Building secure, scalable AI in the cloud with Microsoft Azure
According to Forrester Research, Azure equips businesses to securely scale generative AI, tackling infrastructure and compliance issues while unlocking genuine business benefits.
Generative AI is a game-changer, reshaping how companies work today. It’s swiftly becoming vital for enhancing productivity, sparking innovation, and creating a significant impact. The stakes are high: businesses need to act quickly to adopt AI effectively and realise its full potential or risk lagging behind.
Implementing generative AI across an enterprise securely and effectively isn’t straightforward. Think of generative AI as rocket fuel: it has the power to elevate businesses, but only with the right infrastructure and controls. To speed up their journeys safely and methodically, many organisations are turning to Microsoft Azure as their central hub. By leveraging Azure’s robust cloud infrastructure and top-notch security features, teams can successfully build, launch, and enhance generative AI initiatives, yielding real outcomes.
To gain insights into how companies are readying themselves for AI, we teamed up with Forrester Consulting to survey Azure customers. The resulting 2024 Forrester Total Economic ImpactTM study reveals how businesses are gearing up for AI, the obstacles associated with adopting generative AI in the cloud, and how Azure’s scalable infrastructure and built-in security enable enterprises to deploy AI with confidence.
Challenges with Scaling Generative AI On-Premises
Scaling generative AI is akin to designing transportation systems in an expanding city. Just as urban growth necessitates modern transport infrastructure, AI leaders understand that meaningful AI implementation needs a powerful, flexible cloud foundation capable of meeting future demands. They recognise that cloud agility is crucial to achieving desired outcomes.
- A notable 72% of respondents reported that moving to Azure for AI readiness was essential or significantly lowered the barriers to enabling AI.
- 65% of business leaders agreed that deploying generative AI in the cloud would help meet their objectives by avoiding the restrictions linked to on-premises deployments.
Companies that rely primarily on on-premises systems for their generative AI tasks face considerable hurdles. These systems often lack the flexibility of the cloud, making them feel outdated—similar to congested roads that are tough to maintain, costly to upgrade, and unable to meet current demands. Businesses trying to scale AI in these settings encounter a range of challenges, including limited infrastructure, a scarcity of specialised talent, and integration difficulties, all of which hamper innovation. Issues such as restricted network bandwidth and fragmented data further complicate the adoption process.
Ensuring safe deployment of generative AI is vital to safeguard sensitive data, uphold compliance, and mitigate risk. Surveyed leaders pinpointed four major concerns:
- Data privacy issues, particularly with the rise in AI-generated content.
- Lack of expertise regarding best practices for generative AI security.
- Complex compliance with constantly evolving regulations surrounding AI use and data protection.
- Risks of shadow IT, where users resort to unauthorised tools and applications, exposing firms to vulnerabilities.
To tackle these challenges, it’s crucial to partner with a cloud platform that offers integrated security and regulatory compliance. Migrating to the cloud provides the scalable infrastructure, integrated applications, and AI-ready data foundation needed for successful generative AI deployment. Respondents who transferred their AI workloads to Azure reported enhanced global reach, scalability, and flexibility—key advantages in today’s rapidly changing AI landscape.
Why Enterprises Choose Azure for AI Readiness
Infrastructure limitations hinder the scaling of generative AI. On-premises environments can inhibit performance, inflate costs, and slow down innovation. Our survey found that 75% of companies that moved to Azure for AI readiness felt this migration was either essential or significantly lowered barriers to generative AI adoption.
Despite clear benefits from cloud-based generative AI deployment, teams face challenges in adopting AI responsibly. Concerns about weaknesses, limited expertise in AI security, and data privacy issues are top of mind. Azure addresses these challenges with a comprehensive suite of frameworks that ensure the protection of generative AI workloads throughout their lifecycle—from development to execution.
Leaders surveyed highlighted Azure’s colocation strategy as a primary reason for choosing Azure for deploying generative AI. This strategy eliminates data silos and optimises performance. Tools like Microsoft Defender for Cloud and Microsoft Sentinel enhance security, making Azure a reliable platform for safe, enterprise-grade generative AI implementation.
Four Key Differentiators for Deploying Generative AI with Azure
1. Enterprise-Grade Security and Compliance Solutions
Security worries are a major obstacle when deploying generative AI in the cloud. Azure safeguards AI tasks from development to deployment. Its multi-layered approach helps organisations meet compliance standards and minimises risks throughout the entire AI lifecycle. Essential solutions like Microsoft Defender for Cloud, Microsoft Sentinel, and Azure Key Vault provide thorough protection for generative AI tasks, ensuring data privacy and robust threat management. Supported by Microsoft’s high-level security, compliance, and responsible AI pledges, Azure empowers teams to develop AI solutions that are not only powerful but also ethical, transparent, and compliant.
2. Scalable Cloud Infrastructure
With Azure’s cloud infrastructure, businesses can avoid the constraints of outdated systems, enabling them to launch AI projects quickly and securely. Azure offers an array of advanced AI and machine learning tools that are critical for generative AI success, allowing companies to break free from fragmented data, obsolete security frameworks, and infrastructure limitations. By deploying generative AI in the cloud, businesses can enhance innovation, streamline operations, and confidently create AI-driven solutions.
3. Unified Data and AI Management
For effective AI, a solid data foundation is imperative. Azure’s data management solutions—like Microsoft Fabric, Azure Synapse Analytics, and Azure Databricks—enable firms to centralise their data, enhance governance, and optimise the performance of AI models. By moving beyond the constraints of legacy on-premises environments, businesses gain seamless data access, improved compliance, and the scalability necessary to fuel AI innovation. With Azure, companies can utilise high-quality, well-managed data to drive more accurate and dependable AI results.
4. Faster Innovation
Utilising Azure allows resources to be redirected from maintaining infrastructure to driving AI innovation. The flexible and secure cloud environment of Azure enables organisations to experiment, adapt, and promote AI solutions with lower risks compared to traditional on-premises settings. Surveyed organizations using Azure reported significantly greater confidence in their potential to develop and refine AI and machine learning applications than those relying on on-prem environments. Key advantages include increased agility, reduced risk when modifying AI solutions, and an ability to reinvest infrastructural resources into AI upskilling and innovation.
The Business Impact of Secure Generative AI on Azure
Transitioning to Azure for AI deployment greatly enhances performance and operational efficiency. Key benefits include:
- Optimised resource allocation: Moving to the cloud liberates IT teams from managing infrastructure, allowing them to focus on strategic projects—like developing generative AI use cases—that yield meaningful results.
- Accelerated time to value: Azure AI services empower data scientists, AI and machine learning engineers, and developers to produce high-quality models more quickly.
- Enhanced security and compliance: Azure’s integrated security tools protect workloads, mitigate breach risks, and align with evolving compliance standards.
- Improved AI application performance: Deploying generative AI using Azure enhances application performance—fueling innovation and growth.
Innovation Without Compromise
As IT professionals and digital transformation leaders navigate the intricate landscape of AI adoption, Azure emerges as a trustworthy ally for achieving enterprise AI readiness. With cutting-edge infrastructure, safe and responsible AI measures, and integrated security features, Azure provides a secure and scalable platform for building and managing generative AI in the cloud. By choosing Azure, organisations can fully harness the potential of generative AI to foster innovation, accelerate growth, and create enduring business value.
