The Rise of Privacy-Enhancing AI: Key Regulatory Changes You Need to Track
Blog post from Duality
As global data privacy regulations such as the EU's GDPR, California's CPRA, India's DPDP Act, and the impending EU AI Act become increasingly stringent, AI systems face new challenges and opportunities in handling sensitive data. These regulations emphasize the necessity for robust privacy measures during data processing, not just storage, compelling enterprises to adopt Privacy-Enhancing Technologies (PETs) like federated learning, confidential computing, and homomorphic encryption. Such technologies enable secure computation on sensitive data without exposure, ensuring compliance and fostering trust while allowing cross-border collaboration. As fines for non-compliance grow, the regulatory landscape signals a shift towards architectures that inherently protect data in use, urging AI systems to demonstrate transparency, auditability, and privacy by design in their operations. This evolving framework demands that AI strategies adapt to ensure that models can function without direct access to raw data, thereby aligning with the emerging global data governance standards.
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