How to Secure AI Business Models

🎯 Key Takeaways for quick navigation:

00:00 🌐 Introduction to Generative AI and Security Concerns
– Introduction to generative AI technology and its impact on cybersecurity.
– Mention of executives' concerns related to trust, privacy, and accuracy in generative AI.
00:55 🔒 Securing Data for Generative AI
– Discusses the importance of securing the data used for training and tuning generative AI models.
– Highlights threats such as data poisoning, exfiltration, and leakage.
– Strategies for securing data, including data discovery, classification, cryptography, access controls, and monitoring.
04:29 🤖 Ensuring Trustworthiness of Generative AI Models
– Explains the challenges of sourcing and securing AI models.
– Emphasizes the need for trust in model sources and the potential risks of using unverified models.
– Discusses supply chain management for AI models and the importance of model integrity.
07:27 ⌨️ Securing the Usage of Generative AI
– Addresses security threats related to the usage of generative AI, including prompt injection, denial of service, and model theft.
– Recommends monitoring, semantic guardrails, and emerging tools like machine learning detection and response to mitigate these threats.
10:54 🏢 Integrating AI Security into IT Infrastructure and Governance
– Highlights the importance of considering AI within the context of traditional IT systems and security.
– Discusses the CIA Triad principles (confidentiality, integrity, availability) in AI security.
– Emphasizes the role of governance in directing, managing, and monitoring AI systems for fairness, lack of bias, and ethical operation.

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