon leads at a projected $200 billion, Alphabet plans $175 to $185 billion, Microsoft $120 billion or more, Meta $115 to $135 billion and Oracle about $50 billion.
Roughly two-thirds goes to GPUs and custom accelerators and one-third to facilities, power, cooling and networking, with the power share rising.
David Linthicum assesses what this demand curve means for enterprise cloud buyers.