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# NEBULA:FOG:PRIME – AI x Security Panel Discussion
- URL: https://cje.io/2025/02/13/nebula-fog-prime-ai-x-security-panel-discussion/
- Published: 2025-02-13T23:59:46.000Z
- Updated: 2025-02-15T01:35:56.000Z
- Description: It was an privilege to participate on this panel at the NEBULA:FOG:PRIME AI x Security Hackathon event on the 25th of January.
- Author: Casey Ellis
- Tags: Security, Teach

> It was an awesome privilege to participate on this panel at the NEBULA:FOG:PRIME AI x Security Hackathon event! You can find out more about NEBULA:FOG [here](https://nebulafog.ai/?ref=cje.io). If you want to stay up to date on next steps from the hackathon (or check out some of the hackathon projects and their demos) check out the NEBULA:FOG [Reddit](https://www.reddit.com/r/nebulafog/?ref=cje.io) or [Youtube Channel](https://www.youtube.com/@NEBULAFOG?ref=cje.io).

### Panelists

- **Ariel Herbert-Voss**: Founder of RunSybil and first security hire at OpenAI.
- **Will Pearce**: CEO and founder of [Dreadnode.io](http://dreadnode.io/?ref=cje.io), with extensive experience as a network operator and security consultant, including building ERA teams at Microsoft and Nvidia.
- **Casey Ellis**: Founder of Bugcrowd and co-founder of [disclose.io](http://disclose.io/?ref=cje.io), bringing over 20 years of offensive security expertise to help the community proactively tackle vulnerabilities.
- **Caleb Sima**: Founder of [Whiterabbit.vc](http://whiterabbit.vc/?ref=cje.io), a YC-style accelerator for cybersecurity companies, and an industry veteran with over 25 years of experience, including senior roles at Capital One, Databricks, and Robinhood.

### Some take aways from the panel...

- LLMs enable faster and more scalable offensive security capabilities, allowing security researchers and attackers to automate and parallelize tasks. This "offense at scale" can outpace current defensive capabilities.
- However, the widespread availability of LLMs has also enabled faster development of exploits and attacks, reducing the time defenders have to respond.
- Bridging the gap between academic AI research and real-world security applications has been challenging, as academics focus more on benchmarks while security practitioners care more about practical outcomes.
- When developing with LLMs, common pitfalls include having unrealistic expectations about their capabilities and not properly evaluating their reliability and consistency under real-world conditions.
- To evaluate LLM-based security startups, key questions to ask include: What is the long-term defensibility of the solution? Does it actually require an LLM, or is it just "rubbing some AI on it"? And how well does it scale and perform consistently?