How to Become a AI Cloud Security Specialist

Build a reverse proxy gateway that sits in front of an LLM API (e.g., OpenAI or self-hosted) and implements prompt injection detection, rate limiting, PII filtering on outputs, token budget enforcement, and comprehensive audit logging.

Develop a Terraform-based toolkit that provisions a reference AI/ML platform on AWS with security best practices baked in - private subnets, encrypted storage, scoped IAM roles, network policies - plus Checkov policies that enforce these standards in CI.

Create a tool that scans HuggingFace model repositories for security risks - checking for pickle serialization exploits, verifying model checksums, generating SBOMs for model dependencies, and flagging models with known vulnerability patterns.

Set up a local lab environment with multiple LLM applications and use Microsoft PyRIT and Garak to systematically test them against known attack patterns. Document attack success rates, develop custom attack prompts, and build a regression test suite.

Deploy a RAG application using LangChain, a vector database (ChromaDB or Pinecone), and an LLM. Implement tenant isolation, retrieval access controls, output PII detection, prompt injection prevention, and comprehensive tracing with LangSmith.

Create a comprehensive incident response playbook specifically for AI system compromises - covering data poisoning, model extraction, prompt injection exploitation, and LLM abuse scenarios. Include detection rules, containment procedures, and communication templates.

Build a real-time security dashboard that aggregates signals from cloud security tools (Wiz/Prisma), LLM guardrail systems, and SIEM to provide a unified view of AI platform security posture - including model integrity status, attack attempt metrics, and compliance scores.