GenAI and Cybersecurity: Safeguarding the Digital Age
summary
Panel claims automated attacks rise fast with 51% of traffic bots 37% malicious and attackers using GenAI to iterate around detections; defenders report building and testing detection layers and rapid-response ML but attackers still outpace fixes. Research teams demonstrate real-time deep fakes built but code unpublished and police pilot AI to flag forged documents. Practical countermeasures are concrete: data classification, continuous monitoring, entity analytics and the trend toward self-hosting large models for safer enterprise use.
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chapters (15)
- 0:00Introduction
- 2:13AI's impact on automated attacks
- 5:02AI code risks and security
- 9:41Governance and guardrails for AI
- 15:07Deep fakes and ethics
- 22:35Mitigating AI model risks
- 27:52Monitoring AI security threats
- 33:55Future of application security with generative AI
- 36:27Workforce transformation
- 41:00Regulation vs innovation
- 43:27Reducing deep fake risks
- 44:28Secure coding with AI
- 45:35Managing external AI models
- 46:40Cybersecurity standards relevance
- 47:01Key takeaways
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