AI Security in Practice: Protecting Your AI-Powered Applications - Olivia Liddell
This talk provides a deep dive into securing AI-powered applications by addressing unique vulnerabilities that traditional security measures often miss. Using a fictional online retailer (ABC Company) as a case study, the presentation outlines three primary AI security risks—Prompt Injection, Data Poisoning, and Improper Output Handling. Mitigation requires implementing layered defenses across the entire application workflow: input validation, model protection, output sanitization, and continuous monitoring.
Key takeaways
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AI Attacks are Subtle and Invisible
17:32
Unlike traditional attacks (e.g., DDoS), AI vulnerabilities can be slow, subtle, and partially invisible to standard firewalls or logging systems, requiring a shift in defensive thinking.
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Understand the Three Core Risks
17:32
The three major risks are: Prompt Injection (tricking the model with disguised instructions), Data Poisoning (manipulating training data over time), and Improper Output Handling (trusting model output without validation).
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Implement Layered Defenses
45:00
Security must be applied at four stages: Input Validation (sanitizing user input before it reaches the model), Model Protection (limiting scope and permissions), Output Sanitization (treating all AI-generated content as untrusted), and Monitoring/Operations (tracking model behavior changes).