Maria Bledsoe discusses Forrester Research findings on how Microsoft Azure enables enterprises to securely and efficiently scale generative AI, highlighting the platform’s security, compliance, and data management capabilities.

Building Secure, Scalable AI in the Cloud with Microsoft Azure

Author: Maria Bledsoe

Forrester Research explores how organizations leverage Microsoft Azure to scale generative AI securely, addressing infrastructure, security, and compliance challenges in order to unlock substantial business value.

Introduction

Generative AI is fundamentally transforming the operational landscape for modern enterprises. As AI becomes crucial for driving productivity, innovation, and business impact, organizations face mounting pressure to implement AI solutions at scale swiftly and securely—or risk falling behind the competition.

The Enterprise Challenge: Scaling Generative AI

Large-scale deployment of AI, particularly generative AI, is challenging:

  • Complex Infrastructure Needs: Like rapid urban expansion necessitates modern transport, enterprise AI requires robust and flexible cloud infrastructure for successful scaling.
  • Obsolete On-Premises Systems: On-prem AI environments prove costly, hard to maintain, and ill-equipped for the agility, scalability, and performance AI initiatives demand.
  • Key Reported Barriers:
    • Infrastructure limitations
    • Talent shortages, especially in AI/ML and security
    • Integration difficulties
    • Limited network bandwidth and fragmented data sources

Survey insights from Forrester:

  • 72% of organizations reported that migrating to Azure for AI-readiness was necessary or eased AI enablement.
  • 65% agreed cloud-based generative AI best meets organizational goals when compared to on-premises alternatives.

Security, Compliance, and Adoption Concerns

Cloud migration remains critical for organizations looking to implement generative AI securely. Decision-makers identified their main concerns as:

  1. Data privacy risks (especially with proliferation of AI-generated content)
  2. Lack of generative AI security best practice expertise
  3. Compliance complexity (due to evolving regulations)
  4. Shadow IT risks (unauthorized tool/app use introduces vulnerabilities)

Azure, with its built-in security and compliance controls, offers a strategic advantage for addressing these challenges, enabling safe, scalable deployments.

Azure: The Enterprise Choice for AI-Readiness

Forrester’s findings illustrate that organizations migrating to Azure realize significant reductions in barriers to adopting enterprise AI:

  • 75% cited migration as necessary or greatly beneficial for generative AI adoption.

Azure’s differentiators for responsible, secure AI at scale include:

1. Enterprise-Grade Security and Compliance

  • Tools: Microsoft Defender for Cloud, Microsoft Sentinel, Azure Key Vault, IaaS security controls
  • Approach: Multi-layered protection across the entire AI lifecycle, ensuring data privacy, lifecycle assurance, and threat management
  • Commitment: Adherence to responsible AI standards—ethical, transparent, and compliant AI practices

2. Scalable Cloud Infrastructure

  • Supports rapid project launches, sidesteps on-prem limits, and delivers mission-critical tools for AI and ML
  • Businesses avoid siloed data, legacy security gaps, and infrastructure bottlenecks for faster, safer innovation

3. Unified Data and AI Management

  • Solutions: Microsoft Fabric, Azure Synapse Analytics, Azure Databricks
  • Benefits:
    • Centralized and well-governed data foundation
    • Improved compliance and optimized AI model performance
    • Seamless data integration beyond legacy constraints

4. Accelerated Innovation

  • Flexible environment: Enables fast experimentation and adaptation
  • Survey finding: Azure users report more than twice the confidence in building/refining AI/ML applications than those using on-prem systems
  • Freed resources: Focus shifts from infrastructure maintenance to AI upskilling, innovation, and business transformation

Azure: Impact on AI-Driven Business Value

Organizations adopting Azure for AI reap multiple benefits:

  • Optimized Resource Allocation: IT focuses on developing AI use cases, not infrastructure management
  • Accelerated Time to Value: Developers, data scientists, and engineers deliver high-quality models and solutions faster
  • Enhanced Security & Compliance: Built-in tools reduce breach risk and support dynamic regulatory needs
  • Superior Application Performance: Cloud-scale resources enable improved AI model and application output

The Bottom Line: Innovation Without Compromise

As organizations embrace digital transformation, Azure provides a secure, scalable cloud foundation for responsible generative AI deployment. With a holistic approach—spanning advanced infrastructure, best-in-class security, responsible AI, and unified data management—Azure enables enterprises to fully leverage the business potential of generative AI for sustained innovation and growth.

Read the full Forrester study for more insights on Azure-powered, secure AI deployment.

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