Uploading Internal Data to Chatgpt or Gemini: A Data Governance Nightmare for Businesses

Today’s world of big data has shown businesses are hungry for insights to fuel innovation and better decision-making and there are no doubt large language models (LLMs) like ChatGPT and Gemini represent an exciting leap forward in artificial intelligence. Their ability to analyze information, answer questions, and generate creative insights opens doors for innovation and productivity across various fields.

However, the current state of LLM technology presents significant challenges when it comes to data governance, making them potentially unsuitable for handling a company's internal data. Uploading a company's internal data to these platforms can be a recipe for disaster from a data governance standpoint.

The primary concern lies in the area of data security and privacy. Once a company uploads its data, it relinquishes a significant degree of control over its security. These LLM platforms, while powerful, have varying levels of security measures. This lack of transparency makes it difficult to guarantee the protection of sensitive information, especially if it contains confidential details or personally identifiable data about employees or customers.

Data breaches are also a constant threat in the tech world, and LLMs are still under development. Uploading sensitive data creates a vulnerability where unauthorized personnel could potentially access or leak confidential information.

Additionally, companies are legally obligated to comply with data privacy regulations like the General Data Protection Regulation (GDPR) or Nigeria's Data Protection Regulation (NDPR). Uploading sensitive data to an LLM platform might violate these regulations, putting the company at risk of fines and reputational damage.

Data governance is about more than just security. It also encompasses data integrity and transparency. LLMs rely on complicated algorithms that are frequently kept secret. Once data is uploaded, it becomes difficult to understand how the LLM is using, manipulating, or potentially introducing bias into the information. This lack of transparency makes it challenging to audit the data and hold the LLM platform accountable for its actions.

Furthermore, tracking the origin and changes made to the data becomes a complex task. Data provenance (data lineage), which is the ability to trace the history of data from its origin to its current state, becomes blurry. This lack of clarity makes it difficult to ensure the accuracy and reliability of the information being used by the LLM.

There are safer and more secure alternatives for businesses seeking to leverage the power of LLMs. Companies can invest in on-premises LLM solutions that provide them with more control over data security and privacy. On-premises solutions keep the data physically located within the company's infrastructure, minimizing the risks associated with external access.

Another option is to utilize synthetic data generation. This technique involves creating artificial data that replicates the structure and characteristics of real-world data but without the inclusion of sensitive information. Synthetic data can be a secure alternative for using LLMs while protecting confidential company data.

Finally, federated learning offers a collaborative approach where multiple parties can train an LLM on their data without ever having to share the underlying information itself. This approach allows businesses to benefit from the collective intelligence of the model while keeping their sensitive data private.

Although these platforms provide exciting opportunities for personal exploration and creative endeavours, their limitations in data governance make them a risky proposition for handling official company data. Businesses should prioritize the security, privacy, and integrity of their information.

In conclusion, LLMs offer a powerful tool for data analysis and insights generation, uploading a company's internal data to these platforms can be a serious breach of data governance principles. The risks associated with data security, privacy, integrity, and transparency are simply too great and by exploring alternative solutions like on-premises LLMs, synthetic data generation, or federated learning, companies can harness the potential of LLMs while ensuring the security and responsible use of their valuable data assets.