AI Security Engineer Foundations & Certificate

AIIC.engineer is an AI security engineering training website covering six workshop modules, quizzes, certification, and practical security challenges. Its curriculum covers prompt injection, indirect injection, sensitive information disclosure, supply-chain risks, excessive agency, shadow AI, AI bills of materials, threat modeling, MITRE ATLAS, and secure development practices. Workshop exercises include scanning a repository into an inventory of models, datasets, and agents; analyzing a vulnerable student assistant; restricting database access; validating outputs; scanning agent skills and MCP servers; and using scan-fix-rescan workflows with coding-agent rules and hooks. The training also addresses secure prompting, dependency verification, development and production boundaries, IDE and CLI configuration, developer review, governance, and offensive agent workflows.

AI Engineering from Scratch is a free, open-source curriculum developed by rohitg00 for learning to build and ship AI systems end to end. Its 20-phase sequence covers development tooling, mathematical and machine-learning foundations, deep learning, NLP, computer vision, generative AI, LLM applications, agent engineering, Model Context Protocol (MCP), agent skills, reinforcement learning, and related topics, with implementations in Python, TypeScript, Rust, and Julia. The repository describes 511 lessons and approximately 329 hours of material; each lesson produces a reusable artifact such as a prompt, skill, agent, or MCP server. Learners are instructed to read the lesson documentation, type and run the code from the repository root, record command evidence and outputs, and make a small change before continuing. It provides routes for complete foundations, mathematics and machine learning, production LLM applications, agent engineering, MCP, agent skills, and Claude certification preparation. The repository is distributed under the MIT license and is accompanied by a website containing the same lesson content.

Evo by Snyk is a security and governance platform for AI-native applications, agentic development, and AI systems. It provides continuous visibility, governance, testing, and real-time control across the AI software lifecycle. Its Agentic Development Security capability discovers models, agents, MCP servers, datasets, plugins, tools, and services used by agents across repositories; generates an AI bill of materials; applies policy and guardrails to agent actions; and validates AI-generated code before it reaches repositories, pipelines, or production. Its Continuous Offensive Security capability autonomously stress-tests applications and AI systems like an adversary, with agents coordinating reconnaissance, vulnerability hunting, exploit validation, remediation, and reporting, and an independent validation model intended to confirm exploitability before reporting findings. Testing is intended to run on code changes and pull requests.

Snyk is a security company and developer and application security platform that secures AI-generated code, development agents, and AI-native applications from development through production. Its platform provides continuous validation and integrates with IDEs, CI/CD pipelines, and AI coding assistants, while addressing automated AI attacks, untrusted agentic development, and ungoverned AI applications. Snyk also presents continuous offensive security and AI-powered penetration testing as part of its approach to finding and validating vulnerabilities as code changes.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.