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World Congress 2024 • Aug 20, 2024 • Session details

The transformative impact of GenAI for software development and its implications for cybersecurity

Chris Wysopal

Generative AI accelerates software development but secretly skyrockets your vulnerability rate. Discover why deploying security-trained AI is the only way to auto-correct these hidden flaws.

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#1 about 4 min

Evolution from manual hacking to automated security testing

How manual pen-testing and adversarial thinking evolved into automated application security testing.

#2 about 2 min

Introduction of vulnerabilities in aging software codebases

Analysis reveals that vulnerability rates increase as software complexity and legacy code grow over time.

#3 about 2 min

Measuring the growth of critical security debt in teams

Most development teams struggle to fix discovered flaws within a year, creating a dangerous backlog.

#4 about 2 min

How architectural complexity increases software security challenges

The shift toward microservices, cloud APIs, and open-source dependencies obscures visibility and complicates threat management.

#5 about 3 min

Integrating generative AI into software development workflows

Using large language models for code generation and debugging significantly accelerates programming workflows.

#6 about 2 min

How flawed training data compromises AI code generation

Large language models learn from vulnerable open-source repositories and textbooks that lack critical security constraints.

#7 about 3 min

Academic studies exposing vulnerabilities in AI-generated code

Research from major universities demonstrates a high prevalence of security flaws in outputs from AI coding assistants.

#8 about 3 min

Why developers trust incorrect programming answers from AI models

Studies show engineers frequently select wrong answers provided by AI over correct solutions from traditional developer forums.

#9 about 2 min

AI code assistants increasing software vulnerability velocity

The combination of faster code output and reduced code reuse drives up overall software defect rates.

#10 about 5 min

Using targeted AI models to automate security defect remediation

Training localized models on paired examples of vulnerable and patched code enables instant, integrated remediation suggestions.

#11 about 2 min

Evaluating IP and legal risks of AI security tools

Organizations must scrutinize AI toolchain vendors for accurate training data validation, copyright issues, and intellectual property leakage.

#12 about 2 min

Using targeted prompting to generate secure code components

Explicitly instructing AI models to include security reviews during code generation reduces initial defect rates.

Matching moments

2:05 min

The impact and risks of AI generated code

Chris Heilmann · LIVE

3:37 min

Managing security risks in AI-accelerated development processes

Carey Liu Carey Liu · WWC Europe 2026

5:51 min

Uncovering the hidden risks of generative AI adoption

Maish Saidel-Keesing Maish Saidel-Keesing · WWC 2025

3:28 min

Human accountability in AI-assisted code generation

Daniel Gebler Daniel Gebler +2 · WWC 2025

1:06 min

Addressing security flaws in AI-generated code

Balázs Kiss · WWC 2023

2:58 min

Managing vulnerabilities in auto-generated software development processes

Chris Wysopal Chris Wysopal +2 · WWC 2024

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