Chris Wysopal
The transformative impact of GenAI for software development and its implications for cybersecurity
#1about 6 minutes
From vulnerability researcher to automated security founder
The speaker traces their journey from a hacker testifying before the US Senate to founding Veracode to automate application security testing.
#2about 2 minutes
Analyzing vulnerability introduction and security debt
Data shows that new vulnerabilities increase as software ages and most teams accumulate security debt by not fixing flaws within a year.
#3about 4 minutes
How generative AI is changing software development
Generative AI boosts productivity through code generation and other tasks, but its reliance on vulnerable open-source code for training data introduces significant risk.
#4about 5 minutes
Research shows GenAI tools frequently generate insecure code
Multiple academic studies reveal that 30-40% of AI-generated code contains vulnerabilities, and developers are often overconfident in its security.
#5about 1 minute
Generative AI accelerates the creation of vulnerable code
By increasing code velocity without improving security quality, generative AI leads to a higher rate of new vulnerabilities being introduced into codebases.
#6about 4 minutes
Using AI to automatically find and fix security flaws
The solution to AI-generated vulnerabilities is to use specialized AI models, trained on curated good and bad code, to automate security fixes.
#7about 2 minutes
Evaluating the risks of AI-powered security tools
When adopting AI tools for security, it is crucial to consider the trustworthiness of training data, licensing issues, IP leakage, and fix accuracy.
#8about 1 minute
Prompting for security and embracing automation
Developers should explicitly ask generative AI for secure code and integrate automated security testing and fixing tools to keep pace with development.
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