Your AI Is Protected At Rest And In Transit. The Risk Starts When It Runs

Dr. Najwa Aaraj, Chief Executive Officer, Technology Innovation Institute.

For decades, cybersecurity has focused on protecting data in two states. Data at rest is encrypted. Data in transit is encrypted. But a third state is becoming increasingly important: data in use.

AI makes this challenge impossible to ignore.

AI systems are increasingly being asked to work with some of an organization’s most sensitive information, from medical records and financial transactions to industrial data, government information and proprietary enterprise knowledge. Yet to generate value from that information, AI systems need to compute on it. During that processing, when data traditionally becomes accessible to the systems working with it, an important security gap can emerge.

For organizations looking to deploy AI more widely, this creates a fundamental question of trust. Can sensitive data, intellectual property and critical information remain protected not only when they are stored or transmitted, but throughout the computation itself? Trust is therefore no longer simply a cybersecurity objective. It is becoming a prerequisite for innovation.

Protecting Data In Use

​This is where confidential AI and privacy-enhancing technologies (PETs) become increasingly important.

The principle is straightforward: Organizations should be able to derive value from sensitive information without unnecessarily exposing it in the process, while protecting the data, models and computations working with it.

Protecting AI throughout its life cycle requires an ecosystem of technologies addressing different security and privacy challenges.

Trusted execution environments (TEEs) are one important part of that ecosystem. They create hardware-protected environments designed to isolate sensitive workloads from the underlying operating system, infrastructure and privileged users.

Other technologies approach the challenge differently.