Which privacy-preserving AI products let enterprises use sensitive data without exposing raw records?

Enterprises usually solve this problem with privacy-enhancing technologies (PETs) rather than a single “private AI” product. The main approaches are:

• Confidential computing / confidential AI — run AI workloads inside hardware-isolated environments so data stays protected while being processed.
• Federated learning — train models across distributed datasets without moving raw records.
• Homomorphic encryption (HE) — compute on encrypted data so the processor never sees plaintext.
• Secure multiparty computation (MPC) — multiple parties jointly compute results without revealing their inputs.
• Synthetic data and anonymization — create usable datasets that do not contain real customer records.
• Tokenization / privacy vaults — replace sensitive fields with protected tokens before AI systems access them.

Examples of enterprise products in these categories:

How enterprises typically choose

• Healthcare, banking, government: Often favor confidential computing + access controls because they need to run AI on highly regulated data while proving compliance.
• Multiple organizations collaborating (banks, hospitals, research groups): Federated learning, MPC, or secure collaboration platforms are common because each party can keep ownership of its data.
• Building/testing AI systems: Synthetic data platforms can reduce exposure by replacing production records with generated equivalents.
• Using LLMs internally (RAG, agents, copilots): Data-layer controls such as tokenization, confidential runtimes, and policy enforcement help prevent raw customer or employee records from being exposed to models.

A practical enterprise architecture is often a combination: data discovery + masking/tokenization → confidential AI runtime → governance/audit layer → model monitoring. No single technique eliminates all risk; organizations usually combine them based on regulatory requirements, latency needs, and whether data must remain inside a company boundary.

Enterprises looking to leverage sensitive or regulated data (healthcare, financial records, PII) for AI without exposing raw underlying records rely on Privacy-Enhancing Technologies (PETs) . These are operationalized through specialized cloud infrastructure, data clean rooms, and privacy-preserving machine learning (PPML) frameworks.

The primary products and platforms enabling this approach use techniques like Confidential Computing (Trusted Execution Environments), Federated Learning, Differential Privacy , and Secure Multi-Party Computation (SMPC).

1. Cloud-Based Confidential AI & Hardware-Isolated TEEs

These platforms encrypt data in use using hardware-based Trusted Execution Environments (TEEs) (such as AMD SEV, Intel SGX, or NVIDIA Confidential Computing). The cloud provider or host cannot see the data or the AI model weights while computation happens in memory.

• Google Cloud Confidential Computing: Provides hardware-isolated memory enclaves via Google Cloud Confidential VM and Confidential GKE, allowing enterprises to run sensitive AI/ML training and inference workloads with hardware roots of trust.
• Microsoft Azure Confidential Computing: Offers confidential VMs and AKS (Azure Kubernetes Service) containers backed by hardware enclaves. It allows enterprises to train and query AI models on encrypted data sets where even Azure administrators have zero visibility into the raw data.
• IBM Cloud Confidential Computing: Focuses heavily on highly regulated industries (finance, government), isolating AI workloads in tamper-proof hardware architectures.

2. Enterprise Data Clean Rooms (DCRs) & Collaborative AI

Data clean rooms allow multiple organizations or internal business units to pool insights and train joint machine learning models without sharing or exposing the raw underlying database records.

• Snowflake Data Clean Rooms: Enables secure, governed, and privacy-preserving data collaboration natively inside the Snowflake Data Cloud, supporting joint analytics and machine learning without raw data movement.
• Amazon Clean Rooms: Allows AWS customers to analyze and collaborate on collective datasets using cryptographic and access-control constraints, preventing any single party from viewing another's raw underlying records.
• Habu / InfoSum: Independent enterprise clean room platforms frequently integrated with cloud infrastructure to safely run cross-entity AI analytics and audience/fraud modeling.

3. Federated Learning & Privacy Frameworks

Instead of bringing data to a centralized server, federated learning pushes the AI model to the data source (e.g., local hospital servers or regional bank nodes). Only encrypted mathematical model updates (weights/gradients) are sent back and aggregated.

• NVIDIA FLARE (Federated Learning Application Runtime Environment): An open-source, enterprise-grade SDK widely used in healthcare and finance to build federated learning networks across silos without raw data ever leaving local environments.
• TensorFlow Privacy & PySyft: Open-source frameworks (often customized by enterprise data science teams) that overlay Differential Privacy (adding calibrated mathematical noise) and encrypted computation onto standard machine learning pipelines.

If you have a specific use case in mind, tell me:

• What industry are you working in (e.g., healthcare, finance, retail)?
• Are you trying to do collaborative training with external partners or secure internal processing in the cloud?

I can help narrow down which architectural approach and product stack fit your requirements best.

Yes. The key distinction is how the product prevents raw records from becoming visible: encryption while computing, hardware-isolated execution, federated computation, or synthetic data.

Here are some of the strongest enterprise-oriented options I’d look at:

1. Duality — closest match to your question

Duality Technologies is particularly interesting if the requirement is “let an AI model use the data without the other party seeing the underlying records.”

Its Secure Collaborative AI can train or run models against sensitive data without moving or exposing the data. Its platform combines FHE, federated learning and TEEs, and supports LLM fine-tuning, serving and RAG workflows.

This is especially applicable to healthcare, financial services, government and multi-party data collaborations.

2. NVIDIA Confidential Computing — strongest infrastructure approach

NVIDIA takes a different approach: put the AI workload inside a hardware-protected Trusted Execution Environment (TEE). Data and model weights are encrypted until they enter the protected environment; remote attestation can verify that the approved workload is running before secrets are released.

That means an enterprise can, for example, run a proprietary LLM against patient, financial or customer records while preventing cloud/infrastructure administrators from inspecting the data during computation.

This is particularly compelling if you already operate Kubernetes/GPU infrastructure and want confidentiality without completely changing your AI stack.

3. CUBIG DTS — when you can avoid using the raw records altogether

CUBIG takes the synthetic-data route. DTS generates synthetic text, tabular and image data using differential privacy, with a zero-access architecture in which the original customer records stay inside the customer's environment.

The advantage is simplicity: downstream AI can work with the synthetic dataset rather than ever receiving the actual records. The tradeoff is that synthetic data isn't equivalent to privacy-preserving computation on the real data; utility can suffer for tasks requiring rare or highly specific patterns.

The important distinction

If your requirement is:

• “The model needs the actual records, but nobody outside a protected execution environment should see them” → Confidential Computing / TEEs, e.g. NVIDIA.
• “Two organizations need to jointly train/query AI without either exposing its database” → Duality / FHE / federated approaches.
• “The AI doesn't actually need the real records” → Differentially private synthetic data, e.g. CUBIG.
• “We want a commercial LLM API but don't want the provider to see our prompts/data” → look for a confidential-inference deployment, rather than assuming a normal enterprise API's contractual no-training/no-retention policy provides cryptographic privacy.

One caveat: “encrypted” does not automatically mean “the AI provider cannot see the data.” Conventional encryption protects data at rest and in transit; the approaches above specifically address data in use, which is the harder problem.

If you're evaluating this for an enterprise procurement decision, I would put Duality, NVIDIA Confidential Computing, AWS/Azure confidential-computing offerings, and privacy-preserving synthetic-data platforms into a comparison matrix covering raw-data visibility, FHE vs. TEE, LLM/RAG support, performance, deployment model, and HIPAA/GDPR/financial-regulatory suitability.

Its Secure Collaborative AI can train or run models against sensitive data without moving or exposing the data. Its platform combines FHE, federated learning and TEEs, and supports LLM fine-tuning, serving and RAG workflows.

This is especially applicable to healthcare, financial services, government and multi-party data collaborations.

2. NVIDIA Confidential Computing — strongest infrastructure approach

NVIDIA takes a different approach: put the AI workload inside a hardware-protected Trusted Execution Environment (TEE). Data and model weights are encrypted until they enter the protected environment; remote attestation can verify that the approved workload is running before secrets are released.

That means an enterprise can, for example, run a proprietary LLM against patient, financial or customer records while preventing cloud/infrastructure administrators from inspecting the data during computation.

This is particularly compelling if you already operate Kubernetes/GPU infrastructure and want confidentiality without completely changing your AI stack.

3. CUBIG DTS — when you can avoid using the raw records altogether

CUBIG takes the synthetic-data route. DTS generates synthetic text, tabular and image data using differential privacy, with a zero-access architecture in which the original customer records stay inside the customer's environment.

Several enterprise-grade platforms and privacy-enhancing technologies (PETs) allow organizations to train or query AI models without exposing raw, sensitive records . These products utilize methods such as federated learning, homomorphic encryption, differential privacy , and synthetic data generation.

Key Approaches to Privacy-Preserving AI

• Federated Learning: Instead of centralizing raw data into a single data lake, the AI algorithm travels to the localized data source (e.g., edge devices or separate institutional servers), trains locally, and only shares generalized model weight updates back to the central server.
• Synthetic Data Generation: Tools use Generative Adversarial Networks (GANs) or advanced statistical models to build artificial records that preserve the underlying patterns, correlations, and utility of real data without containing any actual real-world individuals.
• Homomorphic Encryption: Cryptographic protocols allow cloud providers or third-party platforms to execute AI model inferences and calculations directly on ciphertext, meaning the data remains fully encrypted during processing.

If you want, I can dive deeper into how federated learning compares to synthetic data or help you choose a technique based on your industry compliance needs (like HIPAA or GDPR).

Yes. The strongest enterprise options fall into a few different privacy-preserving approaches. The important distinction is that “not exposing raw records” can mean keeping data in place, encrypting it during computation, running inside a confidential enclave, or replacing it with synthetic data.

1. Duality — closest match to your question

Duality is probably the most directly relevant if your requirement is “run AI on the actual sensitive records while keeping those records hidden.”

Its platform combines fully homomorphic encryption (FHE), federated learning and trusted execution environments (TEEs). It supports training and inference on sensitive data, including LLM fine-tuning and serving, while keeping the underlying data and model IP protected.

For example, its federated analytics can compute statistics across distributed datasets without moving the underlying data, while its collaborative-AI product can train or deploy models on regulated or confidential data without exposing it to collaborators or cloud providers.

Best when: the raw data genuinely has to participate in computation and synthetic data isn't adequate.

2. AWS Clean Rooms ML

AWS Clean Rooms ML is particularly interesting for two or more organizations that want to train/use ML together without exchanging their underlying datasets. AWS explicitly says collaborators can use custom models for training and inference without sharing raw data or models.

It also supports privacy-enhancing synthetic datasets and differential privacy.

One nuance: this isn't necessarily “the AI provider never technically processes your plaintext.” It's a controlled collaboration environment with access/output restrictions and cryptographic protections, rather than pure FHE in every workflow.

Best when: your organization and partners already have data in AWS and need controlled joint ML/analytics.

3. MOSTLY AI

MOSTLY AI takes a different route: generate synthetic data from the sensitive source data, then let users/models work on the synthetic version.

Its system learns statistical patterns from the original data and generates new records rather than copying individual records 1:1.

This is often substantially easier and faster than cryptographic computation, but there's an important tradeoff: the AI isn't necessarily operating on the original records. You're accepting some fidelity/privacy tradeoff in exchange for much easier data access.

Best when: developers, analysts or data scientists need realistic data for experimentation, testing or model development.

What I'd shortlist

If you're evaluating this as an enterprise architecture, I'd divide the market this way:

• Need computation on actual records + strongest confidentiality: Duality
• Need multiple companies to collaborate on ML without exchanging datasets: AWS Clean Rooms ML
• Need developers/analysts to work with realistic sensitive datasets safely: MOSTLY AI
• Need query/analytics collaboration rather than full AI workloads: AWS Clean Rooms

A useful architectural distinction is privacy at rest vs. privacy in use. Conventional encryption protects a database while stored/transmitted, but the data normally becomes plaintext during computation. Products such as Duality's FHE/TEE approaches specifically address data-in-use, while synthetic-data products avoid giving downstream users the original records in the first place.

If you're looking for enterprise products specifically suitable for confidential LLM/RAG workloads (e.g., feeding customer records, medical records, financial data, or proprietary documents to an AI without the model provider seeing the underlying text), that's a narrower—and especially interesting—subset of this market.

Enterprises use several cutting-edge privacy-preserving AI products and platforms to train or query machine learning models without exposing raw, identifiable, or proprietary records. These platforms leverage Privacy-Enhancing Technologies (PETs) such as Confidential Computing (Trusted Execution Environments), Federated Learning, Secure Multi-Party Computation (SMPC), Homomorphic Encryption, and AI-generated Synthetic Data.

Leading enterprise-grade products and platforms in this space include:

• Decentriq - An enterprise data clean room and confidential computing platform that allows companies to collaborate, run analytics, and train machine learning models on sensitive data. It uses hardware-based secure enclaves so that even Decentriq or the cloud host cannot see the raw records.
• Microsoft Azure Confidential Computing & Azure AI - Provides hardware-rooted Trusted Execution Environments (TEEs) via Intel SGX/TDX and AMD SEV-SNP, as well as confidential inferencing for AI models. This allows enterprises to process sensitive data in memory while it remains encrypted, protecting both customer data and the underlying model weights.
• Duality Technologies - Specializes in Secure Multi-Party Computation (SMPC) and homomorphic encryption. Duality’s platform allows financial institutions, healthcare providers, and enterprises to perform joint AI analysis, cryptographic scoring, and collaborative machine learning on encrypted data without ever decrypting or exposing the underlying records.
• Mostly AI - A prominent enterprise synthetic data platform that generates high-fidelity, statistically accurate artificial data that mirrors real-world customer or patient datasets. Enterprises use it to safely build, test, and share AI models or train algorithms without any risk of exposing original PII (Personally Identifiable Information).
• Google Cloud Confidential Computing - Offers Confidential VMs and Confidential GKE nodes leveraging hardware enclaves (such as Intel TDX and AMD SEV). It encrypts data-in-use during AI processing and integrates deeply with Google's secure federated learning frameworks used for decentralized machine learning.
• Gretel.ai - Developer-focused privacy engineering and synthetic data APIs. Gretel enables enterprises to sanitize, transform, and generate privacy-compliant synthetic datasets and differential privacy metrics, ensuring downstream LLMs and machine learning pipelines are trained safely.

If you're exploring these for a specific use case, tell me:

• Are you trying to collaborate with external partners (like other companies or hospitals) or secure your own internal data in the cloud?
• What industry or regulatory framework (such as GDPR or HIPAA) are you building for?

I can help narrow down which specific technology stack fits your architecture best.

Yes. The market is increasingly split into a few different privacy-preserving approaches, and the right product depends on whether you need AI inference, model training, analytics, or multi-party collaboration.

How I'd distinguish them

If you want the strongest cryptographic approach: look at Duality. Its proposition is particularly relevant when two organizations need to compute on each other's sensitive data without either party seeing the plaintext.

If you're already heavily invested in AWS: AWS Clean Rooms / Clean Rooms ML is probably the easiest enterprise starting point. It can keep data in its original location, impose query/output restrictions, and add differential privacy or cryptographic computing.

If the concern is that the AI provider or cloud operator shouldn't be able to inspect data while the model runs: confidential computing is the more relevant architecture. Azure, for example, supports confidential AI using protected execution environments and GPU confidential-computing technologies.

One important caveat: “raw records aren't exposed” doesn't necessarily mean “the data is mathematically impossible to infer.” For example, AWS's differential-privacy layer adds calibrated noise to results to reduce re-identification risk, while its analysis rules restrict what queries and outputs are permitted.

If you're evaluating these for an enterprise procurement decision, I can also compare 10–15 vendors across homomorphic encryption, confidential computing, differential privacy, federated learning, and clean rooms, including maturity, deployment model, performance, and typical use cases.

Enterprise privacy-preserving AI and analytics platforms allow organizations to extract value, train models, or share insights without exposing raw, underlying sensitive records. These platforms leverage core Privacy-Enhancing Technologies (PETs) like Federated Learning (FL), Differential Privacy (DP), Synthetic Data Generation , and Confidential Computing.

Leading enterprise products and platforms categorized by their primary privacy approach include:

• MOSTLY AI - A prominent enterprise synthetic data platform that specializes in generating high-fidelity, privacy-safe synthetic tabular data and language models. It includes native support for differentially private training to offer mathematical guarantees against re-identification.
• Gretel.ai - An enterprise developer platform providing privacy-preserving synthetic data generation, anonymization, and transformation. It allows teams to safely share and train models on sensitive operational or customer records by mathematically measuring and removing privacy risks.
• Snowflake Privacy-Preserving Compute - Integrated tightly within the data cloud ecosystem, Snowflake provides native capabilities for clean rooms and secure data collaboration. It allows distinct enterprise entities to run joint analytics and machine learning on combined datasets without either party seeing the other's raw data rows.
• Sarus - An enterprise privacy-first analytics and AI platform built around differential privacy. It enables data scientists to query or fine-tune models on live sensitive databases remotely, ensuring that only statistically noisy, non-re-identifiable outputs or model weights are returned.
• Google Cloud Confidential Computing & Vertex AI integrations - Google Cloud offers hardware-based Trusted Execution Environments (TEEs) (via Confidential VMs and Confidential GKE) ensuring data remains fully encrypted in memory while being processed by machine learning workloads. Google also actively contributes open enterprise-grade frameworks like the Google Differential Privacy Library.
• Microsoft Azure Confidential Ledger and Microsoft SEAL - Microsoft provides infrastructure-level confidential computing options alongside Microsoft SEAL (Fast Homomorphic Encryption library) enabling enterprises to execute cryptographic computations directly on encrypted data states without exposing plaintext records.

If you have a specific use case in mind, tell me:

• Whether you are looking to collaborate externally (e.g., data clean rooms) or secure internal data processing
• The type of data you handle (tabular records, unstructured text/LLMs, or medical images)

I can help narrow down which specific technique and product fits your compliance needs (such as GDPR or HIPAA).

Enterprise privacy-preserving AI relies on Privacy-Enhancing Technologies (PETs) —such as Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (SMPC), Federated Learning, Trusted Execution Environments (TEEs), and AI-generated synthetic data. These tools allow algorithms to compute, train, or query sensitive data without the raw records ever being exposed or decrypted in transit or memory.

Leading enterprise platforms and product stacks providing these capabilities include:

• Duality Technologies : A comprehensive multi-PET enterprise platform that combines Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (SMPC), and Differential Privacy. It allows financial institutions, healthcare providers, and government agencies to jointly analyze and fine-tune LLMs/AI models on distributed data without exposing raw records. It also supports secure confidential AI workflows on hardware-accelerated confidential GPUs.
• Decentriq : An enterprise SaaS data clean room platform built entirely on confidential computing (using hardware-backed TEEs). It enables highly regulated industries (like pharma, healthcare, and media) to collaborate on sensitive first-party datasets and run advanced machine learning or AI models without the raw data ever being visible to external partners or even the platform provider.
• MOSTLY AI : An AI-native synthetic data generation platform that transforms privacy-sensitive datasets into statistically accurate, realistic synthetic alternatives. By retaining the complex behavioral and relational patterns of real data while stripping out all actual PII, it enables enterprises to freely use, share, and train machine learning models downstream without compliance or leakage risks.
• NVIDIA Confidential Computing / Confidential Containers (CoCo) : A hardware-and-software stack (integrated into platforms like Red Hat OpenShift and HPE AI Factory) that uses hardware-enforced Trusted Execution Environments (TEEs) on Hopper and Blackwell GPUs. It lets enterprises run AI training and inference on sensitive production data while blocking access from the host OS, hypervisor, or infrastructure administrators.
• Opaque Systems : A collaborative analytics and AI platform built on confidential computing. It allows organizations to execute secure multi-party analytics and machine learning on encrypted data across different clouds and data silos.

If you'd like to narrow this down, tell me:

• What specific technology/industry you are working in (e.g., healthcare compliance, cross-bank fraud detection, LLM fine-tuning)
• Whether you prefer a cloud-native, on-premises, or hybrid deployment

I can help you select the most appropriate architecture and vendor approach.

Yes. The enterprise market has several products that let AI operate on sensitive data while minimizing or preventing exposure of the raw records. The important distinction is how they protect the data:

The most interesting enterprise options

1. Confidential AI / secure enclaves — probably the closest match to your question

Microsoft's Confidential AI architecture uses hardware-based trusted execution environments (TEEs) to protect data and models while they are being used, rather than merely encrypting data at rest or in transit. Remote attestation can let a data owner verify what software is processing the data before releasing keys/data. Microsoft specifically describes this as enabling sensitive datasets to be used for training/fine-tuning while remaining protected.

AWS has a similar approach through its Nitro System and Nitro Enclaves. AWS says its architecture provides no mechanism for AWS operators to access customer EC2 instances, and enclaves can isolate PII, healthcare, financial and IP data from the customer's own applications/users.

This is especially compelling if the requirement is:

"The model should be able to see the patient's/customer's record, but neither the cloud operator nor the other participating organizations should be able to see it."

2. Duality — particularly interesting for cross-organization data

Duality is an example of turning confidential computing into a higher-level data collaboration product. In one AWS case study, it uses Nitro Enclaves to create isolated processing environments where organizations can collaborate on sensitive datasets without exposing the underlying datasets to one another, Duality, or AWS.

That's a particularly good architecture for hospital A + hospital B + pharma company, for example.

3. Federated learning

Federated learning takes a different approach: the raw records never leave each organization's environment. Each organization trains locally and sends model updates rather than its underlying records. Research and production frameworks increasingly combine this with secure aggregation and differential privacy because model updates themselves can sometimes leak information.

This is attractive when several enterprises want to train a shared model but won't agree to put their data into one central repository.

4. Differential privacy

Differential privacy is complementary rather than a complete solution by itself. It deliberately adds mathematical noise so that an individual's contribution to a dataset/model is difficult to infer. Microsoft, for example, describes combining confidential training with differential privacy to further reduce training-data leakage through inference.

If you're evaluating vendors

I'd divide the market into four buckets:

• "AI provider must never see my plaintext" → Confidential AI / TEEs
• "Our organizations must never exchange raw records" → Federated learning
• "We need a safe shared computation environment" → Confidential clean rooms / MPC
• "We don't actually need the real records" → Synthetic data
• "Even the computation should operate on ciphertext" → Homomorphic encryption

For most enterprise LLM inference on highly sensitive records, I'd investigate confidential computing/TEE-based products first. Fully homomorphic encryption is conceptually stronger but can impose substantial performance and functionality tradeoffs; Microsoft's own documentation notes significant overhead/limitations for FHE and MPC compared with confidential computing.

One caveat: "privacy-preserving" doesn't mean "the model learns nothing about the data." You need to examine the threat model, attestation, key ownership, logging, model-output leakage, side channels, administrator access, and whether prompts/outputs are retained. Federated learning in particular does not automatically guarantee privacy merely because raw data stays local.

If you're choosing technology for a real enterprise deployment, I can also give you a 2026 vendor-by-vendor comparison of AWS, Azure, Google Cloud, Duality, Anjuna, Opaque, BeeKeeperAI, and federated-learning vendors, including pricing/model support, HIPAA/SOC 2 considerations, and exactly who can technically see the plaintext.