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Cryptographic Security of AI APIs and Inference Infrastructure

Hardening AI API endpoints and inference pipelines against quantum-enabled cryptographic attacks.

Full day (6 hours)

Certificate on completion

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Course Overview

For AI platform security leads and MLOps engineers. Covers quantum cryptographic exposure of AI inference APIs, model serving TLS, API key authentication, OWASP LLM Top 10 intersections, and PQC migration using FIPS 203/204/205 for AI-as-a-service architectures.

Curriculum

Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.

#	Session	Topics
1	AI Inference Infrastructure and Its Cryptographic Surface Where cryptography sits in modern AI deployment
2	TLS, mTLS, and API Authentication Under Quantum Threat Protocol-level exposure in AI-as-a-service architectures	TLS 1.3 key exchange (X25519, ECDHE P-256) and its vulnerability to Shor's algorithm Long-lived API keys and OAuth tokens: harvest-now-decrypt-later risk in agentic AI systems mTLS certificate chains for model serving: ECDSA and RSA signing exposure
Break
3	OWASP LLM Top 10 and Quantum Security Intersections Where AI-specific threats compound quantum exposure	LLM01 (Prompt Injection) and LLM06 (Sensitive Information Disclosure): encrypted context windows at risk LLM09 (Supply Chain): model registries, container signing, and pipeline integrity under PQC EU AI Act Article 15 (accuracy/robustness) and NIST AI RMF GM.3 intersection with quantum readiness
4	Interactive Demonstration: AI API Cryptographic Audit Full-day format only	Mapping cryptographic dependencies across an AI inference pipeline (API gateway, load balancer, model server, vector DB) Identifying harvest-now-decrypt-later exposure windows for API traffic containing proprietary prompts and outputs Prioritising migration: which endpoints move to ML-KEM/ML-DSA first and why
Break
5	PQC Migration for AI Infrastructure FIPS 203/204/205 in practice for AI deployments	ML-KEM-768 for API gateway TLS: performance impact on inference latency (handshake overhead, ciphertext size) ML-DSA-65 for model artefact signing and container image verification Hybrid schemes (ML-KEM + X25519) for phased migration without breaking existing client integrations
6	Vendor and Standards Landscape Independent guidance on PQC readiness across AI platforms	Cloud AI platform PQC readiness: current TLS support in major inference APIs ENISA and BSI guidance on quantum-safe AI infrastructure Timeline alignment: CNSA 2.0, ANSSI, BSI migration schedules and their implications for AI deployments
7	Q&A and Migration Planning

Designed and Delivered By

Training courses are designed and delivered by QSECDEF in collaboration with subject specialists. All instructors have direct experience in quantum technologies and security.

QSECDEF brings expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our membership spans 900+ organisations and 1,500+ professionals working at the intersection of quantum technologies and critical infrastructure security.

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Cryptographic Security of AI APIs and Inference Infrastructure

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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