Dublin startup raises US$2.5 m to protect AI data with encryption

Mirror Security, a UCD spin-out, has closed a US $2.5 million pre-seed round to roll out fully homomorphic encryption for sensitive AI workloads, aiming to make AI safer for enterprise use.
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Mirror Security, founded at University College Dublin, has announced a US$2.5 million (approx. €2.15 million) pre-seed funding round to develop what it describes as the next generation of secure AI infrastructure.

The startup’s core product, VectaX, is a fully homomorphic encryption (FHE) engine designed for AI workloads. This technology allows AI systems to process, train or infer on data that remains encrypted, meaning sensitive or proprietary data never has to be exposed in plaintext, even during computation.

Backed by leading deep-tech investors such as Sure Valley Ventures (SVV) and Atlantic Bridge, Mirror Security plans to scale its engineering and AI-security teams across Ireland, the US and India, accelerate development of encrypted inferencing and secure fine-tuning, and target enterprise markets in the US.

As organisations increasingly adopt AI, often handling sensitive data, Mirror Security argues that conventional security measures (like policy-based controls) fall short. Its encryption native approach aims to provide cryptographic guarantees rather than trust-based assurances, positioning the company as a ‘trust layer’ for the emerging AI economy.

The Irish startup also announced a strategic partnership with Inception AI (a subsidiary of G42) to deploy its full AI security stack across enterprise and government systems. Mirror has also formed collaborations with major technology players including Intel, MongoDB, and others.

From a digital policy and global technology governance perspective, this funding milestone is significant. It underlines how the increasing deployment of AI, especially in enterprise and government contexts, is creating demand for robust, privacy-preserving infrastructure. Mirror Security’s model offers a potential blueprint for how to reconcile AI’s power with data confidentiality, compliance, and sovereignty.

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