The software development landscape is rapidly evolving, with AI/Large Language Models (LLMs) like ChatGPT and GitHub Copilot becoming increasingly popular tools. These technologies offer incredible potential for boosting efficiency and innovation, enabling developers to write code faster, debug more effectively, and even generate creative solutions.
Read About What Your Devs Are Doing with AI and How it Impacts Your Software Security
However, this rapid adoption also brings new security challenges that developers must be prepared to address. Failing to do so could mean exposing your organization to serious risks, including data breaches, vulnerabilities, and ethical concerns.
One of the biggest concerns is data security. These tools and models are not generating code; they are pulled from reserves in their database or the internet. Developers need to be acutely aware of the data handling practices of the AI/LLM tools. Treat the information you input into these tools with the same sensitivity as any other confidential data within your organization.
Then there's the issue of code security. While AI/LLMs can generate impressive code blocks, they don't inherently understand security implications. They can inadvertently introduce vulnerabilities like SQL injection flaws or cross-site scripting (XSS) loopholes.
Developers should never blindly trust AI-generated code. Treat it like code written by a junior developer – scrutinize it, review it for vulnerabilities, and test it thoroughly.
Another often overlooked aspect is prompt injection. Like traditional injection attacks, malicious actors can craft prompts to manipulate the AI/LLM's output. This could lead to the model revealing sensitive information, generating malicious code, or changing its intended behavior.
Learn What Engineering Leaders Need to Know About AI Security
Developers need to be aware of this risk and treat user-supplied input that interacts with the AI/LLM with the same level of scrutiny as any other external input. Validate, sanitize, and escape it to prevent unintended consequences.
How can developers acquire the necessary skills and knowledge to navigate these complexities? That's where Security Journey's Secure Coding Training comes in.
Our comprehensive training program goes beyond generic security awareness, equipping developers with the practical skills and deep understanding needed to write secure code in the age of AI.
AI is Supercharging Threats: Level Up Your Defenses with Security Journey
With 8 video lessons and 11 hands-on lessons now available on our secure coding training platform, our experts help developers learn about AI/LLM technologies, the risks and rewards of leveraging AI/LLM tools when developing code, and how to protect products in an AI/LLM world.
To make getting started easy, our experts put together three pre-built AI/LLM Learning Paths:
When leveraging Security Journey's Secure Coding Training for AI/LLM, you help your developers:
By investing in Security Journey's Secure Coding Training, you can empower your developers to confidently and securely harness the power of AI/LLMs. Our training content provides the foundation for building secure software in the age of AI, ensuring that innovation doesn't come at the cost of security.
AI/LLMs are transforming the software development landscape, but it's crucial to approach this new era with a security-first mindset. Security Journey's Secure Coding Training provides developers with the knowledge and skills to navigate the unique challenges of AI/LLM development, ensuring that your organization can innovate confidently while mitigating security risks.
Ready to empower your developers and secure your future? Explore Security Journey's Secure Coding Training today.