The Dark side of AI

AI Voice bot

"Just ask Dave send him a text"

In this episode of the AI Voice Bot Podcast, host Dave, a 20-year veteran in voice technology, explores the growing cybersecurity threat landscape facing AI systems.

The discussion reveals that 38% of AI security incidents in the past year involved compromised training data, showing how vulnerable machine learning models are to manipulation. Key threats include data poisoning, evasion attacks, and model inversion, with real-world examples — from autonomous vehicles misidentifying traffic lights to healthcare AIs leaking patient data.

The hosts highlight that traditional cybersecurity isn’t enough; AI needs a Zero Trust framework, where every dataset, model interaction, and API call is treated as untrusted until verified. They stress techniques like differential privacy, federated learning, and confidential computing as essential defences.

The episode closes by emphasizing that AI itself has become the new attack surface — and that securing it is crucial to maintaining public trust in automation.

Listeners are encouraged to follow, rate, and share the show, and to connect directly with Dave from AI Voice Solutions, the team behind ib2-powered bots that bring smarter, more secure voice automation to business.

Key takeaway:

AI security isn’t about walls anymore — it’s about trust, verification, and vigilance.

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