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

Enterprises can leverage sensitive, regulated, or proprietary data for AI training and inference without exposing raw records by utilizing a combination of Confidential Computing (Trusted Execution Environments/TEEs), Data Clean Rooms, Federated Learning , and Data Masking/Tokenization.

The leading enterprise platforms and products providing these privacy-preserving capabilities include:

• Opaque Systems (Opaque Confidential AI) : Built on hardware-based Trusted Execution Environments (TEEs), Opaque provides a collaborative analytics and machine learning platform. It allows multiple parties or internal business units to run AI models and SQL/vector queries on fully encrypted data without the platform host, cloud provider, or other collaborators ever seeing the underlying raw records.
• Decentriq : An enterprise SaaS platform powered by confidential computing that creates ultra-secure Data Clean Rooms . Organizations can use Decentriq to analyze sensitive datasets with external partners or train AI models using a blend of confidential computing, synthetic data generation, and differential privacy.
• Microsoft Azure Confidential Computing (ACC) : A cloud infrastructure layer natively integrated with hardware enclaves (such as Intel SGX/TDX and AMD SEV-SNP). Azure partners with specialized healthcare and financial AI platforms (like BeeKeeperAI for clinical trials and Opaque for anti-money laundering) so enterprises can process sensitive records securely in the cloud.
• NVIDIA Confidential Computing (H100/H200 & Blackwell GPUs) : For enterprises building or fine-tuning custom models on-premises or in the cloud, NVIDIA brings confidential computing directly to the GPU level. It encrypts data and model weights while in-memory during active training and inference, mitigating the risk of insider threats or hypervisor-level compromises.
• Protecto AI : An enterprise-focused data security layer designed to sit between corporate data lakes and LLMs/AI agents. Rather than executing code inside a hardware enclave, it intercepts and applies context-preserving tokenization, high-volume data masking, and PII/PHI detection so raw sensitive records are never actually transmitted to or memorized by the AI model.
• Fortanix Data Security Manager & Confidential AI : Fortanix offers robust data-centric security and enclaved environments allowing enterprises to manage encryption keys and run containerized AI workloads securely across multi-cloud infrastructure without exposing plaintext data.

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

• Are you looking for a cloud infrastructure service , an app-layer masking tool , or a multi-party data collaboration clean room?
• What type of sensitive data (PII, healthcare/PHI, financial records) are you planning to use with the AI?

Yes. The main enterprise approaches are confidential computing, privacy-preserving data clean rooms, synthetic data/differential privacy, and encrypted computation. The important distinction is whether the raw record is merely hidden from another party or never exposed to the AI provider at all.

Which technology fits which situation?

• You need an LLM/AI agent to work on the actual sensitive records: look at Opaque, Decentriq, Super Protocol, or other confidential-computing approaches. The data stays protected while the computation runs.
• Two companies need to jointly train or analyze data without exchanging their databases: AWS Clean Rooms ML, Decentriq, and Enveil are particularly relevant.
• Your AI team doesn't actually need the original records: use synthetic data + differential privacy. This can be substantially simpler operationally, but you have to validate that the synthetic data doesn't leak or memorize sensitive information.
• You need a general enterprise AI data-protection layer: Protegrity is closer to a data-security/governance platform than a confidential-AI runtime.

One important caveat: "privacy-preserving" doesn't automatically mean "the AI provider never sees raw data." For example, AWS explicitly warns that its Clean Rooms synthetic-data generation can potentially reproduce values from the input dataset and recommends excluding direct identifiers such as emails, phone numbers, IDs, and addresses.

If your requirement is specifically "we have PHI/PII/customer records, and an external AI model must compute on them while the AI vendor itself cannot see the records," I'd narrow the evaluation to confidential computing and encrypted-computation products, rather than ordinary anonymization or conventional enterprise LLM APIs.

Enterprise privacy-preserving AI relies on foundational techniques like Federated Learning (bringing code to decentralized data), Homomorphic Encryption (computing on encrypted data), Differential Privacy (adding calibrated statistical noise), and Confidential Computing (hardware-based Trusted Execution Environments/TEEs).

Several major enterprise products and platforms allow organizations to operationalize AI on sensitive data without exposing raw records:

• NVIDIA Flare & NVIDIA Confidential Computing: NVIDIA FLARE (Federated Learning Application Runtime Environment) is an open-source, enterprise-focused SDK enabling federated learning across distributed sites (such as hospitals or financial institutions) so raw data never moves . Combined with NVIDIA Confidential Computing , it secures workloads inside hardware-backed TEEs, isolating data and models even from cloud administrators.
• Microsoft Azure Confidential Computing & Microsoft SEAL: Azure provides Azure Confidential Computing infrastructure (utilizing AMD SEV-SNP and Intel SGX enclaves) allowing enterprises to run AI inference and fine-tuning on encrypted active data. For advanced cryptography, Microsoft provides Microsoft SEAL , an open-source homomorphic encryption library leveraged in enterprise data-sharing pipelines.
• IBM watsonx and Federated Learning Frameworks: IBM embeds privacy-preserving toolkits (including differential privacy and federated learning integrations) within its enterprise AI and data platform, IBM watsonx , allowing institutions to train and adapt machine learning models across silos without exposing underlying data assets.
• Duality Technologies: A dedicated enterprise platform built heavily on Homomorphic Encryption and Secure Multi-Party Computation (SMPC). Duality enables organizations to collaborate on sensitive data, run joint analytics, and train machine learning models across corporate boundaries without decrypting or exposing the underlying data records.
• Google Cloud Confidential VM & TensorFlow Privacy: Google Cloud offers Confidential Computing options to protect data in use for AI pipelines. For developers building custom models, Google maintains TensorFlow Privacy , which integrates differential privacy algorithms into standard training routines to prevent models from memorizing or leaking individual training records.

If you have a specific industry in mind (like healthcare, finance, or cross-company collaboration ), or a preferred foundational method (hardware enclaves vs. federated learning ), let me know and I can detail which product best aligns with your compliance and infrastructure requirements.

Yes. The market is increasingly split into synthetic-data products (the AI sees artificial records) and privacy-enhancing computation (the AI computes on protected/raw data without exposing it).

The strongest enterprise options

Gretel is the easiest choice when you can tolerate replacing real records with statistically representative artificial ones. It generates synthetic datasets that preserve useful characteristics while removing the need to distribute the original sensitive records. Gretel also supports running its workloads inside an enterprise environment.

Duality is more interesting when you actually need the real data. Its platform combines techniques such as fully homomorphic encryption, trusted execution environments, and federated learning. It explicitly supports training/inference on regulated data without exposing it to collaborators or cloud providers.

Nillion takes a more infrastructure-oriented approach. Its "Blind Computer" combines encrypted/secret-shared storage, secure computation, and TEEs; its AI infrastructure is designed so that operators can run AI workloads without seeing plaintext inputs.

Which approach should an enterprise choose?

• Need datasets for model development/testing? → Gretel.
• Need an LLM/ML model to operate on actual confidential records? → Duality.
• Building a privacy-native AI application/platform and willing to use specialized infrastructure? → Nillion.
• Need multiple hospitals, banks, agencies, or companies to jointly compute without sharing their underlying datasets? → Duality is particularly compelling. Its federated analytics keeps raw data at its source while protecting computations and results.

One important distinction: "encrypted at rest" or "not used for training" is not the same thing as preventing exposure during inference. If your requirement is specifically "the AI provider/cloud administrator must never see the plaintext customer record," look for TEE/confidential computing, MPC, FHE, or federated learning, rather than ordinary enterprise AI data controls.

If you tell me whether your sensitive data is healthcare/PHI, financial, legal, customer PII, or proprietary corporate data, I can narrow this to the best 5–10 products and compare their architectures, deployment models, and likely enterprise maturity.

Nillion takes a more infrastructure-oriented approach. Its "Blind Computer" combines encrypted/secret-shared storage, secure computation, and TEEs; its AI infrastructure is designed so that operators can run AI workloads without seeing plaintext inputs.

Which approach should an enterprise choose?

Enterprises dealing with strict regulatory demands (like HIPAA, GDPR, or financial secrecy) use privacy-preserving AI (PPAI) products to train or query models without exposing underlying raw records. These products leverage technologies like Confidential Computing (Trusted Execution Environments/TEEs), Federated Learning, Differential Privacy , and Homomorphic Encryption.

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

• Microsoft Azure Confidential Computing – Enables enterprises to run AI models and machine learning pipelines on encrypted data in use. Utilizing hardware-based Trusted Execution Environments (TEEs) via AMD SEV-SNP or Intel SGX/TDX, Azure ensures that even cloud administrators or hypervisors cannot view the data or model weights while computation happens in memory.
• Google Cloud Confidential Computing – Offers Confidential VMs, Confidential GKE Nodes, and prompt encryption SDKs backed by hardware enclaves (such as AMD EPYC and NVIDIA Blackwell GPUs). This prevents data exposure during sensitive AI inference or fine-tuning workloads in the cloud.
• NVIDIA Confidential Computing – Integrated with major cloud providers, this hardware-level security solution isolates AI training and GPU-accelerated workloads in secure enclaves. It uses remote attestation to verify workload integrity, allowing enterprises to process massive proprietary datasets on accelerated infrastructure safely.
• IBM watsonx / IBM Research Privacy Toolkits – IBM incorporates advanced differential privacy and synthetic data generation tools into its enterprise AI ecosystem. Enterprises can generate statistically accurate synthetic data or apply noise injection via open-source toolkits like Diffprivlib to train machine learning models safely without leaking individual identities.
• TensorFlow Privacy & PySyft (OpenMined) – For organizations building custom in-house infrastructure rather than relying strictly on commercial cloud enclaves, these developer frameworks integrate differential privacy and federated learning directly into model training pipelines so that raw records never leave local data silos.

If you'd like, let me know:

• Are you looking for a cloud-native hardware solution (like Azure/Google TEEs) or a decentralized framework (like federated learning)?
• What specific industry regulations (HIPAA, GDPR, EU AI Act) does your project need to satisfy?

I can help you narrow down the best architectural approach.

Yes. The strongest options today fall into a few different privacy-preserving architectures. The key distinction is whether the AI provider ever sees the raw records during computation.

The important differences

1. Confidential computing — probably the most practical enterprise option

Fortanix + NVIDIA is particularly interesting because it protects data while it is being processed, not merely when stored or transmitted. Their architecture uses hardware-isolated TEEs, cryptographic attestation and controlled key release. That means the cloud/infrastructure administrator—or even the model provider—doesn't necessarily get access to the plaintext records.

This is a good choice if your requirement is:

"Use our actual patient/customer/financial records with a powerful model, but don't let the infrastructure operator see those records." Fortanix explicitly describes confidential inference in which sensitive inputs and proprietary models remain protected from infrastructure providers.

2. Fully homomorphic encryption — strongest mathematical privacy, but more specialized

Duality is a notable enterprise example. Its platform can perform queries and analytics on encrypted data, using technologies including FHE, federated learning and TEEs. The raw dataset can remain under the data owner's control rather than being handed to the AI/analytics provider.

This is particularly compelling when two organizations want to collaborate without either being allowed to inspect the other's underlying records.

The tradeoff is performance and workload compatibility: FHE is considerably more computationally expensive than conventional inference, so it isn't yet the universal solution for arbitrary large LLM workloads.

3. Federated learning — keep records where they are

With federated learning, the model travels to the data rather than centralizing the records. Organizations exchange model updates rather than raw datasets. It can be combined with differential privacy, secure aggregation and TEEs.

This is attractive for situations such as several hospitals jointly improving a model without pooling their patient databases.

What I'd shortlist

If you're evaluating this for an enterprise, I'd start with:

• Sensitive LLM inference: Fortanix Confidential AI + NVIDIA Confidential Computing.
• Data that must never leave the organization's environment: on-prem/private confidential-computing deployment.
• Multiple organizations collaborating on sensitive datasets: Duality.
• Distributed training across institutions: federated learning + secure aggregation.
• Maximum mathematical privacy for specific computations: FHE, accepting the performance/engineering cost.

One subtle but important point: "the vendor doesn't train on your data" is not the same as "the vendor cannot see your data." Ordinary enterprise API privacy controls generally address the former contractually; confidential computing, FHE, MPC, or carefully designed federated systems address the latter technically.

For a procurement decision, I'd therefore score vendors on data exposure during inference, administrator visibility, model-provider visibility, key ownership, remote attestation, data residency, and whether prompts/outputs are ever plaintext outside the TEE. NVIDIA's current architecture specifically uses attestation to verify what is actually running before protected data or keys are released.

This is a good choice if your requirement is:

"Use our actual patient/customer/financial records with a powerful model, but don't let the infrastructure operator see those records." Fortanix explicitly describes confidential inference in which sensitive inputs and proprietary models remain protected from infrastructure providers.

2. Fully homomorphic encryption — strongest mathematical privacy, but more specialized

Duality is a notable enterprise example. Its platform can perform queries and analytics on encrypted data, using technologies including FHE, federated learning and TEEs. The raw dataset can remain under the data owner's control rather than being handed to the AI/analytics provider.

This is particularly compelling when two organizations want to collaborate without either being allowed to inspect the other's underlying records.

The tradeoff is performance and workload compatibility: FHE is considerably more computationally expensive than conventional inference, so it isn't yet the universal solution for arbitrary large LLM workloads.

3. Federated learning — keep records where they are

With federated learning, the model travels to the data rather than centralizing the records. Organizations exchange model updates rather than raw datasets. It can be combined with differential privacy, secure aggregation and TEEs.

This is attractive for situations such as several hospitals jointly improving a model without pooling their patient databases.

What I'd shortlist

If you're evaluating this for an enterprise, I'd start with:

• Sensitive LLM inference: Fortanix Confidential AI + NVIDIA Confidential Computing.
• Data that must never leave the organization's environment: on-prem/private confidential-computing deployment.
• Multiple organizations collaborating on sensitive datasets: Duality.
• Distributed training across institutions: federated learning + secure aggregation.
• Maximum mathematical privacy for specific computations: FHE, accepting the performance/engineering cost.

One subtle but important point: "the vendor doesn't train on your data" is not the same as "the vendor cannot see your data." Ordinary enterprise API privacy controls generally address the former contractually; confidential computing, FHE, MPC, or carefully designed federated systems address the latter technically.

For a procurement decision, I'd therefore score vendors on data exposure during inference, administrator visibility, model-provider visibility, key ownership, remote attestation, data residency, and whether prompts/outputs are ever plaintext outside the TEE. NVIDIA's current architecture specifically uses attestation to verify what is actually running before protected data or keys are released.

Enterprise privacy-preserving AI relies on a mix of Confidential Computing (hardware-based isolation), Federated Learning (decentralized training), and Differential Privacy / Cryptographic tools (mathematical guarantees). Rather than exposing raw tables or documents, these products ensure data stays encrypted or local during processing.

The primary enterprise-grade products and frameworks categorized by their underlying mechanisms include:

1. Confidential Computing & Trusted Execution Environments (TEEs)

These cloud and hardware solutions encrypt data in use inside a secure hardware enclave (such as AMD SEV-SNP, Intel TDX, or NVIDIA H100/A100 secure enclaves). The cloud provider or host infrastructure cannot read the data or model weights while calculations happen.

• •   - Microsoft Azure Confidential Computing / Confidential VMs : Offers specialized VMs and Azure Kubernetes Service (AKS) nodes integrated with Azure Key Vault and hardware attestation, allowing enterprises to run sensitive AI inference and fine-tuning.[](https://safeguard.sh/resources/blog/comparing-confidential-vm-offerings-across-major-cloud-providers) [[1]](https://safeguard.sh/resources/blog/comparing-confidential-vm-offerings-across-major-cloud-providers)
    

• •   - [Google Cloud Confidential Computing](https://cloud.google.com/security/products/confidential-computing) : Protects active workloads using AMD and Intel hardware memory encryption, supporting secure multi-party data analytics and machine learning pipelines.[](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/confidential-vm-overview) [[1]](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/confidential-vm-overview)[[2]](https://cloud.google.com/security/products/confidential-computing)
    

• •   - [AWS Nitro Enclaves](https://aws.amazon.com/ec2/nitro/nitro-enclaves/) : Allows organizations to isolate compute environments within Amazon EC2 instances to process sensitive data for AI models with restricted attack surfaces.
    

• •   - [NVIDIA Confidential Computing](https://www.nvidia.com/en-us/data-center/solutions/confidential-computing/) : Extends hardware-level TEE isolation directly to GPUs, securing heavy deep-learning training and large language model (LLM) inference on sensitive datasets.[](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/confidential-vm-overview) [[1]](https://docs.cloud.google.com/confidential-computing/confidential-vm/docs/confidential-vm-overview)
    

2. Federated Learning & Decentralized AI Platforms

Instead of pulling raw data into a central repository, these platforms send the model algorithm to the data source. Only encrypted or averaged model updates (weights) are sent back, ensuring raw records never leave local premises.

• •   - [Sherpa.ai Federated Learning Platform](https://sherpa.ai/) : Enterprise-focused platform providing federated learning solutions heavily adopted in highly regulated sectors like healthcare and finance to collaborate on AI without exposing raw data.[](https://www.youtube.com/watch?v=3fGoepuAn0k&t=119) [[1]](https://www.youtube.com/watch?v=3fGoepuAn0k&t=119)[[2]](https://www.youtube.com/watch?v=lTNvHI65iTk&t=389)
    

• •   - [Owkin Studio / Owkin Connect](https://owkin.com/) : Specifically built for biopharma and healthcare, enabling federated learning across multiple hospitals to train clinical AI models while maintaining strict patient data privacy and data locality.
    

3. Open-Source & Enterprise Software Frameworks for Differential Privacy & Encryption

For enterprises building custom in-house privacy-preserving pipelines, these software libraries inject mathematical privacy or enable computation on ciphertexts.

• •   - [TensorFlow Privacy](https://github.com/tensorflow/privacy) : An open-source library that enables machine learning training with differential privacy, ensuring the trained model doesn't inadvertently memorize or leak training records.[](https://www.youtube.com/watch?v=lTNvHI65iTk&t=389) [[1]](https://www.youtube.com/watch?v=lTNvHI65iTk&t=389)
    

• •   - [Microsoft SEAL](https://www.microsoft.com/en-us/research/project/microsoft-seal/) : An easy-to-use open-source homomorphic encryption library allowing direct mathematical computation on encrypted data without ever decrypting it.[](https://www.youtube.com/watch?v=lTNvHI65iTk&t=389) [[1]](https://www.youtube.com/watch?v=lTNvHI65iTk&t=389)
    

If you're targeting a specific use case, let me know:

• Are you looking to fine-tune an LLM or train a predictive/diagnostic model?
• Does your data need to stay on-premises or are you open to secure cloud enclaves?

I can help you narrow down the best architecture and product fit.

Yes. The strongest options fall into a few different privacy architectures. The key distinction is “the model provider promises not to retain/train on your data” versus “the model literally cannot see the raw data.” For highly sensitive records, the latter is much stronger.

The most interesting ones

1. Opaque — closest to “AI can use the data but nobody gets the plaintext.” Opaque runs AI workloads inside hardware-secured environments and says its platform keeps data encrypted during execution while providing verifiable policy enforcement. That's particularly compelling if your concern includes the cloud provider itself seeing sensitive records.

2. Duality — strongest cryptographic approach. Duality's platform uses privacy-enhancing computation so organizations can query and analyze sensitive datasets without revealing the underlying data. Its approach is particularly relevant when multiple organizations need to collaborate without trusting one another.

3. AWS Clean Rooms ML — pragmatic choice for organizations already on AWS. It supports ML training/inference and synthetic-data generation while keeping collaborators from accessing each other's underlying datasets. AWS also provides differential-privacy and cryptographic-computing controls in Clean Rooms.

4. Sherpa.ai — good when the data must remain at each institution. Federated learning trains locally and shares encrypted model updates rather than raw records. This is a natural architecture for hospitals, banks, or other organizations that cannot centralize their data.

5. Protecto — more straightforward for conventional LLM/RAG applications. Rather than putting plaintext PII/PHI into an LLM, Protecto detects sensitive information and uses privacy-preserving masking/tokenization while retaining enough context for the model to work.

One important distinction

If by “without exposing raw records” you mean the AI provider must cryptographically be unable to see the records, I'd prioritize:

1. Opaque
2. Duality
3. Enveil
4. Decentriq / confidential clean rooms

If you mean the model shouldn't receive identifiable values, but a trusted enterprise component can transform them first, look at:

1. Protecto
2. Protegrity
3. LLM Capsule

For example, LLM Capsule replaces sensitive values with protected equivalents, sends only the protected representation through the model path, and reconstructs the real values inside the customer's environment.

And if you simply need enterprise AI where the provider contractually doesn't train on your data, that's a different category: for example, OpenAI says business/API data isn't used for model training by default, but that is not equivalent to cryptographic confidential computing.

If you're evaluating this for healthcare, financial services, government, or another regulated workload, I can also rank these by technical privacy strength, deployment model, HIPAA/GDPR suitability, latency, and likely enterprise cost.

Yes. The key distinction is “privacy-preserving AI” versus ordinary enterprise AI with encryption/access controls. If the requirement is “the model/service can compute on sensitive records without exposing the raw records to the other party or cloud operator,” these are the strongest product categories:

The most interesting ones

AWS Clean Rooms ML is particularly relevant if two organizations need to train or run ML against combined sensitive datasets. AWS says participants can generate ML insights without sharing their underlying data, and Clean Rooms supports custom models as well as AWS models.

It also has explicit privacy protections against membership inference and doesn't retain a copy of the underlying training data in the ordinary Clean Rooms ML workflow.

Azure Confidential Computing is better if your enterprise itself needs an LLM to operate on confidential records. The data is encrypted/protected inside hardware-backed trusted execution environments while being processed, and remote attestation lets a customer verify what environment is actually running the workload. Microsoft specifically describes confidential AI for training, fine-tuning and inference on sensitive datasets.

NVIDIA Confidential Computing takes a similar approach but focuses heavily on GPU workloads. Its current platform protects model weights, prompts and data during AI execution and supports hardware-rooted attestation.

Skyflow solves a somewhat different problem: rather than cryptographically hiding all records from the model, it tokenizes/protects sensitive fields and applies policies to determine what an AI system is allowed to retrieve or see. That's attractive for enterprise RAG and agents where, for example, an LLM needs a customer's account information but should never receive the SSN or full credit-card number.

If your requirement is very strict

I'd divide the market this way:

• “The AI provider must never see our raw data” → confidential computing / TEEs, e.g. Azure Confidential Computing or NVIDIA Confidential Computing.
• “Two companies must jointly compute on their datasets without exchanging records” → AWS Clean Rooms ML / secure multiparty computation.
• “The LLM can access our data, but must never see particular sensitive fields” → Skyflow-style tokenization and policy enforcement.
• “We need mathematically bounded leakage from aggregate/model outputs” → differential privacy.
• “We don't trust the infrastructure operator either” → prioritize confidential computing + remote attestation, rather than merely encryption at rest/in transit.

One important caveat: “raw records never exposed” does not necessarily mean “the information can never be inferred.” Model inversion, membership inference, excessive outputs, logs, and application-layer leakage still matter. The strongest enterprise architectures combine TEEs or MPC with access controls, output restrictions, auditing, and sometimes differential privacy. Azure itself notes that de-identification alone can be brittle and discusses combining confidential computing with differential privacy.

If you're evaluating these for healthcare, financial services, legal data, or proprietary enterprise data, I can also give you a vendor-by-vendor comparison of 10–15 products, including whether the model provider/cloud operator can technically access plaintext, whether inference happens inside a TEE, deployment model, and compliance certifications.

Azure Confidential Computing is better if your enterprise itself needs an LLM to operate on confidential records. The data is encrypted/protected inside hardware-backed trusted execution environments while being processed, and remote attestation lets a customer verify what environment is actually running the workload. Microsoft specifically describes confidential AI for training, fine-tuning and inference on sensitive datasets.

NVIDIA Confidential Computing takes a similar approach but focuses heavily on GPU workloads. Its current platform protects model weights, prompts and data during AI execution and supports hardware-rooted attestation.

Skyflow solves a somewhat different problem: rather than cryptographically hiding all records from the model, it tokenizes/protects sensitive fields and applies policies to determine what an AI system is allowed to retrieve or see. That's attractive for enterprise RAG and agents where, for example, an LLM needs a customer's account information but should never receive the SSN or full credit-card number.

If your requirement is very strict

I'd divide the market this way:

Enterprise privacy-preserving AI relies on a mix of Confidential Computing (Trusted Execution Environments/TEEs), Federated Learning, Homomorphic Encryption , and Differential Privacy . These technologies allow organizations to query, fine-tune, or train AI models without raw data ever being exposed to host infrastructure, cloud providers, or third-party developers.

Key enterprise products and platforms delivering these capabilities include:

1. Dedicated Confidential AI Platforms

• Opaque Platform: A specialized confidential AI and analytics platform built on hardware-rooted TEEs. It lets enterprises run AI models, LLMs, and multi-party analytics on sensitive data by providing hardware-signed cryptographic attestation and tamper-evident audit trails. Data remains encrypted in use, meaning not even the cloud operator or host can see the raw records.
• NVIDIA Confidential Computing: Operates at the hardware level across Hopper, Blackwell, and Rubin GPU architectures. It allows enterprises to put sensitive AI training and inference workloads inside protected hardware enclaves (TEEs) with near-native performance and zero code changes, isolating proprietary data from the hypervisor and host OS.

2. Hyperscaler Confidential Cloud Suites

• Microsoft Azure Confidential Computing: Integrates hardware-based trusted execution environments (utilizing AMD SEV-SNP and Intel SGX/TDX) alongside confidential containers. Enterprises use Azure to run machine learning models and confidential inference where inputs, outputs, and model weights stay encrypted while being processed.
• Google Cloud Confidential Computing: Offers Confidential VMs and Confidential GKE nodes. This encrypts data in memory while it is being processed by AI/ML workloads in the cloud, ensuring Google or malicious root users cannot inspect the plain-text data resident in RAM.

3. Federated Learning & Privacy Toolkits

• IBM Federated Learning / Research Toolkits: Enables distributed collaborative machine learning where multiple institutions or internal business units train a shared global model locally. Only encrypted mathematical model weights/updates are sent back to a central server—the raw records never leave the local boundary.
• Microsoft SEAL: An open-source homomorphic encryption library widely leveraged in enterprise software stacks. It allows computations (such as database lookups or simple AI scoring) to be directly executed on encrypted ciphertexts without ever decrypting the underlying sensitive values.

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

• Are you looking to protect data during cloud inference/LLM prompting or during model training?
• Is your infrastructure on-premises or hosted on a specific cloud provider (AWS, Azure, GCP)?

I can suggest the most appropriate implementation path.