There is no widely adopted AI-based alternative to asymmetric cryptographic schemes like RSA or ECC at present — but AI and ML (machine learning) are being explored in the context of cryptanalysis and post-quantum cryptography research. Here's a full breakdown:

🔐 Classical Asymmetric Cryptography

RSA and ECC:

• Rely on hard mathematical problems: RSA: Integer factorization. ECC: Elliptic curve discrete logarithm problem.
• Proven and mathematically grounded, extensively vetted.

🤖 AI and Cryptography: Current Roles

1. AI for Cryptanalysis (Breaking crypto, not replacing it)

• ML models can learn statistical patterns and attempt to break: Classical ciphers (e.g., substitution, Vigenère). Side-channel data (timing, EM emissions).
• Example: Deep learning used in power analysis attacks on AES.
• Still limited against modern asymmetric schemes.

2. AI for Protocol Design / Optimization

• Some efforts to use AI to optimize cryptographic protocols or auto-generate symmetric ciphers (e.g., RL-based cipher generation).
• But not yet reliable or trustworthy for asymmetric encryption.

🧪 Is AI Used to Create New Asymmetric Schemes?

Not yet in a practical, secure form, because:

• Asymmetric cryptography requires provable security based on hardness assumptions.
• AI systems (like neural networks) are hard to verify for such guarantees.
• AI models are often opaque and may not provide the transparency and auditability needed in cryptography.

At the moment no asymmetric cryptographic scheme built on artificial intelligence models meets accepted security standards. Research on so-called neural cryptography has produced only laboratory demonstrations, and most proposals have been broken soon after publication. Early neural key-exchange attempts Researchers tried using tree parity machines to let two parties train small neural networks until their internal weights synchronized, then treat those weights as a shared secret. Cryptanalysts showed that an eavesdropper can converge on the same secret faster than the honest parties, which defeats the security goal. Newer variants add complexity but still lack a convincing proof that breaking them is hard. Why neural networks struggle to serve as public key primitives Public key cryptography depends on a function that is easy to compute yet hard to invert without a hidden trapdoor. Neural networks are excellent approximators, not one-way functions with a mathematical hard problem behind them. Their parameters can leak through model extraction, chosen-input attacks, or side channels, and no reduction-based proofs relate their security to a known difficult problem. What is actually progressing for post quantum security Standards bodies are moving toward lattice and code based systems such as ML-KEM, Dilithium, Falcon, and HQC. These schemes come with decades of mathematical study and have withstood extensive public cryptanalysis.TakeawayAn AI model can assist cryptographic workflows, for example by generating keys or spotting anomalies, but it is not yet a sound foundation for a public key system. For quantum resilience the practical choice is to adopt lattice or code based algorithms that are now entering formal standards rather than hoping that the complexity of a deep model will conceal a secret.

While RSA and ECC remain dominant, AI-enabled cryptographic alternatives are emerging—especially for post-quantum and distributed systems. Models using zero-knowledge proofs, neural key exchange, or federated architectures offer promising directions. In my paper on privacy-preserving federated learning, we introduced Ze-KPr, a ZKP-based authentication system functioning like asymmetric cryptography without key exposure. Additionally, our fuzzy-optimized cyberattack model supports lightweight, ML-driven security for IoT, and Deep Guard enhances digital authenticity using AI-based decision modeling. These frameworks don’t replace RSA/ECC yet but illustrate how AI can reinforce or even rethink cryptographic primitives.