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Learning Roadmap

How to Become a AI Quantum-Safe Security Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Quantum-Safe Security Specialist. Estimated completion: 9 months across 5 phases.

5 Phases
38 Weeks Total
High Entry Barrier
Expert Difficulty
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  1. Cryptographic Foundations & Quantum Threat Landscape

    6 weeks
    Goals
    • Master symmetric and asymmetric cryptography fundamentals including RSA, ECC, AES, and TLS
    • Understand quantum computing basics: Shor's algorithm, Grover's algorithm, and their cryptographic implications
    • Learn the Harvest-Now-Decrypt-Later threat model and why PQC migration is urgent
    Resources
    • Coursera: Cryptography I & II by Dan Boneh (Stanford)
    • NIST IR 8413: Status Report on the Third Round of the NIST PQC Standardization Process
    • IBM Quantum Learning: Introduction to Quantum Computing
    • Book: 'Post-Quantum Cryptography' by Bernstein, Buchmann, Dahmen
    Milestone

    You can explain classical and quantum threats to cryptographic systems, assess HNDL risk for an organization, and articulate why PQC migration is strategically urgent.

  2. Post-Quantum Cryptography Implementation

    8 weeks
    Goals
    • Implement NIST-standardized PQC algorithms (ML-KEM, ML-DSA, SLH-DSA) using reference libraries
    • Build hybrid classical/PQ key exchange and digital signature workflows
    • Gain hands-on experience with liboqs, OpenSSL 3.x PQC provider, and Cloudflare CIRCL
    Resources
    • Open Quantum Safe (OQS) project documentation and liboqs GitHub
    • NIST FIPS 203, 204, 205 final standards
    • AWS PQC TLS integration guides
    • PQCRYPTO EU project deliverables
    Milestone

    You can build a working hybrid TLS connection using PQC algorithms, benchmark performance tradeoffs, and identify implementation pitfalls like side-channel vulnerabilities.

  3. AI/ML Security & Pipeline Hardening

    8 weeks
    Goals
    • Understand AI-specific attack surfaces: adversarial examples, model extraction, data poisoning, prompt injection
    • Learn secure ML pipeline design including encrypted inference and privacy-preserving training
    • Master model provenance, attestation, and supply chain security using SLSA and Sigstore
    Resources
    • MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
    • OWASP Top 10 for LLM Applications
    • TensorFlow Privacy documentation
    • Google's Secure AI Framework (SAIF)
    • HuggingFace Safetensors and model signing guides
    Milestone

    You can threat-model an end-to-end ML pipeline, implement supply chain attestation, and integrate privacy-preserving techniques like federated learning or encrypted inference.

  4. Quantum-Safe AI Security Integration

    10 weeks
    Goals
    • Design comprehensive quantum-safe migration strategies for AI infrastructure
    • Build cryptographic agility frameworks for AI services
    • Implement formal verification of PQC protocol integrations
    • Develop automated cryptographic inventory and compliance tooling
    Resources
    • NIST SP 800-131A and CNSA 2.0 Suite guidance
    • ETSI QKD and PQC integration specifications
    • ProVerif and Tamarin prover tutorials
    • HashiCorp Vault PQC integration patterns
    Milestone

    You can lead a quantum-safe migration project end-to-end: assess current state, design the roadmap, implement hybrid solutions, verify correctness, and present findings to executive stakeholders.

  5. Industry Leadership & Specialization

    6 weeks
    Goals
    • Develop expertise in a vertical specialization (financial services, defense, healthcare, or cloud infrastructure)
    • Contribute to open-source PQC projects or publish research on quantum-safe AI security
    • Build thought leadership through writing, speaking, or standards body participation
    Resources
    • IETF PQC working group drafts and mailing lists
    • NIST NCCoE PQC Migration Project
    • Conference proceedings: PQCrypto, RWC, CCS, USENIX Security
    • Industry working groups: Cloud Security Alliance Quantum-Safe Security WG
    Milestone

    You are recognized as a subject matter expert, can lead organizational PQC strategy, contribute to standards development, and mentor junior practitioners.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

Hybrid PQ TLS Proxy for Model Serving

Beginner

Build a reverse proxy that terminates TLS using hybrid classical/PQ key exchange (X25519 + ML-KEM-768) and forwards requests to an AI model serving backend. Benchmark handshake latency and throughput against a classical-only baseline.

~30h
Post-quantum cryptographyTLS configurationNetwork security

Cryptographic Inventory Scanner for ML Repositories

Intermediate

Develop a CLI tool that scans Python and Go codebases to identify all cryptographic library usage, classifies algorithms by quantum resistance (vulnerable, quantum-safe, unknown), and generates a prioritized migration report with remediation suggestions.

~50h
Static analysisCryptographic agilityPython/Go tooling

Quantum-Safe Model Signing Pipeline

Intermediate

Implement an end-to-end ML model signing and verification system using ML-DSA (Dilithium) signatures, integrated with a CI/CD pipeline (GitHub Actions). Sign model artifacts at build time and verify them before serving deployment.

~40h
Digital signaturesML-DSA implementationCI/CD security

PQ-Encrypted Federated Learning Prototype

Advanced

Build a simplified federated learning system where model updates from clients are encrypted using PQ KEM before aggregation. Implement hybrid key exchange for the aggregation server and verify the integrity of aggregated updates using PQ signatures.

~80h
Federated learningPQ key encapsulationSecure aggregation

Formal Verification of a Hybrid Key Exchange Protocol

Advanced

Model a hybrid X25519+ML-KEM key exchange protocol in ProVerif or Tamarin and formally verify properties including secrecy, forward secrecy, and authentication. Document any discovered weaknesses and propose protocol modifications.

~60h
Formal verificationProVerif/TamarinProtocol security analysis

Quantum Threat Risk Dashboard for AI Infrastructure

Intermediate

Create a web dashboard that visualizes the quantum readiness posture of an organization's AI services: cryptographic algorithm usage heatmap, HNDL exposure scores by data classification, migration progress tracking, and CNSA 2.0 timeline compliance status.

~45h
Risk quantificationData visualizationCompliance mapping

PQ-Secure Secrets Management for AI Agents

Intermediate

Build a HashiCorp Vault plugin or wrapper that enables PQ-secure secret storage and retrieval for AI agent workflows. Implement PQ-encrypted transit, PQ-signed audit logs, and crypto-agile key rotation policies.

~55h
Secrets managementHashiCorp VaultCryptographic agility

Side-Channel Resistant PQC Implementation Benchmark

Advanced

Implement ML-KEM in constant-time C or Rust, test for timing side-channel leakage using tools like ctgrind or dudect, and compare side-channel resistance across multiple open-source PQC library implementations (liboqs, PQClean, pqcrypto).

~70h
Side-channel analysisConstant-time programmingPQC implementation

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