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
Vibe Coding: Practical Guide to Updating Your AppSec Strategy for AI
Watch on-demand to learn how to empower AppSec managers to become AI enablers, rather than blockers, through a practical, training-first approach. We'll show you how to leverage Secure Code Warrior (SCW) to strategically update your AppSec strategy for the age of AI coding assistants.
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AI Coding Assistants: A Guide to Security-Safe Navigation for the Next Generation of Developers
Large language models deliver irresistible advantages in speed and productivity, but they also introduce undeniable risks to the enterprise. Traditional security guardrails aren’t enough to control the deluge. Developers require precise, verified security skills to identify and prevent security flaws at the outset of the software development lifecycle.
Resources to get you started
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