Data security, lifecycle, and strategy for generative AI applications - AWS Prescriptive Guidance
Intended audienceObjectives

Data security, lifecycle, and strategy for generative AI applications

Romain Vivier, Amazon Web Services

July 2025 (document history)

Generative AI is transforming the enterprise landscape. It enables unprecedented levels of innovation, automation and competitive differentiation. However, the ability to realize its full potential depends not only on powerful models but also on a strong and purposeful data strategy. This guide describes data-specific challenges that arise in generative AI initiatives and offers clear direction about how to overcome them and achieve meaningful business outcomes.

One of the most fundamental shifts brought by generative AI is its reliance on large volumes of unstructured and multimodal data. Traditional machine learning typically depends on structured, labeled datasets. However, generative AI systems learn from text, images, audio, code, and video that are often unlabeled and highly variable. Organizations must therefore reassess and expand their traditional data strategies to include these new data types. Doing so helps them to create more context-aware applications, improve user experiences, boost productivity, and accelerate content generation, while reducing reliance on manual input.

The guide outlines the full data lifecycle that supports effective generative AI deployment. This includes preparing and cleansing large-scale datasets, implementing Retrieval Augmented Generation (RAG) pipelines to keep models' context up to date, conducting fine-tuning on domain-specific data, and establishing continuous feedback loops. When completed correctly, these activities enhance model performance and relevance. They also deliver tangible business value through faster delivery of AI use cases, improved decision support, and greater efficiency in operations.

Security and governance are presented as critical pillars of success. The guide explains how to help protect sensitive information, enforce access controls, and address risks (such as hallucinations, data poisoning, and adversarial attacks). Embedding robust governance and monitoring practices into the generative AI workflow supports regulatory compliance requirements, helps protect the enterprise's reputation, and builds internal and external trust in AI systems. It also discusses agentic AI challenges related to data and highlights the need for identity management, traceability, and robust security in agent-based systems.

This guide also connects the data strategy to each phase of generative AI adoption: envision, experiment, launch, and scale. For more about this model, see Maturity model for adopting generative AI on AWS. At each stage, the organization must align its data infrastructure, governance model, and operational readiness with its business goals. This alignment enables a faster path to production, mitigates risk, and makes sure that generative AI solutions can scale responsibly and sustainably across the enterprise.

In summary, a robust data strategy is a prerequisite for generative AI success. Organizations that treat data as a strategic asset and invest in governance, quality, and security are better positioned to deploy generative AI with confidence. They can move more quickly from experimentation to enterprise-wide transformation and achieve measurable outcomes, such as improved customer experiences, operational efficiency, and long-term competitive advantage.

Intended audience

This guide is intended for enterprise leaders, data professionals, and technology decision-makers who want to build and operationalize a robust and scalable data strategy for generative AI.  The recommendations in this guide are suitable for enterprises embarking on or advancing their generative AI journey. It helps you align your data strategy, governance, and security frameworks to maximize the business value and benefits of generative AI. To understand the concepts and recommendations in this guide, you should be familiar with fundamental AI and data concepts, and you should be familiar with the basics of enterprise IT governance and compliance.

Objectives

Modifying your data strategy according to the recommendations in this guide can have the following benefits:

  • Understand how data requirements and practices differ between traditional ML and generative AI, and understand what these differences mean for your enterprise data strategy.

  • Understand the differences between structured, labelled data for traditional ML and the unstructured, multimodal data that fuels generative AI.

  • Beyond established ML practices, understand why generative AI models require new approaches to data preparation, integration, and governance.

  • Learn how data synthesizing through generative AI can accelerate more traditional ML use cases.