The article also explores how data-in-use encryption can protect patient data in healthcare organizations, allowing them to securely collaborate and use AI and machine learning for better patient outcomes. It discusses the need for the encryption community to develop quantum-resistant algorithms in response to the potential challenges of quantum computing. The article concludes by stressing the importance of a privacy-centered strategy in the age of generative AI programs, particularly in the healthcare sector where patient data privacy is regulated.
Key takeaways:
- Data-in-use encryption is a powerful tool for protecting sensitive data in sectors such as financial services, healthcare, and defense. It acts as a shield for data during active use, much like the protective force fields in sci-fi movies.
- Healthcare organizations can use data-in-use encryption to secure large volumes of sensitive patient data. This technology allows them to collaborate securely, embark on new initiatives, and leverage AI and machine learning for better patient outcomes.
- The cybersecurity landscape is dynamic and constantly evolving with new threats and sophisticated forms of cyberattacks. Data-in-use encryption has the capacity to adapt and evolve to combat these threats, including the potential challenges of quantum computing.
- Generative AI programs present new challenges, especially in the healthcare sector where patient data privacy is regulated. Data-in-use encryption, combined with robust privacy strategies and policies, can help healthcare organizations harness the power of generative AI safely.