AI infrastructure design: How to manage unstructured data for generative AI

AI-ready infrastructure starts with effective unstructured data management. Emails, documents, chats, and transcripts contain the richest signals for AI, but also the greatest risk. Without continuous data discovery, automated classification, and real-time permission enforcement, AI initiatives either stall or create exposure.

There are no sustainable AI efficiencies without trusted, fully permissioned data. That means governance that travels with the data: ensuring consent, preferences, and policies actively control how unstructured data flows into analytics and AI systems.

Get unstructured data right, and AI becomes scalable, compliant, and revenue-driving—not risky and reactive.

Why unstructured data matters for generative AI

Generative AI is reshaping how enterprises operate, but most organizations are hitting the same wall. A striking 84% of companies already using generative AI report problems with their data sources. The bottleneck isn't model quality or computing power. It's unstructured data.

Consider what that means at scale: roughly 80% of enterprise data lives in PDFs, chat logs, support transcripts, and internal documents—all of it largely ungoverned and underutilized. Without a deliberate strategy for managing unstructured data, AI initiatives don't just slow down. They stall entirely.

The organizations pulling ahead have made unstructured data a strategic priority. Your teams generate massive volumes of data rich with context and business value every day. But that same data carries real risk: exposed sensitive information, decentralized permissions, and complex compliance obligations.

The right infrastructure doesn't just store this data. It discovers, classifies, governs, and activates it at scale: moving your organization from stalled AI pilots to production systems that drive measurable growth.

How to build infrastructure that supports AI

Building robust AI infrastructure means rethinking data management from the ground up. You need systems that ingest from every source, classify automatically, enforce permissions in real time, and track lineage from collection to model training. Most AI projects stall for a simple reason: teams don't know which data they're authorized to use.

The solution comes down to three non-negotiables:

• Automated data mapping and discovery: you need to know what data you have and where it lives.
• Real-time auditabilityL you need to understand how data moves across systems
• Automated permission enforcement: Only authorized data should ever reach your AI.

These principles must apply equally to structured databases and unstructured cloud storage. Without them, you cannot scale.

Data discovery and classification

Modern enterprises manage an average of seven different data storage platforms. Manual inventory doesn't scale. You need automated discovery that scans every system where unstructured data lives, including O365, Slack, S3, Azure, Google Suite, internal file shares, and beyond.

Transcend Unstructured Discovery finds and governs sensitive data across PDFs, text logs, and more. Using advanced techniques like regular expressions and named entity recognition, it scans your systems, identifies files, and surfaces sensitive information inside documents, spreadsheets, and presentations — automatically.

The results speak for themselves: up to 80% reduction in manual effort and 25–40% improvement in classification accuracy over manual processes. Real-time visibility and consistent labeling form the foundation for governance. Without classification, permission enforcement is impossible.

Scalable storage and compute environments

AI workloads require a fundamentally different storage architecture than traditional applications.

The cloud object storage market, valued at $15.5 billion in 2024, is projected to reach $45.2 billion by 2033, reflecting just how rapidly enterprises are rearchitecting for scale. Object storage delivers the elasticity and performance AI training demands, with single models routinely spanning hundreds of terabytes of text, images, and video.

The winning approach separates storage from compute. Data lakes built on object storage—Amazon S3, Google Cloud Storage, Azure Blob Storage—provide the foundation. Data lakehouses add structure and governance on top. Together, they support both batch training and real-time inference at scale.

Compute cannot be an afterthought. AI workloads are projected to consume around 28% of global data center capacity by 2027. Distributed compute, including containerized workloads, frameworks like Spark, and GPU-backed nodes, enables parallel processing across your entire data estate.

Data quality and permission enforcement

Clean, permissioned data isn't a nice-to-have, it's the foundation of reliable AI. 73% percent of organizations cite data quality and bias as their top challenge. Dirty data produces inaccurate models, while improper permissions introduce compliance risk. Both erode the value of your AI investments.

The permission problem is particularly complex. Consents live in one system, opt-outs in another, and "Do Not Train" signals in a third. Pipelines pull from dozens of sources, each governed by a different model. Without a unified permission layer, there's no way to guarantee your training data is authorized.

Transcend solves this with a compliance layer that captures, saves, and enforces permissions across your entire data estate, including AI-specific controls like Do Not Train. Opt-outs apply everywhere, automatically. Compliance is baked in at every stage, not bolted on after the fact.

Transcend's role in streamlining unstructured data for AI

Most organizations piece together discovery, workflow tools, and custom enforcement scripts and call it a strategy. It doesn't scale. AI initiatives fail not from lack of investment, but from fragmented foundations that can't support production-grade demands.

Transcend delivers a unified compliance layer: automated mapping, real-time auditability, and permissions enforcement built in from the start. With hundreds of integrations across databases, data warehouses, SaaS platforms, and unstructured storage, Transcend enforces permissions everywhere your data lives, not just in the systems you remember to check.

Real-time permissions across enterprise systems

Multi-brand and multi-region organizations need cross-system consistency. When a user opts out, that signal should propagate immediately: across every system, workflow, and model. Most organizations can't achieve this because their systems don't talk to each other.

Transcend changes that. It automatically discovers, classifies, and updates permissions across your entire estate—no manual spreadsheets, no missed updates. Changes propagate in real time to data warehouses, AI workflows, and production systems without engineering intervention.

This is what separates infrastructure-grade governance from ad hoc tooling. Your AI always runs on clean, permissioned data because the compliance layer filters it before models ever see it. No retrofits, manual checks, or surprises.

Automated governance and reduced engineering burden

Manual governance doesn't scale, and custom scripts eventually break. 58% percent of enterprises identify classifying unstructured data for AI as their single greatest challenge. And moving that data without disruption ranks a close second.

Transcend automates sync, enforcement, and integration — eliminating the ad hoc scripts and one-off logic that drain engineering bandwidth. Your teams stop maintaining compliance plumbing and start focusing on model development, platform modernization, and user-facing features.

Underpinning this is Sombra, Transcend's secure execution gateway. Each node is stateless for fault tolerance and built for horizontal scaling. With Structured or Unstructured Discovery, Sombra can deploy a Transcend-hosted LLM to classify content across your stack—processing approximately 18,000 classifications per hour with GPU-backed nodes.

Accelerating AI readiness with clean data

AI readiness depends on trustworthy data, not just advanced models. Transcend centralizes permissions, so you have a single layer to enforce opt-outs, deletions, and consents across your entire data ecosystem.

Every action is logged. Every data movement is traced. When a user requests deletion, their data is removed from production systems, backups, and training sets simultaneously. This gives CIOs the audit trail and confidence to green-light AI workloads without hidden compliance risk.

And it gets you there faster. Most platforms require custom integration work for every new model, system, or brand. Transcend deploys once and enforces everywhere. AI-ready data is available quickly, compressing your time to value and accelerating time to market for new initiatives and products.

Implementing AI-ready infrastructure design

Production-grade AI needs more than just tech. You want cross-functional alignment, clear governance, and security-first design. Only 18% of organizations have an enterprise-wide council with real AI authority. Without this, your tech won't deliver value.

Establish cross-functional governance

You need collaboration between IT, privacy, security, and data teams. Each brings expertise: IT optimizes performance, privacy enforces compliance, security handles controls, and data teams lead experimentation.

Set up a center of excellence or a governance committee. Their job is to:

• Define data usage, retention, and control policies for AI training
• Standardize policies and escalation paths for exceptions

Handle governance once and enforce it everywhere. Transcend gives you a unified compliance layer, so every team knows which data to use and why.

Leverage automated compliance layers early

Retrofitting compliance after the fact is expensive and slows deployment. Integrate compliance frameworks from the start.

Transcend runs continuous monitoring and instant policy enforcement on sensitive data. All data gets classified as it enters your stack, so permissions auto-apply. Data that doesn’t meet policies never reaches your AI. Preventive controls beat manual audits every time, reducing risk.

Early compliance integration means teams don't pause for reviews—they move quickly, testing new data, models, and architectures while staying within governance guardrails.

Driving strategic impact with AI infrastructure

Modern AI infrastructure generates more than compliance, it drives growth. Organizations with well-developed AI strategies are twice as likely to see revenue growth. Governed models deliver personalized experiences, brand loyalty, and new revenue.

Clean, permissioned data makes predictions and recommendations more accurate. Customers get relevant experiences while their preferences are respected. Your team ships new AI features and enters new markets without regulatory headaches.

The market opportunity is real and time-sensitive. Enterprise AI is projected to grow from $24 billion in 2024 to $150–200 billion by 2030, at over 30% annually. Organizations that solve the governance challenge now will capture that growth. Those still fighting fragmented data, manual processes, and compliance gaps will fall behind.

If you're ready to build on a foundation that scales, reach out to Transcend for a demo. We'll show you exactly how to discover, classify, and govern unstructured data across your entire stack, so every AI initiative runs on data you can trust.