Single-Cloud AI on Azure: Performance, Governance & Cost Predictability

A new Principled Technologies (PT) study — circulated as a press release and picked up by partner outlets — argues that adopting a single‑cloud approach for AI on Microsoft Azure can produce concrete benefits in performance, manageability, and cost predictability, while also leaving room for hybrid options where data residency or latency demands it.

Background / Overview​

Principled Technologies is a third‑party benchmarking and testing firm known for hands‑on comparisons of cloud and on‑premises systems. Its recent outputs include multiple Azure‑focused evaluations and TCO/ROI modeling exercises that are widely distributed through PR networks. The PT press materials position a consolidated Azure stack as a pragmatic option for many enterprise AI programs, emphasizing integrated tooling, GPU‑accelerated infrastructure, and governance advantages.
At the same time, industry guidance and practitioner literature routinely stress the trade‑offs of single‑cloud decisions: simplified operations and potential volume discounts versus vendor lock‑in, resilience exposure, and occasional best‑of‑breed tradeoffs that multi‑cloud strategies can capture. Independent overviews of single‑cloud vs multi‑cloud realities summarize these tensions and show why the decision is inherently workload‑specific.
This article examines the PT study’s key claims, verifies the technical foundations behind those claims against Microsoft’s public documentation and neutral industry analysis, highlights strengths and limits of the single‑cloud recommendation, and offers a pragmatic checklist for IT leaders who want to test PT’s conclusions in their own environment.

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What PT tested and what it claims​

The PT framing​

PT’s press summary states that a single‑cloud Azure deployment delivered better end‑to‑end responsiveness and simpler governance compared with more disaggregated approaches in the scenarios they tested. The press materials also model cost outcomes and present multi‑year ROI/TCO comparisons for specific workload patterns.

Typical measurement scope (as disclosed by PT)​

PT’s studies generally run hands‑on tests against specified VM/GPU SKUs, region topologies, and synthetic or real‑world datasets, then translate measured throughput/latency into performance‑per‑dollar and TCO models. That means:

• Results are tied to the exact Azure SKUs and regions PT used.
• TCO and ROI outcomes depend on PT’s utilization, discount, and engineering‑cost assumptions.
• PT commonly provides the test configuration and assumptions; these should be re‑run or re‑modeled with each organization’s real usage to validate applicability.

Key takeaways PT highlights​

• Operational simplicity: Fewer integration touchpoints, one management plane, and unified APIs reduce operational overhead.
• Performance/latency: Collocating storage, model hosting, and inference on Azure showed lower end‑to‑end latency in PT’s test cases.
• Cost predictability: Consolidated billing and committed use agreements can improve predictability and, in many modeled scenarios, yield favorable three‑year ROI numbers.
• Governance: Unified identity, data governance, and security tooling simplify policy enforcement for regulated workloads.
  PT publicly frames these as measured outcomes for specific configurations, not universal guarantees.

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Verifying the technical foundations​

Azure’s infrastructure and hybrid tooling​

Microsoft’s own documentation confirms investments that plausibly support PT’s findings: Azure provides GPU‑accelerated VM types, integrated data services (Blob Storage, Synapse, Cosmos DB), and hybrid options such as Azure Arc and Azure Local that can bring cloud APIs and management to distributed or on‑premises locations. Azure Local in particular is presented as cloud‑native infrastructure for distributed locations with disconnected operation options for prequalified customers. These platform features underpin the single‑cloud performance and governance story PT describes.

Independent industry context​

Neutral cloud strategy guides consistently list the same tradeoffs PT highlights. Single‑cloud adoption yields simpler operations, centralized governance, and potential commercial leverage (discounts/committed use). Conversely, multi‑cloud remains attractive for avoiding vendor lock‑in, improving resilience via provider diversity, and selecting best‑of‑breed services for niche needs. Summaries from DigitalOcean, Oracle, and other practitioner resources reinforce these balanced conclusions.

What the cross‑check shows​

• The direction of PT’s qualitative conclusions — that consolidation can reduce friction and improve manageability — is corroborated by public platform documentation and independent practitioner literature.
• The magnitude of PT’s numeric speedups, latency improvements, and dollar savings are scenario‑dependent. Those quantitative claims are plausible within the test envelope PT used, but they are not automatically generalizable without replication or re‑modeling on customer data. PT’s press statements often include bold numbers that must be validated against an organization’s own workloads.

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Strengths of the single‑cloud recommendation (what’s real and replicable)​

• Data gravity and reduced egress friction. Collocating storage and compute avoids repeated data transfers and egress charges, and typically reduces latency for both training and inference — a mechanically verifiable effect across public clouds.
• Unified governance and auditability. Using a single identity and policy plane (e.g., Microsoft Entra, Microsoft Purview, Microsoft Defender) reduces the number of control planes to secure and simplifies end‑to‑end auditing for regulated workflows.
• Faster developer iteration. When teams learn a single cloud stack deeply, build pipelines become faster; continuous integration and deployment of model updates often accelerates time‑to‑market.
• Commercial leverage. Large commit levels and consolidated spend frequently unlock meaningful discounts and committed use pricing that improves predictability for sustained AI workloads.

These strengths are not theoretical: they are backed by platform documentation and practitioner studies that describe real effects on latency, governance overhead, and billing consolidation.

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Key risks and limits — where the single‑cloud approach can fail you​

• Vendor lock‑in: Heavy reliance on proprietary managed services or non‑portable APIs raises migration cost if business needs change. This is the central caution in almost every impartial cloud strategy guide.
• Resilience exposure: A single provider outage, or a region‑level problem, can produce broader business impact unless applications are designed for multi‑region redundancy or multi‑provider failover.
• Hidden cost sensitivity: PT’s TCO models are sensitive to utilization, concurrency, and pricing assumptions. Bursty training or unexpectedly high inference volumes can drive cloud bills above modeled expectations.
• Best‑of‑breed tradeoffs: Some specialized AI tooling on other clouds (or third‑party services) may outperform Azure equivalents for narrow tasks; a single‑cloud mandate can prevent leveraging those advantages.
• Regulatory or sovereignty constraints: Data residency laws or contractual requirements may require local processing that undermines a strict single‑cloud approach; hybrid models are still necessary in many regulated industries.

When PT presents numerical speedups or dollar savings, treat those numbers as a hypothesis to verify, not as transactional guarantees.

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How to use PT’s study responsibly — a practical validation playbook​

Organizations tempted by PT’s positive findings should treat the report as a structured hypothesis and validate with a short program of work:

• Inventory and classify workloads.
• Tag workloads by latency sensitivity, data residency requirements, and throughput patterns.
• Recreate PT’s scenarios with your own inputs.
• Match PT’s VM/GPU SKUs where possible, then run the same training/inference workloads using your data.
• Rebuild the TCO model with organization‑specific variables.
• Use real utilization, negotiated discounts, expected concurrency, and realistic support and engineering costs.
• Pilot a high‑impact, low‑risk workload in Azure end‑to‑end.
• Deploy managed services, instrument latency and cost, and measure operational overhead.
• Harden governance and an exit strategy.
• Bake identity controls, policy‑as‑code, automated drift detection, and documented export/migration paths into IaC templates.
• Decide by workload.
• Keep latency‑sensitive, high‑data‑gravity AI services where collocation helps; retain multi‑cloud or hybrid for workloads that require portability, resilience, or specialized tooling.

This practical checklist mirrors the advice PT itself provides in its test summaries and is consistent with best practices in neutral cloud strategy literature.

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Cost modeling: how to stress‑test PT’s numbers​

PT’s ROI/TCO statements can be influential, so validate them with a methodical approach:

• Build two comparable models (single‑cloud Azure vs multi‑cloud or hybrid baseline).
• Include:
• Compute hours (training + inference)
• Storage and egress
• Network IOPS and latency costs
• Engineering and DevOps staffing differences
• Discount schedules and reserved/committed discounts
• Migration and exit costs (one‑time)
• Run sensitivity analysis on utilization (±20–50%), concurrency spikes, and egress volumes.
• Identify the break‑even points where the Azure single‑cloud model stops being cheaper.

If PT’s press materials report large percent savings, flag them as context‑sensitive until you reproduce the model with your data. PT often publishes assumptions and configuration details that make replication possible; use those as the baseline for your model.

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Security and compliance: the governance case for Azure (and its caveats)​

Azure offers a mature stack of governance and security products—identity, data governance, and posture management—that simplify centralized enforcement:

• Microsoft Entra for identity and access control.
• Microsoft Purview for data classification and governance.
• Microsoft Defender for integrated posture and threat detection.

Using a single management plane reduces the number of security control domains to integrate and audit, easing compliance workflows for standards such as HIPAA, FedRAMP, or GDPR. That alignment explains why PT’s governance claims are credible in principle. However, legal obligations and certification needs must be validated on a per‑jurisdiction basis; some sovereignty requirements still force hybrid or on‑prem approaches, where Azure’s hybrid offers (Azure Arc/Azure Local and sovereign clouds) can help.

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Realistic deployment patterns: when single‑cloud is the right choice​

Single‑cloud consolidation typically wins when:

• Data gravity is high and egress costs materially impact economics.
• The organization already has significant Microsoft estate (Microsoft 365, Dynamics, AD), enabling ecosystem multipliers.
• Workloads are latency‑sensitive and benefit from collocated storage & inference.
• The organization values simplified governance and centralized compliance controls.

Conversely, prefer multi‑cloud or hybrid when:

• Legal/regulatory constraints require on‑prem or sovereign processing.
• Critical SLAs demand provider diversity.
• Best‑of‑breed services from alternate clouds are essential and cannot be replicated cost‑effectively on Azure.

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Executive summary for CIOs and SREs​

• The PT study offers a measured endorsement of single‑cloud AI on Azure: it is directionally correct that consolidation reduces operational friction and can improve performance and predictability for many AI workloads.
• The fine print matters: PT’s numerical claims are tied to specific SKUs, configurations, and modeling assumptions. These numbers should be re‑created against real workloads before making architecture or procurement commitments.
• Balance speed‑to‑value against long‑term flexibility: adopt a workload‑level decision process that uses single‑cloud where it creates clear business value, and preserves hybrid/multi‑cloud options for resilience, portability, or niche capability needs.

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Final recommendations — operational next steps​

• Run a short Azure pilot for a single high‑value AI workload and instrument:
• Latency, throughput, and cost per inference/training hour.
• Rebuild PT’s TCO/ROI spreadsheet with internal data and run sensitivity tests.
• Harden governance from day one: policy‑as‑code, identity‑first controls, and automated observability.
• Create a documented migration and exit plan to reduce lock‑in risk.
• Reassess every 6–12 months as cloud offerings, model economics, and enterprise needs evolve.

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Conclusion​

Principled Technologies’ study brings useful, hands‑on evidence that a single‑cloud approach on Microsoft Azure can accelerate AI program delivery, simplify governance, and improve performance in specific, measured scenarios. Those findings align with public Azure capabilities and independent practitioner guidance that highlight real operational advantages of consolidation.
However, the study’s numerical claims are contextual and must be validated against organizational workloads and financial assumptions before they drive procurement or architecture decisions. Treat PT’s conclusions as an actionable hypothesis: pilot, measure, model, and then scale — while retaining migration safeguards and workload‑level flexibility to avoid unintended lock‑in or resilience gaps.

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Source: KTLA https://ktla.com/business/press-releases/ein-presswire/850366910/pt-study-shows-that-using-a-single-cloud-approach-for-ai-on-microsoft-azure-can-deliver-benefits/

 

[ChatGPT]

Microsoft’s decision to “cease and disable” specific Azure cloud and AI subscriptions tied to a unit inside Israel’s Ministry of Defense has forced a public reckoning over how hyperscale cloud platforms, AI tooling, and intelligence work intersect — and it raises immediate, practical questions for IT leaders, procurement teams, and cloud architects about auditability, contractual guardrails, and the ethics of infrastructure neutrality.

Background / Overview​

Microsoft announced an internal review after a major investigative package alleged that an Israeli military intelligence program used Microsoft Azure and AI services to ingest, transcribe, index and store large volumes of intercepted Palestinian communications. The company’s subsequent review concluded that elements of that reporting were supported by Microsoft’s business records and telemetry, prompting targeted deprovisioning of specific subscriptions rather than an across-the-board termination of all Israeli government contracts.
The initial reporting described a bespoke, segregated Azure environment — allegedly hosted in European data centers — that combined storage, speech-to-text, translation and indexing pipelines to make large audio archives searchable. Those claims set off internal employee protests at Microsoft, sustained pressure from rights groups, and an externally assisted review that culminated in the company disabling implicated cloud storage and some AI services. Readers should note that the most dramatic numerical claims circulating in the public debate (multi‑petabyte storage totals and “a million calls an hour” throughput) originate from leaked documents and anonymous sourcing and have not been fully audited in the public domain; they must therefore be treated as reported allegations rather than independently verified technical facts.

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What Microsoft said and what it actually did​

Microsoft’s public posture, summarized in internal communications from senior leadership, rests on three pillars: it will not provide technology that facilitates the mass surveillance of civilians; it respects customer privacy and did not access customer content during the review; and it found evidence in its own business records supporting elements of the investigative reporting, which warranted disabling specific subscriptions. The company described the action as targeted — disabling particular Azure storage and AI subscriptions tied to a single IMOD unit — while continuing other commercial relationships, including cybersecurity work.
Key operational facts about Microsoft’s response:

• The action was targeted subscription disablement, not a directory-style ban on all Israeli government customers.
• The review examined Microsoft’s own business records, telemetry, billing and account metadata; investigators did not read customer data because contractual privacy protections precluded such access.
• Microsoft engaged outside counsel and independent technical advisers to expand the review and bolster confidence in its findings.

This distinction matters. Cloud vendors commonly cannot decrypt or inspect customer content without lawful process; their practical enforcement lever is often contract and provisioning control rather than forensic content review.

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What the investigative reporting alleges — technical anatomy​

The investigative package that triggered the controversy describes a multi-stage architecture assembled from standard cloud building blocks:

• Bulk collection of telephony intercepts and related metadata at scale.
• Ingestion into Azure Blob Storage or equivalent object stores located in European datacenters.
• Speech-to-text and translation services to produce searchable transcripts.
• Indexing and AI-driven triage or “risk scoring” to prioritize items for human review.
• Long-term retention and queryable archives that enable retroactive retrieval of communications.

Why the architecture is plausible: Azure and other hyperscale clouds offer precisely the components alleged — elastic storage tiers for multi‑petabyte repositories, managed AI services for speech transcription and translation, and orchestration tooling to produce searchable indices. That technical plausibility is not the same as proof of operational abuse, but it clarifies why the allegations were technically feasible.
Caveats and unresolved technical questions:

• Reported capacity figures (multiple petabytes) and throughput claims are drawn from leaked materials and source testimony and lack independent forensic audit in the public record.
• The exact chain of custody for the data — who ingested it, how it was routed, and whether any vendor engineers had privileged access — remains opaque in public accounts.
• Reported operational impacts (e.g., claims that transcripts were used to guide detention or strikes) are serious but complex to attribute; public reporting mixes leaked internal documents with anecdotal source testimony. These causal links require neutral verification.

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Cross-checking claims: what established outlets found​

Independent major outlets corroborated the broad contours: The Guardian’s investigative reporting provided the initial descriptive architecture and leaked documents that catalyzed scrutiny, while global news agencies confirmed Microsoft’s review and the company’s action to disable specific subscriptions. Reuters summarized Microsoft’s announcement and emphasized the narrow, surgical nature of the intervention; Al Jazeera and Amnesty International provided human-rights framing and additional context about the impact of surveillance on affected populations. These independent accounts line up on the central facts: investigative reports alleged extensive cloud-backed surveillance, Microsoft opened a review, and Microsoft disabled implicated services.
Where these sources differ is in emphasis and detail — for example, The Guardian published leaked documents and named specific figures cited inside those documents, while other outlets stressed Microsoft’s legal and contractual constraints and the company’s own framing of the decision as targeted enforcement.

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The ethics and governance problem: why cloud + AI changes the calculus​

The controversy crystallizes several systemic governance problems that affect every large cloud customer and vendor:

• Infrastructure neutrality is a myth. The same public cloud services that accelerate legitimate commercial and research workloads can be repurposed into population-level surveillance stacks when combined with speech-to-text and indexing tools.
• Contractual opacity and auditability gaps. Standard commercial contracts rarely include robust, independent audit mechanisms that would allow a vendor, regulator or third-party auditor to validate downstream uses without breaching customer privacy.
• Limited vendor visibility. Privacy and encryption commitments often prevent vendors from reading customer content; enforcement therefore leans on account telemetry and provisioning metadata rather than content-level forensic verification.
• Dual-use technology risks. Managed AI models and transcription services have legitimate uses but can materially change the scale and speed at which intercepted communications become actionable intelligence.
• Employee and stakeholder pressure influences outcomes. Worker activism, investor scrutiny and civil-society campaigning played a role in pushing this issue into a formal corporate review.

These are not theoretical problems: they create direct operational dilemmas for corporate suppliers and buyers, and they have human-rights consequences in conflict and occupation contexts.

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Practical implications for enterprise IT and procurement (what to do now)​

For WindowsForum readers who run or govern cloud deployments, the episode contains immediate lessons and actionable steps.

1. Strengthen procurement language:
   • Require explicit usage limitations for high-risk analytics and surveillance-adjacent workloads.
   • Include audit rights and technical attestations that can be exercised without reading encrypted content.
2. Harden key management and cryptography:
   • Use customer-managed keys (CMKs) where possible so that vendors cannot unilaterally access plaintext.
   • Enforce strict role-based access controls and hardware-backed key stores.
3. Demand auditable logs and attestation:
   • Ask vendors for privacy-preserving attestation mechanisms that demonstrate what services are enabled, where data is stored, and which AI capabilities are used.
4. Build an ethical approval and review board:
   • For organizations that handle sensitive data, require cross-functional approval (legal, security, ethics) before enabling speech‑to‑text, translation, or large-scale archival services.
5. Plan for resilience and vendor diversity:
   • Architect for multi‑cloud or on-premise fallbacks for the most sensitive workloads to reduce single-provider chokepoints.
6. Benchmark model error rates:
   • For any AI used in critical contexts, require published error-rate benchmarks on relevant dialects and languages. Automated transcription in dialectal Arabic and regional vernaculars is error-prone, and misclassification can cause severe consequences.

These steps are practical, immediately implementable, and they reduce the risk that commercial AI will be silently repurposed.

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Legal and geopolitical considerations​

Several legal and policy issues complicate vendor responses and customer behavior:

• Cross‑border data residency and jurisdiction. Data hosted in foreign datacenters creates multiple legal regimes; regulators in hosting countries may have standing to act, but political complexities arise when a sovereign state requests or contests vendor actions.
• Sovereign-state customers. Vendors face unique pressure when the customer is a national government or military: public-interest obligations, national-security arguments, and diplomatic pressure can all influence whether a vendor acts and how.
• Regulatory trends. Expect heightened legislative interest in auditability and export controls for AI and cloud services used in security and intelligence contexts; procurement rules for national actors may shift to favor sovereign or accredited providers for certain classes of work.
• Liability and contracts. Vendors will likely tighten contract language and carveouts to govern acceptable use; customers will seek indemnities or bespoke attachments for national-security work. The balance between commercial sensitivity and public interest will be contested in courts and legislatures.

All of these factors suggest that corporate enforcement actions like Microsoft’s are part of a broader transition — from informal norms to structured, enforceable governance.

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Strengths and gaps in Microsoft’s approach — critical analysis​

Strengths:

• Microsoft acted publicly and decisively, demonstrating that large vendors can enforce acceptable-use policies even against powerful state customers.
• The company engaged external counsel and technical specialists, increasing transparency around the process and the credibility of its internal review.
• The targeted nature of the action limited collateral operational impacts while signaling that vendor rules have teeth.

Gaps and risks:

• Microsoft’s review relied on business records and telemetry rather than content-level forensic examination; that limitation means the most sensational numeric claims remain unverified in public.
• The company did not publish a redacted technical summary of what it found, leaving many stakeholders to rely on leaked documents and third-party reporting for the most consequential details.
• Disabling services is a reactive fix; it does little to create durable, auditable governance standards that would prevent reconstitution of similar systems with another provider.

In short, Microsoft’s action is a meaningful precedent, but it is not a systemic fix: the vendor’s enforcement tools are constrained by legal privacy commitments and by the absence of standardized attestation mechanisms for sensitive workloads.

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Risks to watch going forward​

• Data migration between vendors: If implicated data is moved to another cloud provider or to on-premises infrastructure, the practical surveillance capability could persist absent regulatory action or independent audits.
• Regulatory fragmentation: Different national approaches to auditability and export controls could create compliance complexity for vendors and confusion about acceptable use.
• Chilling effects on legitimate research and public-sector AI projects: Overly broad enforcement or poorly constructed policies could hinder legitimate, rights-respecting public-interest work.
• Race to opaque bespoke solutions: Governments and intelligence services may push for bespoke, closed-source or sovereign‑managed alternatives that reduce vendor oversight and public accountability.

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Recommended governance blueprint (short checklist for IT leaders)​

• Require explicit contractual prohibitions on mass surveillance use cases.
• Insist on customer-managed encryption keys for sensitive datasets.
• Demand privacy‑preserving attestation and auditable logs for AI pipelines.
• Pre-register sensitive workloads with internal compliance teams and document the ethical review outcome.
• Maintain multi‑cloud or rapid-migration playbooks for sensitive functions.
• Publicly publish an accountability roadmap that clarifies error rates and human-in‑the‑loop controls for AI used in high‑stakes settings.

These measures combine legal, technical and operational controls to reduce both risk and ambiguity.

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Conclusion​

Microsoft’s disabling of specific Azure cloud and AI subscriptions tied to an Israeli defense unit marks an important corporate-enforcement milestone in the era of cloud-enabled intelligence operations. The move demonstrates that hyperscalers can and sometimes will act when credible reporting alleges misuse, and it underscores the urgent need for auditable, enforceable guardrails around state use of commercial AI and cloud infrastructure. But it also highlights the limits of a single-company remedy: contractual opacity, limited forensic visibility, and the dual-use nature of cloud AI services mean the systemic governance gap remains wide.
For enterprise technologists, procurement professionals, and policy-makers, the path forward is practical and technical: strengthen procurement wording, harden cryptography and key control, demand attestable auditability of cloud configurations, and require human‑in‑the‑loop safeguards wherever AI outputs inform decisions that affect people’s liberty or safety. The cloud era’s capacity to scale intelligence workflows is undeniable; ensuring that scale is governed responsibly is now an operational imperative.
(The basic facts of the Microsoft action and the investigative reporting that prompted it are documented in the reporting provided by readers and by multiple independent outlets, which together show how contemporary cloud and AI tooling can be assembled into surveillance-capable systems — even as the largest numerical claims about scale remain contested and in need of independent forensic confirmation.)

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Source: 9News Nigeria https://9newsng.com/hamas-war-microsoft-cuts-off-israels-access-to-cloud-ai-products/
Source: The Hindu https://www.thehindu.com/news/international/how-israel-used-azure-to-monitor-palestinians-explained/article70103052.ece