AI Code Generation: Code Security & Quality, Benefits, Risks & Top Tools

What is AI code generation?

AI code generation is exactly what it sounds like — using artificial intelligence to write and improve code. Tools powered by large language models (LLMs) and specialized AI systems can help developers generate boilerplate code, fix bugs, and even refactor entire sections of an application. And developers are leaning in. According to a GitHub survey, 92% of developers have already used AI coding tools at work or on personal projects.

For example, GitHub Copilot can whip up an entire function just from a simple comment, while OpenAI’s ChatGPT can generate full applications across multiple programming languages. Whether you’re creating a quick Python script or building a front-end interface, AI can speed up the process dramatically. 

But that speed comes with a trade-off. AI-generated code isn’t always secure, and that’s where things get tricky. 

How AI writes code

At its core, AI learns to write code by studying a lot of existing code. Models are trained on millions of code snippets from open-source projects, documentation, tutorials, and more. When a developer types a prompt (e.g., "create a login form in React") the AI predicts the most likely code for that request based on all the examples it’s seen before. The result can be impressive, but it also depends heavily on the quality (and security) of the training data.

AI-powered coding: Purposes and examples

AI coding tools aren’t just about speed. They help in lots of different ways. Need to design a new feature? AI can suggest the code. Need to spot a typo or fix a bug? AI can catch it. 

Let’s say you’re building a responsive web app. Instead of hand-coding every component, you could simply describe what you want (e.g., "create a responsive card layout with a call-to-action button"), and an AI-powered assistant can generate the framework for you.

AI is also great for code translation and refactoring. Imagine your team wants to move from PHP to Python. Rather than rewriting everything from scratch, you could use AI to translate key modules, accelerating the migration process.

Finally, AI can automatically generate test cases, reducing the time spent on manual testing. AI can analyze your code and write comprehensive unit tests to make sure your application behaves as expected.

AI code assistance and its effects on cybersecurity

AI-powered coding has its perks, but security is a major concern. If an AI tool learns from insecure code, it might unknowingly repeat those same vulnerabilities in your project. This is especially dangerous for developers who aren’t security experts — if you don’t know what to look for, it’s easy to miss these risks entirely.

To mitigate these risks, organizations should implement best practices for secure AI-assisted coding. First, developers should always review AI-generated code before integrating it into their projects. AI code should be treated as a suggestion rather than a final implementation, requiring validation and testing. It’s also important to integrate security measures, like Snyk Code's real-time static application security testing (SAST), into the development process. 

These tools provide insights into potential security flaws, allowing teams to address them before they reach production. For example, Snyk Code scans AI-generated code in real time, identifying security risks before they become issues. 

What are the benefits of AI-generated code?

There’s a reason so many developers love AI code generation. It speeds up development and reduces the repetitive grind. Tasks that used to take hours can now take minutes.

But AI’s value isn’t just about speed. By taking care of tedious work, AI frees developers to focus on innovation: solving hard problems, designing better user experiences, and brainstorming creative features. For example, instead of spending hours setting up basic form validation, developers can focus on making that form delightful to use.

There’s also the financial benefit. Faster coding means lower development costs. Teams get more done in less time, and businesses see faster returns on investment.

Limitations and challenges of AI code generation

Of course, AI isn’t perfect. One of its biggest flaws is a lack of context awareness. AI might generate code that works technically but completely misses the point of what your app actually needs.

Another issue is that AI can only generate code based on what it’s seen before. If the training data includes bad practices, outdated patterns, or security flaws, the AI will happily reproduce them.

AI also doesn’t understand your business goals or user needs the way your team does. It can generate code that looks right, but without human oversight, you could end up with features that don’t align with your product vision or user expectations.

Risks of writing code with AI

With all its benefits, AI-generated code still comes with real risks. The most concerning? Security vulnerabilities. If AI copies insecure patterns, your application could end up wide open to attackers.

There’s also the risk of over-reliance on AI. Developers who lean too heavily on automation could see their own coding skills decline over time, making them less capable of spotting errors — or understanding why AI-generated code might be faulty.

Another potential pitfall is intellectual property risk. Since AI is trained on publicly available code, it could accidentally reproduce copyrighted snippets or improperly licensed content. This opens up legal risks if you’re not careful.

Finally, accountability gets murky. If something goes wrong with AI-generated code — from a functional bug to a full on security breach — who’s responsible? Without clear processes for reviewing, validating, and documenting AI contributions, assigning responsibility becomes difficult.

Snyk Code: Your AI security companion

To help teams safely adopt AI coding tools, Snyk Code acts as a powerful, AI-enhanced security companion. Snyk Code automatically scans AI-generated code in real time, identifying vulnerabilities and providing developers with actionable fixes directly within their development environments. 

Powered by DeepCode AI, Snyk Code leverages a hybrid AI approach that combines machine learning with human-curated security knowledge, ensuring highly accurate detection of both known and emerging vulnerabilities. With DeepCode AI Fix, Snyk Code doesn’t just find issues — it offers intelligent auto-fixes to resolve them quickly, reducing the back-and-forth between developers and security teams.

With Snyk Code acting as your AI security companion, your team can:

• Proactively identify and fix vulnerabilities before they become security incidents.

• Empower developers with real-time security insights, helping them write safer code without slowing down.

• Enhance compliance with security best practices and industry standards.

• Confidently scale AI-driven development, ensuring code quality and security at every stage.

AI is changing the way developers work — but security can’t be an afterthought. With Snyk, you get the best of both worlds: faster development powered by AI and stronger security built into your process.