Why developers need security skills to effectively navigate AI development tools
Artificial intelligence engines are starting to populate everywhere, with each new model and version seemingly bringing forth more powerful and impressive capabilities that can be applied in a variety of fields. One area that has been suggested as a good possible use case for AI is writing code, and some models have already proven their abilities using a multitude of programming languages.
However, the premise that AI could take over the jobs of human software engineers is overstated. All of the top AI models operating today have demonstrated critical limitations when it comes to their advanced programming prowess, not the least of which is their tendency to introduce errors and vulnerabilities into the code they compile at cracking speed.
While it’s true that the use of AI can help save some time for overworked programmers, the future will likely be one where humans and AI work together, with talented personnel entirely in charge of applying critical thinking and precision skills that ensure all code is as secure as possible. As such, the ability to write secure code, spot vulnerabilities, and establish that applications are as protected as possible long before they ever enter a production environment is vital.
In this new white paper from Secure Code Warrior, you will learn:
- The pitfalls of blind trust in LLM code output.
- Why security-skilled developers are key to safely “pair programming” with AI coding tools.
- The best strategies to upskill the development cohort in the age of AI-assisted programming.
- An interactive challenge to showcase AI limitations (and how you can navigate them).
The promise of artificial intelligence writing complex code at the touch of a button is intriguing, but the reality is that AI will need a lot of help from human developers to craft truly secure and reliable code.
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
Book a demo
Artificial intelligence engines are starting to populate everywhere, with each new model and version seemingly bringing forth more powerful and impressive capabilities that can be applied in a variety of fields. One area that has been suggested as a good possible use case for AI is writing code, and some models have already proven their abilities using a multitude of programming languages.
However, the premise that AI could take over the jobs of human software engineers is overstated. All of the top AI models operating today have demonstrated critical limitations when it comes to their advanced programming prowess, not the least of which is their tendency to introduce errors and vulnerabilities into the code they compile at cracking speed.
While it’s true that the use of AI can help save some time for overworked programmers, the future will likely be one where humans and AI work together, with talented personnel entirely in charge of applying critical thinking and precision skills that ensure all code is as secure as possible. As such, the ability to write secure code, spot vulnerabilities, and establish that applications are as protected as possible long before they ever enter a production environment is vital.
In this new white paper from Secure Code Warrior, you will learn:
- The pitfalls of blind trust in LLM code output.
- Why security-skilled developers are key to safely “pair programming” with AI coding tools.
- The best strategies to upskill the development cohort in the age of AI-assisted programming.
- An interactive challenge to showcase AI limitations (and how you can navigate them).
Artificial intelligence engines are starting to populate everywhere, with each new model and version seemingly bringing forth more powerful and impressive capabilities that can be applied in a variety of fields. One area that has been suggested as a good possible use case for AI is writing code, and some models have already proven their abilities using a multitude of programming languages.
However, the premise that AI could take over the jobs of human software engineers is overstated. All of the top AI models operating today have demonstrated critical limitations when it comes to their advanced programming prowess, not the least of which is their tendency to introduce errors and vulnerabilities into the code they compile at cracking speed.
While it’s true that the use of AI can help save some time for overworked programmers, the future will likely be one where humans and AI work together, with talented personnel entirely in charge of applying critical thinking and precision skills that ensure all code is as secure as possible. As such, the ability to write secure code, spot vulnerabilities, and establish that applications are as protected as possible long before they ever enter a production environment is vital.
In this new white paper from Secure Code Warrior, you will learn:
- The pitfalls of blind trust in LLM code output.
- Why security-skilled developers are key to safely “pair programming” with AI coding tools.
- The best strategies to upskill the development cohort in the age of AI-assisted programming.
- An interactive challenge to showcase AI limitations (and how you can navigate them).
Click on the link below and download the PDF of this resource.
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
View reportBook a demo
Artificial intelligence engines are starting to populate everywhere, with each new model and version seemingly bringing forth more powerful and impressive capabilities that can be applied in a variety of fields. One area that has been suggested as a good possible use case for AI is writing code, and some models have already proven their abilities using a multitude of programming languages.
However, the premise that AI could take over the jobs of human software engineers is overstated. All of the top AI models operating today have demonstrated critical limitations when it comes to their advanced programming prowess, not the least of which is their tendency to introduce errors and vulnerabilities into the code they compile at cracking speed.
While it’s true that the use of AI can help save some time for overworked programmers, the future will likely be one where humans and AI work together, with talented personnel entirely in charge of applying critical thinking and precision skills that ensure all code is as secure as possible. As such, the ability to write secure code, spot vulnerabilities, and establish that applications are as protected as possible long before they ever enter a production environment is vital.
In this new white paper from Secure Code Warrior, you will learn:
- The pitfalls of blind trust in LLM code output.
- Why security-skilled developers are key to safely “pair programming” with AI coding tools.
- The best strategies to upskill the development cohort in the age of AI-assisted programming.
- An interactive challenge to showcase AI limitations (and how you can navigate them).
Table of contents
Secure Code Warrior is here for your organization to help you secure code across the entire software development lifecycle and create a culture in which cybersecurity is top of mind. Whether you’re an AppSec Manager, Developer, CISO, or anyone involved in security, we can help your organization reduce risks associated with insecure code.
Book a demoDownloadResources to get you started
Resources to get you started
10 Key Predictions: Secure Code Warrior on AI & Secure-by-Design’s Influence in 2025
Organizations are facing tough decisions on AI usage to support long-term productivity, sustainability, and security ROI. It’s become clear to us over the last few years that AI will never fully replace the role of the developer. From AI + developer partnerships to the increasing pressures (and confusion) around Secure-by-Design expectations, let’s take a closer look at what we can expect over the next year.
OWASP Top 10 For LLM Applications: What’s New, Changed, and How to Stay Secure
Stay ahead in securing LLM applications with the latest OWASP Top 10 updates. Discover what's new, what’s changed, and how Secure Code Warrior equips you with up-to-date learning resources to mitigate risks in Generative AI.
Trust Score Reveals the Value of Secure-by-Design Upskilling Initiatives
Our research has shown that secure code training works. Trust Score, using an algorithm drawing on more than 20 million learning data points from work by more than 250,000 learners at over 600 organizations, reveals its effectiveness in driving down vulnerabilities and how to make the initiative even more effective.
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Reactive Versus Preventive Security: Prevention Is a Better Cure
The idea of bringing preventive security to legacy code and systems at the same time as newer applications can seem daunting, but a Secure-by-Design approach, enforced by upskilling developers, can apply security best practices to those systems. It’s the best chance many organizations have of improving their security postures.
Developer-driven coding.
Securely.
Contact us today and make software security an intrinsic part of your development process.
