AI Security & PQC
Quantum threats to AI systems — data pipeline protection, model weight security, agent authentication, agentic commerce, and encryption at scale.
Why this matters: AI pipelines and model weights are attack surface too — an adversary who steals training data or model IP today can decrypt it the moment a cryptographically-relevant quantum computer arrives, and agentic systems add a new class of authentication risk.
Every AI system depends on a data pipeline — the flow from raw data ingestion through preprocessing, storage, training, and inference. Each stage involves cryptographic operations that protect data confidentiality and integrity. With the advent of cryptographically relevant quantum computers (CRQCs), these protections face an existential threat.
Harvest Now, Decrypt Later (HNDL)
Adversaries can intercept and store encrypted training data today, then decrypt it when a CRQC becomes available. For proprietary datasets worth millions in curation costs, this is a direct intellectual property theft vector. The data's value outlives the encryption protecting it.
Cryptographic Touchpoints
Data Ingestion
TLS key exchange (ECDH → ML-KEM)
Channel harvest exposes raw training data
Data Signing
Manifest signatures (ECDSA → ML-DSA)
Forged manifests enable data poisoning
Storage Encryption
Key wrapping (RSA → ML-KEM)
HNDL on encrypted datasets
Model Output
Model signing (RSA/ECDSA → ML-DSA)
Tampered models produce biased outputs
ML-KEM replaces ECDH for key exchange, and ML-DSA replaces ECDSA/RSA for digital signatures.
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