The Gene Simmons of Data Protection - AI Inference-time Guardrails

The Gene Simmons of Data Protection: Protegrity's KISS Method

Today, we are releasing our final FINAL episode from our series, entitled The Gene Simmons of Data Protection - the KISS Method, brought to you by none other than Protegrity. Protegrity is AI-powered data security for data consumption, offering fine grain data protection solutions, so you can enable your data security, compliance, sharing and analytics.

Episode Title: Navigating the Future of Data Management: Type Systems, Quantum Computing, and Protegrity's Innovations

In our final-FINAL episode, we are speaking with Ave Gatton, Director of Generative AI. We talk about how AI safety doesn't end with training, it begins with inference. We explore the overlooked frontier of AI security, from prompt-injection, data leakage, and model manipulation. Ave helps to understand how you can build guardrails that operate in real time, and adapt to evolving threats.

Questions

• What are inference-time threats and why are they becoming a critical focus in AI security? 
• How do inference-time risks differ from training-time risks? 
• Why is inference-time protection critical for safe, scalable AI adoption? 
• How do inference-time threats vary across industries? Is there any industry where these attacks are most prevalent? 
• Why are traditional security models insufficient at inference? 
• What is the impact of inference-time breaches on AI adoption? 
• What role does compliance play in shaping inference-time guardrails?
• What practical steps can organizations take to secure inference today? 
• How can businesses balance performance with security when adding guardrails? 

Links

• https://www.protegrity.com/
• https://www.linkedin.com/in/averell-gatton/

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