Digital ethics sits at the peak of the Gartner Hype Cycle for Privacy, 2021, as people become increasingly aware of the value of their personal information and frustrated by lack of transparency and continuing misuse.
Gartner defines digital ethics as the systems of values and moral principles for the conduct of electronic interactions among people, organisations and things. With the adoption of AI, for the first time, the ethical discussion is taking place before — and during — a technology’s widespread implementation.
Organisations are moving to secure personal data, and governments are implementing strict legislation to enforce it. Gartner predicts that by the end of 2023, more than 80% of companies worldwide will be facing at least one privacy-focused data protection regulation.
“Even where regulations do not yet exist, customers are actively choosing to engage with organisations that respect their privacy,” said Bart Willemsen, research vice president at Gartner. “The application of new technologies such as those on this Hype Cycle will provide a path to protecting privacy in a volatile environment.”
To address these changes in law and customer demand, security and risk management leaders must carefully select technologies that balance innovation with compliance. Gartner predicts that by 2024, worldwide privacy-driven spending on data protection and compliance technology will exceed $15 billion annually.
Proactive and mature organisations are moving away from reactive compliance towards proactive privacy by design. When they do, they start to invest in innovations on the left side of the Hype Cycle, such as homomorphic encryption, a set of algorithms that enable computation on encrypted data; and differential privacy, a system for using or sharing a dataset while withholding or distorting certain information about individual records in it.
New innovations added to this year’s Hype Cycle for Privacy are:
- Influence engineering, the production of algorithms designed to automate elements of digital experience that guide user choices at scale by learning and applying techniques of behavioural science. Though still largely theoretical, breakthroughs in areas such as emotion detection and language generation show clear potential to automate influential aspects of communication.
- Federated machine learning (ML), an important innovation in (re)training ML algorithms in a decentralised environment without disclosing sensitive information. Federated ML uses knowledge of model coefficients contained in local nodes such as smartphones softbots, (semi)autonomous vehicles or IoT edge devices, without exchanging data samples, enabling more-personalised experiences without compromising privacy.
- Sovereign cloud, the provision of cloud services within a single geography meeting data residency and other legislative requirements.