Artificial Immune Systems (AIS)
Protection against advanced system-level threats. The AIS is a layered system designed to proactively and reactively address the complexities of advanced AI and quantum threat environments.
Adversarial
Machine Learning
FOR PROACTIVE THREAT DETECTION AND MITIGATION
Adversarial Machine Learning focuses on protecting AI systems from attacks by studying how these systems respond under threat. PureCipher leverages adversarial training techniques to fortify AI models, enhancing their resilience and enabling them to effectively detect and neutralize malicious activities.
Fully Homomorphic
Encryption (FHE)
FOR PRIVACY-PRESERVING COMPUTATIONS
PureCipher employs a quantum-safe Fully Homomorphic Encryption (FHE) scheme to achieve zero trust, enabling computations on encrypted data without the need for decryption. This advanced technology empowers AI models to perform inferences on encrypted data, significantly reducing the attack surface and enhancing overall security.
Noise Based
Computations
FOR PERFORMING INFERENCES USING NOISE-BASED DATA REPRESENTATION
PureCipher’s patent-pending Noise Based Computations (NBC) introduces a revolutionary method by embedding secure data into a new dimension using a noise matrix. This advanced mechanism ensures that sensitive information remains protected from security threats and unauthorized access during data processing activities.