This is a guest post for the Computer Weekly Developer Network written by Jay Harel, VP of Product at Opaque Systems – Opaque is a confidential computing platform that enables secure data sharing, multi-party analytics and machine learning on encrypted data.
Harel writes as follows…
The development of autonomous software has made significant strides in recent years.
However, the ability to process sensitive data in a secure and privacy-preserving manner and address data privacy, data security and intellectual property protection is essential for the continued advancement of autonomous software. Data privacy is critical for autonomous software using sensitive data, such as personal information or financial data. Autonomous systems that process sensitive data must comply with data privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union, which require companies to protect personal data privacy. Failure to comply with these regulations could result in significant financial penalties, damage to a company’s reputation and loss of customer trust.
Data security is another significant concern for autonomous software.
The risk of data breaches, theft, or unauthorised access to sensitive data is a constant threat to autonomous systems. Companies must be confident that any autonomous implementation has robust data security measures to protect sensitive data, including encryption, access control and secure data storage and transmission protocols.
IP, you see?
Intellectual Property (IP) protection is also critical for autonomous software that uses proprietary data, such as trade secrets or confidential business information. Companies must protect their proprietary data from unauthorised access or theft. Failure to protect this data could result in significant financial losses, loss of competitive advantage and damage to a company’s reputation.
As autonomous software moves from industry-specific areas to more complete intelligent applications, the ability to work with and protect sensitive and confidential information will be a critical differentiator and factor for success.
Applications in many areas require processing sensitive data, from financial service applications that use sensitive financial data for fraud detection and risk assessment to healthcare applications that use sensitive patient data for disease diagnosis, drug discovery and personalised medicine. Any industry application using automated software that processes sensitive data must be highly secure to prevent data breaches and leakage.
As the advancement of autonomous software continues to evolve, the autonomous stack will increasingly rely on privacy-enhancing technologies (PETs) and confidential computing to provide complete end-to-end security for the entire data processing pipeline, from data ingestion to output.
The future of the autonomous software stack depends on the ability to process sensitive data securely so that multiple parties can collaborate on said data to solve major industry challenges without compromising its privacy or security.
The major advances we’re seeing in healthcare, finance, manufacturing and energy and the amount of data we have at our disposal make automation a viable option, but organisations must ensure privacy and security are not compromised or risk paying the price, both in fines and in customer trust (or lack thereof).