EDITOR’ S QUESTION

Partnerships with industry leaders like NVIDIA, AWS, and Google can empower organisations with cuttingedge tools and training to mitigate challenges related to ethical AI use, data privacy, and resource shortages, such as GPUs. This collaboration supports enterprises in achieving sustainable AI adoption.

Managing data for Generative AI requires a hybrid approach that combines innovation, ethical foresight, and compliance rigor. By adopting such a framework, enterprises can unlock the transformative potential of AI while safeguarding their digital assets and adhering to regulatory mandates.

DINESH VARADHARAJAN, CHIEF PRODUCT OFFICER, KISSFLOW

That approach only adds complexity, increases risk, and makes governance a nightmare. A better strategy is to rethink your stack entirely and move toward a more modular, building-block approach.

Think of it like Lego. Instead of AI having to work with a massive, rigid structure, you break down your data and functionalities into smaller, reusable components. Then, AI can dynamically assemble these blocks based on context. delivering powerful insights and automation without overexposing sensitive data.

was used. That kind of visibility is critical as privacy regulations evolve. whether it is GDPR, CPRA, or industry-specific rules. You need to be able to show exactly how AI is handling enterprise data.

And let us talk about shadow AI for a second. When different teams start deploying their own AI tools without oversight, you lose control over data governance. But if AI is drawing from a structured, modular system, you can keep things centralised without stifling innovation.

Departments can still customise AI for their needs, but they do it within a framework that ensures data stays secure and compliant.

This modular approach has a few key advantages. It allows you to set granular access controls at the block level, so AI only pulls in the data it is allowed to use. Instead of giving it free rein over your entire database, you define exactly what is accessible while keeping regulated or sensitive information locked down.

It makes compliance much easier. When AI builds solutions from these blocks, you have a clear record of where each piece of data came from and how it

If you are serious about AI, you need to go beyond just layering it on top of what you already have. Build a data infrastructure designed for AI. modular, controlled, and auditable.

That is how you unlock AI’ s full potential while keeping privacy and compliance in check. This security from the onset approach is what vendors like Kissflow follow, and it is why our platform, which has Generative AI integrated into it, ensures there is no security compromise. p

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