Page 154 - Red Hat PR REPORT - OCTOBER 2025

10/7/25, 1:53 PM Latest News
long-term control, compliance, and differentiation. Therefore, when it comes to long-term business value, governance, and security, the real question is not simply how to use the
technology, but who owns it. This is particularly vital for the Middle East, where digital transformation is rapidly accelerating, fuelled by ambitious government-led initiatives aimed at
positioning the region as a global innovation hub.

From Hype to Strategy

The Middle East is investing heavily in AI. According to PwC, the region stands to gain over USD320 billion from AI by 2030, with Saudi Arabia and the UAE leading the charge.
National strategies - from Abu Dhabi’s ambition to be the world’s first AI-native government in the next two years under Abu Dhabi Government Digital Strategy 2025-2027 to Saudi
Arabia’s Vision 2030 - are accelerating digital transformation across all sectors.
Despite this momentum, many businesses in the region are still in the early to mid-stages of adopting AI. Many executive teams eager in the Middle East are eager to embrace AI, but
internal IT departments often lack the tools, frameworks, and talent to do so securely and effectively. As a result, organizations increasingly rely on third-party vendors, exposing
themselves to risks such as shadow IT, data breaches, and a loss of strategic autonomy.

Understanding and Bridging the Gap

Public or hosted AI models offer speed and convenience, but they come with significant trade-offs. These uses proprietary Large Language Models (LLMs) today such as ChatGPT,,
Gemini, Claude, or are trained on the open internet, not enterprise-specific datasets.
While impressive in general tasks, they often falter when applied to highly specialized or regulated use cases. More importantly, uploading confidential or sensitive business data into
third-party systems raises serious privacy, compliance, and data sovereignty concerns - particularly in sectors like government, healthcare, banking, and finance.They come with
significant trade-offs:
1. Data Security & IP Leakage: Sending proprietary data to third-party models risks exposing sensitive information. Public models are trained on general datasets — not enterprise-
specific data — and offer little visibility or control over where data flows or how it’s used.

2. Compliance & Governance Challenges: Regulatory environments like GDPR, HIPAA, and industry-specific mandates require strict controls over data location, processing, and
access. Hosted models often operate in black-box environments that can’t satisfy audit and compliance requirements.

3. Lack of Personalization: Foundation models are trained on broad, public datasets. Without access to internal knowledge bases, customer records, or domain-specific data, their
responses remain generic — limiting their usefulness in enterprise workflows.
That’s why to mitigate these risks and future-proof their operations, companies must recognize the need to take ownership of their AI infrastructure. They must shift their position from
‘consuming tools’ to ‘building solutions.’ By developing in-house solutions, they can maintain full control over their data, customize AI systems to reflect business-specific contexts, and
ensure scalability and governance from the ground up.
The RoleOpen-Source Models and Private AI

For organizations wondering where to begin, open source AI models provide a powerful and practical foundation. There has been an "explosion of capability" from open-source
technologies in the three two years - models like Llama, Mistral, and Phi-2, Qwen, DeepSeek, Granite and many others are outperforming proprietary models in enterprise use cases.
Unlike proprietary platforms, open source models offer transparency, flexibility, and a lower barrier to entry. This ecosystem offers a rich foundation of tools, models, and frameworks
that empower organizations to build AI solutions tailored to their specific needs - without the constraints of vendor lock-in or proprietary architectures. The flexibility of open source
enables enterprises to experiment, iterate, and scale AI solutions in ways that are cost-effective,and aligned with internal governance and compliance mandates.

Private AI refers to the development and deployment of AI services within an organization’s controlled environment — whether on-premises, in a private cloud, or across a hybrid
infrastructure using open source models.
• Small, optimized, purpose-built models that reduce cost and improve performance.
• Model alignment with enterprise data using techniques like Retrieval-Augmented Generation (RAG), fine-tuning.
• Enterprise-grade deployment at scale across hybrid cloud environments.
By treating AI as a service just like, compute, storage and other enterprise platforms — internal teams consume AI securely and efficiently while IT retains full control. Running AI
privately isn’t just about control — it’s a smarter business move:
• Predictable Economics: GPU-as-a-Service models allow teams to share resources, manage quotas, and prevent idle time, all while avoiding the unpredictability of per-token cloud
pricing.
• Elimination of Shadow IT: By delivering centralized AI services internally, Red Hat helps IT teams reduce fragmented tooling and unauthorized use of external APIs.
• Faster Innovation, Lower Risk: With Private AI, developers and data scientists can prototype, fine-tune, and deploy AI applications with enterprise data — while staying compliant with
internal policies and regulatory requirements.
• Unified AI Service Delivery: Through Models-as-a-Service, Red Hat empowers organizations to distribute AI capabilities to all departments — from customer support to engineering —
without sacrificing oversight or governance.
By embracing Private AI, CIOs can unlock the full value of their enterprise data, reduce cost and risk, and deliver smarter, more personalized AI experiences. In the race to scale AI
responsibly, the future belongs to those who build their own runway.

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