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Donato Capitella, an expert in threat modeling AI applications at WithSecure, dives into the complexities of LLM security. He discusses the importance of creating an LLM security canvas and addresses the risks of prompt injection attacks that can jeopardize user data. The conversation emphasizes the need for skepticism towards AI outputs and highlights strategies for threat detection and validation. Donato also explores the future of AI, including the innovative role of autonomous agents and the contributions of ethical hackers in enhancing cybersecurity.
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Quick takeaways
- Understanding the context of AI use is crucial for establishing effective security measures against threats like prompt injection.
- Developing solid threat models enables organizations to proactively identify and mitigate vulnerabilities in generative AI applications.
Deep dives
Transforming Voice Data with Speech AI
Assembly AI focuses on converting voice data into actionable insights using advanced speech AI models. Their technology is designed for a range of applications, including speech-to-text, speech understanding, and generating summaries from audio data. For instance, the API provides developers the ability to extract metadata like entities and personally identifiable information from voice recordings, facilitating nuanced applications in various industries. This capability allows developers to create innovative products powered by voice data, which is increasingly abundant in today's digital landscape.
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