Revolutionizing
AI Security

for Edge Devices

The Challenge

AI has the potential to transform edge devices—smartphones, IoT gadgets, and beyond. But despite its promise, AI hasn't yet been widely adopted on edge devices. Why? Security. Until now, there hasn’t been a solution capable of protecting AI models without compromising performance, power efficiency, or area constraints.

Experience the power and simplicity of our solution in action:

Our Breakthrough

We’ve developed a groundbreaking security solution that makes secure
AI on edge devices a reality. Here’s how we redefine what’s possible:

Unmatched
Efficiency

Uses a fraction of the power consumption compared to traditional unprotected encryption (e.g., AES), allowing for truly efficient deployment on power-constrained devices.

Uncompromised
Performance

Protecting your AI models with zero performance degradation.

Robust
Security

Defending against sophisticated physical and cyber-physical threats, including Side-Channel Attacks (SCA), Fault Injection Attacks (FIA), and Quantum threats.

Space
Optimization

Significant area reductions enable seamless integration into even the most resource-constrained SoCs.

Why It Matters

The ability to move AI to edge devices transforms industries. It:

Enhances
Responsiviness

Enables real-time decision-making critical for applications like autonomous systems and smart healthcare.

Cuts costs

Reduces reliance on cloud computing and associated latency issues.

Secures
Sensitive Data

Protects intellectual property and user privacy from sophisticated attacks.

Who Can Benefit?

Our solution is designed for:

IoT Device
Manufacturers

SoC
Designers

OEMs and AI
Developers

Enabling secure, energy-efficient AI in next-gen smart devices.

Integrating lightweight, secure AI protection directly and simply into chip architectures.

Securing intellectual property and ensuring compliance with emerging standards.

Discover
What’s Possible

We’re here to help you unlock
the potential of AI at the edge.

Fortify’s AES security evaluation by SGS

“Summary. The leakage analysis (Welch t-test) on over 30 million traces did not show statistically significant first- and second-order differences between trace sets with fixed and random inputs. The template-based DPA analysis, on the pseudo-random trace set for the profiling phase (15 million traces) and on a sub-set of 300k fix input traces for matching phase targeting the first-round S-box output, and template attack on ciphertext, did not indicate any potential information leakage.”

” The results for the soft IP presented in the report were obtained on the TOE which is the basic hardware implementation of the soft IP without additional levels of security (e.g. that are present in a secure silicon layout). Therefore the internal strength of the soft IP itself was evaluated. This indicates that the investigated features and parameters of the soft IP implementation should be robust against SCA and fault injection attacks in different implementations including ASIC. Nevertheless, according to the Common Criteria rules, the strength of the final composite product must be evaluated on its own.”

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