Trellix VP of Product on GenAI in Cybersecurity

How can GenAI be used to enhance cybersecurity products? In this webinar, Trellix VP of Product Deepak Dalvi will speak on novel uses of AI, specially how PMs can use GenAI to disrupt and add value to Cybersecurity products. Tune in for insights on real-world use cases of GenAI in cybersecurity outside of now traditional LLM integrations into enterprise products.

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Show Notes:

1. Generative AI has real-world applications in cybersecurity, beyond just theory and hype.
2. Product managers should focus on customer needs when implementing Gen AI, not just the technology itself.
3. Gen AI can improve operational efficiency in security operations centers by automating alert triage.
4. Experimentation and failing fast are key when exploring Gen AI applications in products.
5. Partnerships with cloud providers and other tech companies are important for successful Gen AI implementation.
6. Gen AI can help with multilingual translations and threat hunting in cybersecurity products.
7. Design partnerships with customers are crucial for validating Gen AI features and use cases.
8. Metrics-driven approaches help measure the impact and value of Gen AI implementations.
9. Gen AI can significantly speed up threat detection and investigation processes.
10. Product managers should consider data residency, responsible AI usage, and certifications when implementing Gen AI.
11. Cost models for Gen AI implementations can vary (e.g. customer-owned vs. vendor-provided infrastructure).
12. Gen AI implementations should ideally use existing product metering to avoid friction in adoption.
13. Security concerns around Gen AI models need to be addressed through proper tokenization and bias elimination.
14. Gen AI has potential applications beyond cybersecurity as assistive technology in various industries.
15. Product managers should stay informed about AI advancements to identify new problem-solving opportunities.
16. Gen AI can help create human-like context and insights from large volumes of security data.
17. Implementing Gen AI may require balancing between large language models and smaller, more specialized models.
18. Gen AI is still maturing as a technology, with many potential applications yet to be discovered.
19. There are concerns about job displacement due to Gen AI, but it’s also seen as enhancing human capabilities.
20. Gen AI is expected to become a norm in many applications, solving previously unsolved problems and improving efficiency.