Disaster Recovery Journal Summer 2025

dence on, searching proprietary databases while keeping queries private, together create a strong demand signal for keeping data secure while at the same time being able to compute on it. Fully homomorphic encryption (FHE) is the most promising privacy-enhancing technology (PET), positioned to respond to that signal with its unique ability to allow computation on encrypted data without ever decrypting it. At its core, FHE is a cryptographic method that allows computations to be performed directly on encrypted data, producing an encrypted result that, when decrypted, matches the result of performing the same operations on the unencrypted data. Unlike traditional encryption, which requires data to be decrypted before it can be processed, FHE keeps data encrypted throughout the entire computation process. This revo lutionary capability has the potential to transform how organizations handle sensi tive data, particularly in cloud computing and AI applications. However, while FHE offers unparal leled security benefits, it also presents sig nificant implementation challenges. This article explores the key trends driving and constraining FHE adoption in 2025, the industries poised to adopt it first, and the broader implications for the future of Certain industries are poised to be early adopters of FHE due to their height ened security and privacy requirements. Finance and banking are among the most likely to integrate FHE first, as institutions look to enhance fraud detection, asset trad ing, and risk analysis while keeping cus tomer data encrypted. Additionally, FHE can help financial institutions meet com plex cross-border regulatory requirements by enabling secure transaction process ing without exposing sensitive financial details. Healthcare and life sciences represent another major sector that stands to benefit from FHE. Medical research and clinical trials rely on vast amounts of sensitive patient data, and FHE allows this data to be analyzed without ever being decrypted, ensuring researchers can access the data secure computation. FHE Entry Points

while complying with regulations like HIPAA and GDPR. Similarly, AI-driven diagnostics, which require access to exten sive patient histories and imaging data, can operate securely without compromis ing patient privacy. Government and national security agencies are also expected to leverage FHE for encrypted intelligence shar ing between allied nations. With the rise of cyber espionage and geopolitical threats, ensuring sensitive intelligence can be processed and analyzed securely is paramount. Moreover, law enforcement agencies could use FHE to conduct data driven investigations without exposing the broader private details of individuals, striking a balance between security and civil liberties. Finally, blockchain and Web3 applica tions are increasingly looking to FHE to solve privacy challenges. Smart contracts, which are typically executed on public led gers, can be enhanced with FHE to enable privacy-preserving transactions that still comply with regulatory requirements. Additionally, decentralized finance (DeFi) platforms could use FHE to facilitate pri vate transactions without revealing wallet identities, providing users with greater security and anonymity while assuring transactions are correctly executed and at the same time maintaining compliance with emerging financial regulations. The Adoption Drivers: Why FHE Is Gaining Traction The AI revolution has thrown the race for next-generation PETs into overdrive. Next-generation AI technologies are reli ant on retrieval augmented generation (RAG) to produce tailored results and perform tasks for their users. Think of an AI assistant that can, for example, search your email for an exchange about travel plans and tell you your mother’s flight number for her upcoming visit. RAG requires granting AI unfettered access to private troves of data – and that is a risk that rightly makes enterprise customers extremely wary. According to Gartner, 40% of organizations using AI have expe rienced an AI privacy breach. FHE pro vides a mathematical guarantee of privacy,

allowing enterprises to embrace RAG without fear. FHE would also shore up fundamental security risks inherent to the cloud com puting architecture that forms the back bone of modern digital infrastructure. The traditional cybersecurity model of “perim eter defense” is proving inadequate in an era where billions of endpoints and attack surfaces are exposed daily. In response, 2025 is shaping up to be the year of the “encrypted cloud.” According to Gartner, 60% of large organizations will use at least one PET this year. FHE will be at the core of this shift, enabling zero-trust architec tures where data is never exposed in plain text, even during computation. That privacy and security upgrade arrives just in time: quantum computing technology is advancing rapidly, and its arrival will facilitate a dramatic increase in hacking capabilities. Quantum computers will be better able to exploit the decryption of sensitive data as plaintext during com putation, making even this momentary exposure far riskier. In the quantum para digm, FHE will be required to keep data continuously secure during processing. Finally, FHE could help organizations develop consistent privacy strategies for a complex regulatory landscape. Regulatory bodies worldwide are increasingly holding organizations accountable for how they manage and protect user data. GDPR fines grow more punishing every year, and the European Union recently fined itself €400 for violating its own privacy rules—high lighting just how complex compliance has become. FHE could provide a breakthrough for regulatory compliance by enabling orga nizations to process sensitive data with out exposing it, allowing businesses to conduct audits, analytics, and AI training while remaining compliant with GDPR, HIPAA, and other data protection laws. The Adoption Barriers: What’s Holding FHE Back? FHE’s biggest challenges have always been efficiency and ease of use. While software acceleration efforts have signifi cantly reduced computational costs, FHE still requires orders of magnitude more

36 DISASTER RECOVERY JOURNAL | SUMMER 2025