Learning Roadmap
How to Become a AI Security Compliance Specialist
A step-by-step, phase-based learning path from beginner to job-ready AI Security Compliance Specialist. Estimated completion: 7 months across 4 phases.
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Foundations of AI Security & Regulatory Landscape
6 weeksGoals
- Understand the core AI/ML lifecycle and where security risks emerge
- Learn the OWASP Top 10 for LLM Applications and common attack vectors
- Survey the global AI regulatory landscape (EU AI Act, NIST AI RMF, ISO 42001)
Resources
- OWASP Top 10 for LLM Applications (2025 edition) - free guide
- NIST AI Risk Management Framework 1.0 - full document
- Coursera: 'AI For Everyone' by Andrew Ng (ML lifecycle primer)
- EU AI Act official text and European Commission explainer pages
MilestoneYou can categorize AI systems by risk level, identify OWASP LLM Top 10 vulnerabilities, and articulate the purpose of three major AI governance frameworks.
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Technical Security Controls for AI Systems
8 weeksGoals
- Implement guardrails and content safety filters using real-world tooling
- Conduct prompt-injection and data-poisoning simulations in sandboxed environments
- Set up model audit trails using MLflow or Weights & Biases
Resources
- NVIDIA NeMo Guardrails documentation and GitHub examples
- OpenAI Safety Best Practices guide
- HuggingFace 'Evaluate' library documentation
- TryHackMe AI Security learning path
MilestoneYou can configure guardrails on an LLM endpoint, simulate a prompt-injection attack, and produce an audit-ready model card for a HuggingFace model.
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Compliance Frameworks & Governance Operations
8 weeksGoals
- Perform a full EU AI Act gap analysis for a sample AI system
- Draft an Algorithmic Impact Assessment (AIA) document
- Design a compliance-integrated MLOps pipeline with automated checks
Resources
- ISO/IEC 42001:2023 standard (purchase or library access)
- OneTrust AI Governance certification program
- Responsible AI Institute free assessment toolkit
- GitHub Actions for ML compliance automation tutorials
MilestoneYou can produce a complete regulatory evidence package for an AI system, map it to ISO 42001 controls, and build automated compliance gates into a CI/CD pipeline.
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Industry Specialization & Incident Response
6 weeksGoals
- Apply AI security compliance to a specific vertical (fintech, healthcare, or government)
- Design and execute an AI incident response tabletop exercise
- Prepare for professional certification (AIGP, CIPP/E, or ISO 42001 Lead Auditor)
Resources
- IAPP AI Governance Professional (AIGP) certification prep materials
- CREST AI Security Assessment framework
- MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
- Industry-specific case studies from NIST and ENISA
MilestoneYou can independently scope, assess, and document AI security compliance for a real-world organization in your chosen vertical and lead an incident response exercise.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
OWASP LLM Top 10 Vulnerability Lab
BeginnerSet up a local LLM application (e.g., using LangChain + a local model) and systematically reproduce each vulnerability from the OWASP Top 10 for LLM Applications. Document attack vectors, impact, and basic mitigations.
NIST AI RMF Risk Assessment for a Sample AI System
BeginnerSelect an existing open-source AI application and conduct a full risk assessment using the NIST AI RMF framework. Produce a Govern, Map, Measure, and Manage report with actionable recommendations.
Guardrails Configuration & Testing Pipeline
IntermediateBuild a production-grade content safety system using NVIDIA NeMo Guardrails and Guardrails AI. Create Colang policies for topic restriction, PII detection, and output validation. Automate testing with adversarial prompt datasets.
CI/CD Compliance Gate for ML Models
IntermediateDesign and implement a GitHub Actions pipeline that automatically evaluates ML models for fairness, security, and documentation compliance before deployment. Include bias metric checks, model card generation, and license verification.
EU AI Act Compliance Toolkit
IntermediateBuild a reusable compliance toolkit (Python library or web app) that guides users through EU AI Act risk classification, generates gap analysis reports, and maps findings to ISO 42001 controls. Include templates for technical documentation.
LLM Red-Teaming Playbook & Execution
AdvancedDevelop a comprehensive red-teaming methodology for LLMs using the Garak framework. Create custom attack modules for domain-specific threats, execute assessments against multiple models, and produce detailed remediation reports.
End-to-End AI Governance Platform Prototype
AdvancedBuild a prototype AI governance platform that includes a model registry, automated risk classification, compliance monitoring dashboards, incident response workflow, and audit trail logging. Integrate with MLflow and GitHub.
Cross-Jurisdictional AI Compliance Mapping Analysis
AdvancedCreate a comparative analysis of AI compliance requirements across EU AI Act, NIST AI RMF, Singapore AI Verify, and Brazil's AI Bill. Build an interactive matrix tool that maps obligations and identifies conflicts for multinational organizations.
Ready to Start Your Journey?
Prep for interviews alongside your learning — it reinforces every concept.