Artificial Intelligence and the Effective Security of AES: A Narrative Review of Side-Channel Analysis, Encrypted-Traffic Analysis, and the Post-Quantum Positioning of AES-256

This paper provides a narrative review on the role of artificial intelligence in cryptography, with a substantial focus on the Advanced Encryption Standard (AES) and, where the distinction matters, on AES with 256-bit keys (AES-256), at the intersection of cryptanalysis, encrypted-traffic analysis, and post-quantum computing. Deep learning side-channel analysis has been evolving into a general-purpose attack tool; neural distinguishers have entered classical cryptanalysis; and encrypted-traffic analysis shows that payload confidentiality does not imply metadata privacy. We distinguish throughout between the security of the primitive, the security of a concrete implementation, and metadata privacy at the protocol level, since the results surveyed here bear on the latter two rather than on the first. AES-256 remains structurally robust, including against the Grover bound in an idealized quantum model, but should be deployed alongside hardened implementations, post-quantum key establishment such as ML-KEM, and protocol-level countermeasures, rather than treated as sufficient on its own.