AI Is Writing More Code — But Who Is Securing It?

Software development is changing at a remarkable pace. AI coding tools can now help developers write functions, generate tests, explain unfamiliar code, fix bugs and build application components in minutes. For businesses, that can mean faster releases and more time for teams to focus on complex work.

That sounds like a clear win. But there is another question worth asking: who is checking the security of all that new code? AI-assisted development can make software creation faster, but faster code does not automatically mean safer code. AI-generated code can contain vulnerable patterns, introduce unfamiliar dependencies or make mistakes that are difficult to spot at first glance.

As AI coding becomes part of everyday development, Application Security (AppSec), secure coding, code review and AI security testing need to evolve with it.

The Rise of AI-Assisted Software Development

AI-assisted development is moving from an experiment into a practical part of the software development workflow. Developers can use AI coding tools to generate code, suggest improvements, create documentation, write tests and troubleshoot errors.

The attraction is obvious. Repetitive tasks take less time, prototypes can be created faster and developers can get useful suggestions without starting from a blank screen.

But AI is an assistant, not the final authority. Every piece of AI-generated code still needs to be understood, tested and reviewed before it becomes part of a production application.

Why Faster Coding Does Not Always Mean Safer Coding

There is a difference between code that works and code that is secure. A generated function may produce the expected result during testing while still exposing sensitive data, handling input incorrectly or using an insecure authentication approach.

When development accelerates, security checks can struggle to keep up. Secure coding therefore needs to remain part of the workflow rather than becoming a final check before release.

The faster teams can create software, the more important it becomes to make security scalable too.

The Hidden Security Risks of AI-Generated Code

AI-generated code can look polished and convincing, which is exactly why it deserves careful review. A developer may receive a working solution to an API integration, database query or authentication task without immediately seeing the security implications behind it.

The risk is not that AI always produces insecure code. The risk is assuming code is safe simply because it was generated confidently and appears to work.

Strong AppSec practices, automated checks and human code review provide the additional layer of confidence that AI output alone cannot provide.

AI Hallucinations and Vulnerable Code Patterns

AI systems can sometimes generate incorrect information with a convincing level of confidence. In coding, that might mean suggesting an outdated method, an incorrect implementation or a package that is not what the developer expects.

These mistakes can become security problems when they are copied directly into production systems. Developers should verify AI suggestions against trusted documentation, testing environments and established secure coding practices.

AI security testing can provide another layer of protection by helping teams identify suspicious or vulnerable code patterns before they move further through the development lifecycle.

The Growing Risk of Open-Source Dependencies

Modern applications are rarely built entirely from scratch. Developers rely on frameworks, libraries, packages and other open-source components to build software faster.

AI coding tools can make this even easier by recommending packages or generating code that depends on them. But every dependency becomes part of the software supply chain and can introduce security or maintenance concerns.

Businesses need visibility into their open-source dependencies, including what they use, whether known vulnerabilities exist and whether those components are still maintained.

How Attackers Are Targeting AI Coding Workflows

As organisations adopt AI coding, attackers have another area of the development process to target. Weak dependencies, compromised components and insecure code can potentially move from development environments into applications when appropriate controls are missing.

Security therefore cannot be treated as something that happens after development is finished. AI-assisted development needs security considerations from the beginning, including the tools developers use, the code they generate and the dependencies they introduce.

Where Traditional Code Review Falls Short

Traditional code review remains valuable, but AI can dramatically increase the volume of code being produced. Asking teams to manually inspect every change with the same level of attention may not scale.

The answer is not to remove human review. It is to combine human judgement with automated code scanning, security testing and dependency checks. Automation can identify common issues quickly, while developers and security professionals focus on findings that require context and judgement.

AI-Powered Security Testing and Code Scanning

Security testing is evolving alongside AI-assisted development. Automated code scanning can help identify common vulnerabilities, insecure patterns and potential weaknesses across large codebases.

AI security testing can add another layer by helping teams analyse code at scale and highlight areas that deserve closer investigation. Used properly, these tools do not replace security professionals. They help them spend more time on higher-value security decisions.

The strongest approach is to make testing part of the development workflow, so potential issues are found while they are still relatively easy to fix.

The Human-in-the-Loop Security Model

The future of secure AI-assisted development is unlikely to be humans versus AI. It is more likely to be humans working with AI, with clear responsibilities on both sides.

AI can generate suggestions quickly. Developers and security teams provide the context, judgement and accountability needed to decide whether those suggestions belong in production.

A human-in-the-loop model means AI-generated code is tested, validated and reviewed rather than trusted automatically.

Building a Secure AI-Assisted Development Lifecycle

Businesses adopting AI-assisted development should build security into the full software lifecycle, not add it at the end.

Key practices include:

• Use secure coding standards alongside AI coding tools.
• Combine automated code scanning with human code review.
• Monitor open-source dependencies and apply dependency scanning.
• Include AI security testing throughout development.
• Use DevSecOps practices to bring development, security and operations together.
• Set clear rules for how developers use AI tools and handle generated code.

This approach helps security keep pace with development instead of becoming a bottleneck after the code has already been written.

Conclusion: Speed and Security Must Evolve Together

AI is changing how software is built, and AI coding can give development teams a significant productivity boost. But the ability to create more code faster also means organisations need stronger ways to understand, test and secure that code.

The answer is not to avoid AI-generated code. It is to use it responsibly. With strong Application Security (AppSec), secure coding practices, code review, open-source dependency management, software supply chain controls, AI security testing and DevSecOps, businesses can move faster without treating security as an afterthought.

AI may be writing more of the code. But making sure that code is secure remains a shared responsibility.

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