What are the potential drawbacks of using cloud-based AI systems?

1 Data security

One of the most critical issues of using cloud-based AI systems is data security. When you store and process your data in the cloud, you are entrusting it to a third-party provider, who may have different policies and practices than you. You also expose your data to potential breaches, leaks, or attacks from hackers, competitors, or malicious actors. To protect your data, you need to ensure that your cloud provider has strong encryption, authentication, and authorization mechanisms, and that you comply with the relevant data protection regulations and standards.

2 Data privacy

Another challenge of using cloud-based AI systems is data privacy. When you use cloud-based AI services, such as machine learning, natural language processing, or computer vision, you may need to share sensitive or personal data with the provider, such as customer information, health records, or biometric data. This may raise ethical and legal concerns, especially if the provider uses your data for other purposes, such as advertising, profiling, or research. To safeguard your data privacy, you need to understand the terms and conditions of the provider, and obtain the consent of the data subjects, if required.

3 Data quality

A third drawback of using cloud-based AI systems is data quality. When you use cloud-based AI services, you may not have full control over the quality and accuracy of the data that the provider uses to train and run the AI models. You may also face challenges in validating and verifying the results and outputs of the AI models, especially if they are complex or opaque. To ensure data quality, you need to establish clear criteria and standards for the data that you use and receive from the provider, and monitor and evaluate the performance and reliability of the AI models.

4 Data sovereignty

A fourth challenge of using cloud-based AI systems is data sovereignty. When you use cloud-based AI services, you may not know where your data is stored and processed, and which jurisdiction it falls under. This may create legal and regulatory conflicts, especially if your data crosses borders or regions with different laws and rules. For example, you may face issues with data localization, data transfer, or data retention requirements. To address data sovereignty, you need to select a cloud provider that has data centers in the locations that you operate in, and that complies with the relevant laws and regulations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?