Roadmap
A structured sequence of AI security projects — from LLM defense and log analysis to adversarial ML and automated red teaming. Built progressively and documented openly.
Overall Roadmap Progress
Build the foundational tools that every AI security engineer needs to have shipped.
LLM Prompt Injection Defense Toolkit
A library of defensive patterns, filters, and evaluation scripts that detects and mitigates prompt injection attacks. Includes a test harness with 200+ adversarial examples and benchmark results across mitigation strategies.
AI-Powered Log Analyzer (LLM + Vector Search)
A tool that ingests logs, builds a semantic vector index, surfaces anomalies using embedding similarity, and uses an LLM to generate analyst-ready summaries and incident reports. No rules required.
Phase 1 Milestones
Deepen technical credibility with the work that separates AI security engineers from security generalists.
Adversarial Attack Playground
Interactive research environment implementing FGSM, PGD, C&W, and DeepFool in PyTorch with decision boundary visualizations and robustness benchmarking. Includes paper reproductions with annotated notebooks.
Secure MLOps Template
Production-ready GitHub template with model scanning CI, dependency audit, secrets management via SOPS, signed model artifacts, container hardening, and SBOM generation. Fork-ready for any ML project.
AI Threat Modeling Framework
A structured threat modeling methodology for AI systems inspired by MITRE ATLAS and STRIDE, covering 6 AI attack surfaces with worked examples, severity scoring, and countermeasure mappings.
Phase 2 Milestones
The most complex and end-to-end projects — each one combining multiple focus areas into a single deployable system.
LLM Red Teaming Evaluation Suite
Automated framework that tests LLMs across jailbreaks, safety bypasses, toxicity, data extraction, and prompt leakage. Generates structured evaluation reports aligned with industry red-team standards (PyRIT, NIST AI RMF).
AI-Powered SOC Assistant
Multi-agent LangGraph system that automates SOC Tier-1 workflows: alert triage, threat intel correlation, MITRE ATT&CK mapping, and analyst report generation. Integrates with Elastic SIEM.
Research Paper Reproductions (3–5 Papers)
Faithful reproductions of landmark adversarial ML papers (FGSM, PGD, UAP, Membership Inference) with annotated code, original result comparisons, and commentary on what still holds in 2026.
Phase 3 Milestones
Where These Skills Apply
Types of roles where these technical skills — LLM security, adversarial ML, red teaming, and secure AI engineering — are directly relevant.
AI Security Engineer
Anthropic · OpenAI · Google DeepMind · Microsoft
Build and maintain security systems for AI products: prompt injection defenses, red teaming frameworks, model security reviews. Requires LLM security engineering + evaluation frameworks.
AI Red Team Lead
OpenAI · Scale AI · Cohere · Stability AI
Lead structured adversarial testing of AI models before release: jailbreak research, safety evaluation, structured reporting. Requires red teaming methodology + evaluation suite experience.
Adversarial ML Engineer
DARPA · MITRE · Academic Labs · Defense Contractors
Research and publish on adversarial ML, robustness, and formal verification. Requires deep technical knowledge of attack algorithms and evaluation methodology.
MLSecOps / Secure AI Platform Engineer
AWS · Azure AI · Google Cloud · Enterprise AI Teams
Secure the AI platform infrastructure: model supply chain, deployment pipelines, runtime monitoring. Requires MLOps + security engineering hybrid skills.