Today’s encryption methods – from AES to TLS – were designed long before AI and high-speed, data-driven computation became central to business operations. They excel at protecting data at rest (stored on disk) and in motion (transmitted across networks), but they weren’t built to safeguard data in use, which includes data actively processed by AI systems.

For 35 years, RSAC has been a driving force behind the world’s cybersecurity community. The power of community is a key focus for the 2026 conference. With that in mind, this article is largely authored by sponsors and exhibitors who told Cybercrime Magazine why RSAC is critically important to them, and to all of us.

As AI models compress years of enterprise data into compact, portable assets, a single breach can expose everything. Traditional perimeter defenses leave models sitting in plaintext during training and inference — the unlocked vault. Fully homomorphic encryption changes that, enabling computation on encrypted data without ever surfacing it in memory.

Although large-scale quantum computers may still be years away, the need to act is immediate: organizations must begin migrating to post-quantum cryptography (PQC) today. From lattice-based constructions to novel methods for securing confidential information, the shift to PQC marks a new chapter in digital security, where quantum and classical computing coexist.

The 1980s were a far cry from what life looks like today. If you weren’t around, you probably don’t remember using paper maps to navigate, renting VHS movies at Blockbuster, printing photos from film, or calling people to talk on their landline phones.

Luigi Caramico, a veteran in the data protection industry, has been at the forefront of cybersecurity innovation for over two decades. As the founder and CTO of DataKrypto, Caramico is pioneering a new era of data security with fully homomorphic encryption (FHE) technology that promises to revolutionize how organizations protect their most sensitive information in the age of AI.

AI thrives on scale, pulling data from everywhere to train and operate effectively. But the more powerful AI becomes, the more concentrated and exposed the data feels. Regulators are not trying to slow innovation, they’re trying to make sure citizens’ most private records don’t become collateral damage in the AI race.