MagicCube’s Software-First Security Gains $10M Investment From e&

A $10 million investment signals a shift in digital security as UAE’s e& capital joins a funding round for MagicCube, a company developing software-first approaches to safeguarding AI, identity, and payments. The partnership underscores a shared focus on delivering security independent of hardware at a time when nations and enterprises are investing in advanced compute and data capabilities. According to Eddy Farhat, Executive Director, Corporate Ventures at e&, “MagicCube is addressing a growing need at the intersection of digital identity, payments, and AI security.” This collaboration positions MagicCube to build a neutral trust fabric, independent of specific hardware vendors or jurisdictions, and reflects the Gulf region’s emergence as a key hub for AI infrastructure and digital innovation.

e& Capital Invests $10M in MagicCube’s Post-Quantum Security

MagicCube offers security independent of hardware, a critical proposition for organizations grappling with increasingly complex data flows and stringent compliance requirements. This investment highlights the growing importance of the Gulf region as a focal point for artificial intelligence infrastructure and digital innovation; e& capital, the investment arm of a global technology group based in the UAE, is positioning itself for future growth. Sam Shawki, CEO and co-founder of MagicCube, emphasized the collaborative aspect of this funding, stating, “Following Verifone’s support, and at a time when the Gulf is shaping the future of AI and digital infrastructure, having e& in our corner is both a powerful endorsement and a strategic signal.” MagicCube’s Software Defined Trust platform aims to provide a neutral security layer, independent of specific hardware vendors or geopolitical jurisdictions, allowing partners to confidently scale operations globally and maintain control over critical data and AI models.

Software Defined Trust Platform Secures AI, Payments, and Identities

Traditional security methods are increasingly challenged by the proliferation of AI, digital identities, and the need for secure payments across diverse infrastructures; organizations often depend on physical hardware for safeguarding sensitive data, creating vulnerabilities as workloads shift between cloud and edge environments. MagicCube addresses this with a different approach, attracting a second closing of $10 million in funding, with UAE’s e& capital joining initial investor Verifone, signaling substantial investment in software-first security solutions. This financial backing is a strategic bet on a new paradigm for securing digital assets. The company offers security independent of hardware, a core offering designed for nations, enterprises, and hyperscalers investing in advanced computing and data management. This platform aims to establish a neutral trust fabric, securing critical workloads across geographical boundaries, devices, and cloud providers, enhancing resilience and compliance.

MagicCube is addressing a fast-growing need at the intersection of digital identity, payments, and AI security.

Eddy Farhat, Executive Director, Corporate Ventures at e&

The recent $10 million funding round closing for MagicCube signals a shift in focus for digital security investment, with UAE-based e& capital joining existing backers like Verifone; this isn’t simply capital injection, but a strategic bet on software-first security approaches. This regional investment arrives as nations and enterprises rapidly expand efforts to secure artificial intelligence models, digital identities, and the increasingly complex flows of cross-border data.

Following Verifone’s support-and at a time when the Gulf is shaping the future of AI and digital infrastructure-having e& in our corner is both a powerful endorsement and a strategic signal.

Sam Shawki, CEO and co-founder of MagicCube
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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