There’s good reason for data transfer laws in terms of personal and business privacy as well as national security reasons. Consider that in all regulated industries, vast amounts of data are regularly collected, processed, and stored, including personal information, as well as call logs, messages, and location data. This data could be used to identify individuals, follow their behaviors and movements, and their communication activities. While most of the time the customer data is used simply for the business intent, it can become a prime target for cybercriminals looking to steal personal information for malicious reasons.
How to address the data transfer challenges
While there can be many challenges, in general, when it comes to sharing data with outsourcers working on your critical data analytics, AI, or other IT projects, the benefits of outsourcing may be well worth the hassle. At a time when tech talent shortages continue and technology is becoming increasingly more complex but critical to business growth, outsourcing IT projects can be the best solution to accelerate digital transformation more cost-effectively and efficiently.
With a greater understanding of data transfer requirements, here are best practices to ensure compliance, as well as a successful outsourcing partnership.
Do your research. When considering outsourcing as a viable model, conduct extensive research and fully understand the requirements and regulations of your industry, your country, and the countries where you may be doing business. Often, legal teams and consultants can be good partners to help you ensure compliance.
Consider the nearshore approach. When transferring data outside the country is not an option, a good way to outsource IT projects is to work with an outsourcing partner in your own country. Nearshoring to Puerto Rico, for example, provides U.S. organizations with the best of both worlds: world-class IT experts and an outsourcing partner to handle your projects that follows all the same rules and regulations, since it’s a U.S. territory.
Use synthetic data for data-driven projects. For machine learning and other AI projects that require lots of datasets, synthetic data that is artificially generated helps you avoid data-privacy issues, since it does not use real-world personal data but still can be used to effectively train solutions. It’s important to note, however, that while development can be done with synthetic data, with many regulated industries, testing and implementation must be done within the U.S.
Ask the right questions of your outsourcing candidates. Before selecting an outsourcing partner, you should be aware of your data transfer regulations and restrictions, and if you’re able to partner with the firm, make sure to fully understand its data protection policies and protocols. Inquire about where they store data, how they manage data, and, equally as important, how they destroy data when the project ends. Also ask how staff is trained on data protection best practices. At Wovenware, we create virtual machines to manage and process data and develop customer solutions. Once the project ends, those virtual machines and backup systems are destroyed.
The effective transfer, flow, and accumulation of relevant data is essential to the development of today’s digital solutions, but data transfer regulations and industry compliance requirements can put a halt to many forms of outsourcing. It pays to arm yourself with an understanding of your country, state, or industry’s laws and create your own data transfer and privacy policies and best practices to preserve the integrity of data—the true lifeblood to effective AI and software.