By Ahmad Shakora, Group Vice President South, Emerging Markets (META), Cloudera
In the high-pressure world of the professional kitchen, there is a sacred rule: mise en place. Translated literally as ‘everything in its place’, it is the rigorous process of gathering, washing, chopping, and organizing ingredients before the stove is even lit. Without it, the dinner rush becomes a disaster.
Organizations across the Middle East are facing their own dinner rush in the form of GenAI. Businesses are racing to implement AI solutions to drive efficiency, innovation, and economic growth as part of regional ambitions such as Saudi Vision 2030 and the UAE’s AI strategy, yet most are ignoring the mise en place. They are trying to cook at scale with a pantry that is disorganized, siloed, and, in many cases, unsafe.
This is the data readiness crisis. In a business landscape where data security and sovereignty are essential, addressing this crisis is more than a matter of speed; it is the absolute, foundational requirement for achieving Private AI.
The Precursor to Private AI: You Can’t Secure What You Can’t Find
For the last few years, data has rightly been seen as the new raw ingredient for AI. But raw data, like unwashed produce, is a liability until it is prepared.
Today, the goal has shifted. CEOs and business leaders aren’t just looking for AI; they are looking for AI that delivers high, differentiated value for their businesses. As such, they need a controlled environment where data privacy and security are maintained throughout the entire lifecycle. This has prompted the shift towards Private AI. They need to ensure their proprietary recipes (intellectual property) and ingredients (sensitive customer data) never leak into the public domain, and make sure they adhere to food hygiene regulations (regulatory compliance).
Crucially, you cannot have Private AI without data readiness. The industry reality is that for successful AI, your focus must shift: you need more data governance, not just more models. If you cannot explain your data landscape – where it comes from, who touched it, and where it lives – you cannot secure it. Data readiness is the foundation of the governing standards required to run AI safely. Without lineage, quality, and governance, your AI strategy isn’t just slow; it’s a compliance risk waiting to explode.
Box-out
Data readiness is the foundation of a secure and compliant AI strategy comprising of three key elements:
- Unified Governance: a universal governing standard that ensures consistent security, lineage, and quality policies follow data across all environments (on-prem and cloud).
- Data Security and/or Sovereignty: adopting Open Standards (like Apache Iceberg) to own data and prevent vendor lock-in.
- Compute-to-Data: the practice of bringing models and inference directly to governed data, reducing egress fees and security risks.
The Pop-Up Kitchen Trap vs. True Hybrid Flexibility
To bypass the mess in their main data centers, many leaders try to build ‘pop-up kitchens’ with siloed cloud environments where they move a small subset of data to run a specific pilot. While this might produce a quick appetizer, it fails at scale.
This approach introduces the cloud tax: sizable egress fees incurred every time you want to move data out of a specific provider’s walled garden. Worse, it breaks the security chain. When you move data out of your core environment to a proprietary model, you lose the metadata and context that make the data compliant. Or rather, you or your favorite consulting partner becomes responsible for replicating it.
The industry reality is hybrid. Some workloads belong in the cloud, while others (due to cost, performance, or sovereignty) must remain on-premises. A pop-up strategy forces you to choose one or the other, locking your ingredients into a proprietary freezer.
The Solution: The Universal Pantry for Private AI Anywhere
To achieve true readiness, organizations must move toward Private AI. This is the vision of a unified platform that brings AI to your data, rather than forcing you to move your data to the AI.
This approach relies on a Unified Data Fabric that acts as a Universal Pantry, ensuring data is ready to cook regardless of where it resides. This comprises of three elements:
- Unified Governance: In a professional kitchen, hygiene standards apply everywhere. Similarly, a Shared Data Experience creates a persistent, active metadata and security layer. Policies, tags, and lineage follow the data across all environments. If a user is denied access to Personally Identified Information (PII) on-prem, they are strictly denied in the cloud. This solves the governance fragmentation that kills Private AI projects.
- Data Security and/or Sovereignty: Private AI enables you to own your data, not your vendor. By adopting open table formats like Apache Iceberg, you ensure your data is ready for any tool or model without vendor lock-in. You keep the keys to your own pantry, ensuring true data sovereignty.
- Compute-to-Data: Instead of moving massive datasets to a model (and paying the egress tax), bring compute to the data. This is the essence of Private AI: running models and inference directly alongside your governed data, whether in the cloud, the data center, or at the edge.
Stop Cleaning, Start Cooking
We are entering an era where competitive advantage will not go to the company with the largest LLM, but to the company that can trust its data enough to use it to gain unique insights and business value.
Private AI is not a buzzword; it is a strategic necessity for businesses that value their intellectual property. But it does not start with the algorithm. It starts with the mise en place.
And if your data isn’t ready to cook? Put down the model and fix the pantry first.
