Training AI on Private Data – Risks, Rewards and Recommendations

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More companies want to tap into their proprietary data troves to develop tailored, high-performance AI models. But training models on private data carries potential risks around data security, unfair bias, and legal compliance. Here we explore best practices for private AI training.

The Promise and Perils of Private Training Data

Using confidential business data like customer info, financial records, user content and more to train AI models can have advantages:

  • Models can learn nuances specific to your business domain
  • Leverage data that competitors don’t have access to
  • Improve performance on core tasks with in-house data

However, it also has dangers:

  • Exposing private data during the ML training process
  • Models inadvertently encoding biases, discrimination
  • Violating regulations around using certain data types

What do biases in AI look like?

One of the best examples I have seen was when a user asked for ChatGPT to write about specific jobs (doctor and nurse), in the responses ChatGPT made the assumption that the doctor was a man and that the nurse was a woman. This is a very basic example but I found it very interesting that it was the case and the ramifications of these biases can be catastrophic to a business.

Recommendations for Keeping Private Data Secure

When training AI on private data, rigorous measures are needed to mitigate risks:

  • Anonymize data by removing personally identifiable information
  • Build secure enclaves with encrypted storage for data
  • Vet all data used for unfair bias with testing suites
  • Control access to data with role-based permissions
  • Use differential privacy techniques when sharing model insights
  • Overall, limit exposure of the data as much as possible and perform ethical reviews.

Navigating Data Regulations and Compliance

Depending on your industry, regulations like HIPAA (healthcare) and GDPR (Europe) have strict requirements around using private data to train AI. Strategies include:

  • Conduct a legal review of all training data used
  • Ensure opt-in consent where required
  • Carefully evaluate use of protected class data
  • Consult regulators early in development process
  • Document data sources and compliance procedures
  • Though compliance may constrain some use cases, it is essential to avoid legal jeopardy.

Why Trusted AI Providers Are Key

For most companies, it is prudent to partner with an AI vendor who can ensure:

  • End-to-end data encryption
  • Ethical and secure AI practices
  • Compliance experts on staff (unlikely, that’s why we are here)
  • Audit trails on data sourcing and access

This lifts the burden while still benefiting from private data’s advantages. Training AI responsibly on private data is challenging but possible. With the right strategic partners and diligent practices, companies can tailor models to their needs while protecting people and themselves. 

Options for training custom large language models (LLMs) on private data:

Anthropic’s Constitutional AI (one of our favorites)

  • Trains custom LLMs tailored to your data
  • Focused on privacy and security
  • Pricing not publicly listed, enterprise focused
  • https://www.anthropic.com

Cohere’s Custom Models

Google Cloud AI Platform

Amazon SageMaker

Microsoft’s Azure Open AI Service

NVIDIA Base Command

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