After this lesson, you will be able to: Translate AI-in-cybersecurity literacy into resume bullets, portfolio pieces, and interview-ready answers for AI security or security-AI roles.
AI security is still defining itself, which means the candidates who get hired today are the ones who can demonstrate both sides (AI + security) clearly. This lesson maps the landscape.
AI Security Engineer, designs defences for AI products (prompt injection, data poisoning, model theft). $130-$250k. Security Engineer (AI-augmented SOC), uses Security Copilot, CrowdStrike Charlotte, or similar; speaks both languages. $110-$180k. ML Security Engineer, pen-tests models, builds defensive evals. $140-$240k. AI Red Team, focused on jailbreaking, prompt injection, exploit research against frontier models. $150-$300k senior. Search 'AI security engineer', 'ML security', 'prompt injection' on LinkedIn.
Skills: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, prompt injection categories (direct / indirect / jailbreak), AI red-team frameworks (Garak, PyRIT, Promptfoo), Microsoft Security Copilot / CrowdStrike Charlotte familiarity, basic Python for testing. Projects: 'Ran Garak against a public Llama-3 deployment; documented 12 prompt-injection findings.' 'Wrote a defensive eval suite for a fictional customer-support agent (Promptfoo).' 'OWASP LLM Top 10 walkthrough: implemented each defence in a sample chatbot and documented results.' Certs: Security+ held; AI security has no canonical cert yet, but DEFCON AI Village participation is a strong signal.
'Walk me through the OWASP LLM Top 10.' (Memorise; new but standardising fast.) 'What's the difference between direct and indirect prompt injection?' (Direct: attacker in chat; indirect: attacker plants the prompt in a document the agent reads.) 'How would you red-team a customer-support chatbot before launch?' (Tests practical AI red-team thinking.) 'What's the role of NIST AI RMF?' (Risk management framework specific to AI systems.) 'How do you detect prompt injection at runtime?' (Heuristics, classifiers, output validation, content filters; honest answer: nothing is foolproof.)
Two of these in 60 days lands AI security interviews; this field still rewards demonstrated work.
Implement a tiny chatbot via the Anthropic or OpenAI API. Run Garak and PyRIT against it. Document findings.
Write a Promptfoo eval suite for the chatbot covering 10+ adversarial prompts.
Implement each OWASP LLM Top 10 defence in your chatbot; publish a writeup.
Download a public Llama 3 / Mistral model on Hugging Face; explore the safety filters; document gaps with concrete examples.
Follow DEFCON AI Village (or similar) talks; write a 1-page summary of each.
Treating AI security as just prompt engineering. Real AI security spans red teaming, model integrity, data poisoning, supply chain, and identity. Cover breadth. Listing 'prompt injection' without showing a single example you found. Forgetting NIST AI RMF and MITRE ATLAS. These are the standardising frameworks; reference them. Pretending to know everything. The field changes monthly; honesty about what you don't know is a strong signal.
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