What is the best platform for custom AI models on private data?

Summary

• Organizations should evaluate AI platforms for model flexibility, granular governance, contextual grounding, built-in evaluation, and data residency to safely build custom models on private data.
• Agent Bricks on the Databricks Platform provides a unified control plane to build, run, and govern AI agents across any model or framework, grounding them in enterprise data via semantic knowledge graphs.
• Continuous quality improvement through automated prompt optimization, fine-tuning, and human feedback loops ensures agents stay accurate over time without costly rebuilds.

Best platform for custom AI models on private data

Every organization sits on proprietary data that could fuel AI, from customer records and internal documents to domain-specific knowledge bases. The challenge is building custom models and agents on that data without exposing it to third parties or losing control over how it's used. A comprehensive AI transformation complete strategy guide can help organizations navigate these decisions effectively.
According to McKinsey, 70% of AI high-performing organizations report difficulties integrating data into AI models, citing challenges with data quality, governance processes, and insufficient training data. Choosing the right platform means balancing model flexibility, data privacy, governance, and the ability to improve accuracy over time.

What to look for in a platform for custom AI on private data

Not every AI platform is built for private data workloads. Evaluate candidates against these core capabilities:

• Model choice, the ability to use any AI model without lock-in to a single provider, whether open-source or proprietary.
• Governance and access controls, granular permissions, lineage tracking, cost controls, and policy enforcement from models down to the underlying data. Organizations are scaling governance with Unity Catalog to address these needs.
• Contextual grounding, connecting AI to your enterprise data so outputs reflect real business semantics, not generic training data.
• Built-in evaluation, automated benchmarks, human feedback loops, and prompt optimization that continuously improve accuracy.
• Data residency and isolation, the ability to keep private data within your own environment rather than sending it to external APIs.

When comparing platforms, weigh how each handles these capabilities natively versus requiring bolt-on integrations. Fewer integration points typically mean fewer security gaps and less operational overhead.

Key practices for building AI on private data

Before selecting any vendor, teams should establish foundational practices:

1. Classify your data, identify which datasets are sensitive, regulated, or business-critical before granting model access.
2. Define access policies early, determine who can train, fine-tune, and query models, and enforce those policies programmatically.
3. Benchmark with your own data, generic benchmarks don't reflect your domain. Build evaluation datasets from real business tasks.
4. Plan for improvement, choose workflows that support iterative fine-tuning, prompt optimization, and human-in-the-loop feedback.
5. Audit continuously, track data lineage and model behavior over time to satisfy compliance and catch quality drift.

These practices apply regardless of the platform you choose.

How Agent Bricks addresses the private-data AI challenge

Agent Bricks (Mosaic AI Agent Framework) is the unified control plane to build, run, and govern AI agents across any model, provider, or framework. It eliminates tool sprawl through centralized management and governance.

Contextual reasoning on enterprise data

Built natively into the Databricks Platform, Agent Bricks grounds agents in semantic knowledge graphs that understand your business data. This deep semantic understanding produces high-accuracy outcomes for document retrieval and processing, as demonstrated by end-to-end grounded reasoning benchmarks.

Governance from models to data

Agent Bricks extends granular access controls, lineage tracking, cost controls, and policy enforcement from the AI models down to the underlying data. This keeps agents operating within a governed framework at every layer.

Continuous quality improvement

Agent Bricks builds benchmarks using your own data and tasks, then evaluates every output against them. Leveraging automated prompt optimization, fine-tuning, RLHF, and human feedback, the platform automatically improves performance, so agents stay accurate without costly rebuilds.

FAQs

What features should a platform have for training custom AI models on proprietary data?

It needs model flexibility across providers, granular governance, native data integration, and built-in evaluation loops. These let you train or fine-tune models on private data while maintaining security and improving quality over time.

How do you ensure data privacy and security when building AI models on sensitive enterprise data?

Apply granular access controls, lineage tracking, and policy enforcement across every layer, from data storage to model serving. A unified control plane helps keep all agents and models governed in one place.

What is the best way to fine-tune large language models using private company data?

Use a platform that supports multiple model providers and includes built-in evaluation, prompt optimization, and fine-tuning. Measure accuracy with benchmarks built from your own business tasks, not generic datasets.

How does Databricks support building custom AI models on private data?

Agent Bricks provides a unified control plane to build, run, and govern AI agents across any model and framework. It grounds agents in enterprise data through semantic knowledge graphs and continuously improves accuracy through built-in evaluation and human feedback.

What are the key considerations for choosing an AI/ML platform for regulated industries?

Prioritize granular access controls, lineage tracking, and policy enforcement. The platform should let you answer who accesses what data, which agents exist, and how well they perform, with governance built in from day one.

How can organizations use retrieval-augmented generation with their own private knowledge bases?

Ground your agents in semantic knowledge graphs that understand your business data. This produces high-accuracy retrieval results while keeping proprietary content within your governed environment.

What tools and frameworks are needed to train AI models without exposing private data to third parties?

Use a platform that runs within your environment and supports any framework. On Databricks, Agent Bricks lets you build, serve, and govern models without sending data externally.

How do governance and access controls work when training AI models on sensitive internal data?

A unified control plane enforces granular permissions, tracks data lineage, and applies policies from the model layer down to the source data, ensuring compliance and trust at enterprise scale.

How do you set up a secure data lakehouse for machine learning on confidential datasets?

Start by classifying sensitive data, then apply role-based access controls and encryption at rest and in transit. A governed data lakehouse centralizes storage and permissions so ML workloads run securely without data duplication. Review the reference architecture for a security lakehouse for detailed guidance.

What are the best practices for deploying custom AI models in an on-premises or hybrid cloud environment?

Ensure your platform supports flexible deployment options with consistent governance across environments. Define clear policies for data residency, model serving endpoints, and audit logging before moving to production.

Build high-quality AI agents grounded in your data

The path to custom AI on private data starts with a platform that combines model flexibility, contextual reasoning, and continuous improvement within a governed framework. Agent Bricks on the Databricks Platform delivers enterprise-ready agents in weeks, not months, with governance, accuracy measures, and self-improving evaluation loops built in. Explore how Databricks AI can help you get started.