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AI Security

Lesson 4 of 5 · 9 min read

Statistics for AI Security Evaluations

Choose an evaluation target and analysis that match the sampling process. Work through clustered and paired results, weighted judge audits, repeated-trial estimators and selection effects, while keeping statistical uncertainty separate from required security boundaries.

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Practise it as an interviewer asks it

AI Red Teaming, Evaluation & ResponseHow is AI red teaming different from a penetration test and from safety benchmarking?AI Red Teaming, Evaluation & ResponseWalk me through red-teaming a customer-facing chatbot from scoping to report.AI Red Teaming, Evaluation & ResponseWhat is attack success rate, and how do you report findings when results are probabilistic?How LLM Systems WorkExplain tokens, context windows and sampling temperature, and why non-determinism changes how you write security tests.Prompt Injection & JailbreaksA prompt injection classifier catches 95% of attempts in your evaluation. Do you ship it? Defend your answer.AI Red Teaming, Evaluation & ResponseHow do you scope and set rules of engagement for an LLM red team?

Course lessons that teach this

Evaluate AI Security, From Protocol to DecisionAudit the observer before accepting its labels22 minEvaluate AI Security, From Protocol to DecisionCorrect the estimate for the review sample25 minEvaluate AI Security, From Protocol to DecisionCompare defenses with matched cases and useful work25 min

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Attack Success Rate and Probabilistic FindingsEvaluating Prompt-Injection Defences: Adaptive Attacks, Utility and BenchmarksGuardrails and Their LimitsScoping an AI Red Team EngagementWhat AI Red Teaming Is