Artificial intelligence is transforming industries—from financial services and healthcare to national defense and public infrastructure. Yet as organizations increasingly rely on AI to drive innovation and automate decision-making, they face a growing challenge: how to ensure these systems can learn from and act on sensitive data without compromising privacy, security, or intellectual property.

AI’s progress depends on access to large volumes of high-quality data. But that data often contains personal information, proprietary assets, and critical infrastructure insights—creating new risks for exposure, misuse, and manipulation. Research shows a majority of organizations have already experienced AI-related security incidents, including unintentional data leakage and adversarial manipulation of training data. Meanwhile, compliance with privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) grows increasingly complex and costly.

Traditional security models—focused on network perimeters, access controls, and threat detection—are not designed to protect what AI relies on most: the data and models themselves. As a result, many enterprises and public institutions are reluctant to scale AI initiatives, concerned about reputational harm, regulatory violations, and ethical risk.

In this session, Ravi Srivatsav, CEO and Co-founder with deep expertise in AI and cryptographic privacy, introduces a new paradigm: zero-knowledge AI. This emerging security framework enables AI systems to operate on encrypted data—without ever exposing sensitive content to internal systems, external services, or even the AI itself. By combining privacy-enhancing technologies such as fully homomorphic encryption (FHE) and Trusted Execution Environments (TEEs), zero-knowledge AI protects data and model integrity across the entire AI lifecycle.

TEEs provide a secure enclave for sensitive computations, shielding data and models during processing and safeguarding against external threats and insider access. This hardware-based isolation adds a critical layer of defense against model theft, unauthorized access, and runtime manipulation—particularly in cloud or shared environments.

Srivatsav will explore how zero-knowledge AI mitigates risks such as model exfiltration, data poisoning, reverse engineering, and compliance failures. He will also highlight real-world use cases across healthcare, finance, and government—where secure AI enables life-saving research, fraud detection, and personalized citizen services without compromising trust or privacy.