AI-Assisted Coding Is Speeding Up Development, But Is It Making Software Less Secure? 

There’s no doubt that AI is fast leveling the playing field in software development. The days of iterating and setting up a dev. team before actual coding can be done are fading fast. Now, you only need fewer hands with AI to iterate, develop, and deploy softwares fast. Thanks to AI’s access to online code repositories and its built-in natural language processor (NLP), AI can quickly analyze ideas in their rawest form, create a strategy map, code, and debug all in a matter of minutes. Basically, AI is optimized to do the heavy lifting while you – the human prompt engineer, handle the fine-tuning. 

But, as great as AI-assisted coding is, there’s a subtle risk involved. And the risks revolve around the old saying, “fast isn’t always good.” 

AI Coding and the Risk of Hallucination

There’s no doubt that AI is fast. When you merge its speed with its access to real data and code repositories, it’s reasonable to believe that AI should know what it’s doing. For instance, the average developer knows to debug code and also resort to open-source repositories like GitHub for assistance. So, if a human with limited attention span, speed, and skills can rely on available resources to build decent software, then AI should be able to do better. But, as statistics show, AI coding platforms don’t always know what they’re doing.  

Typically, when AI hallucinates code, everything looks accurate on the surface. But when you try to run the code, it either fails, runs into an error, or you find critical security flaws in the backend. As such, the consensus is that if you’re working with AI, always double-check.

To an extent, the issue with AI coding is that despite its access to data and coding resources, it needs better context to work properly. Seeing as AI is trained on human intelligence, it’s unsurprising that it may also possess certain human failings. Think about it this way: the average human experiences “decision paralysis” when faced with multiple choices or an overload of information. But unlike a human who may not move forward until their choices are greatly streamlined, AI is designed to “figure it out”. As such, you’ll find that AI’s access to vast amounts of data – which may be conflicting, plays a key role in it generating false and unsecure code.

Security Risks in AI-assisted Coding

For most AI-generated code, the goal is to achieve a set objective as quickly as possible. But when such an objective isn’t clearly defined with necessary security guidelines, the tradeoff between speedy results and security risks becomes inevitable. What’s more is that since the same AI is typically deployed to debug its own code, it will most likely overlook subtle security risks embedded in its speedy code. 

While AI’s speed isn’t in doubt, the security tradeoff is too critical to ignore. The consensus from most security studies on AI-assisted coding highlights 4 security risks

• Quality regression: apart from hallucinating false code, there’s a noticeable decline in quality for most AI-generated code due to consistent reuse. 

• Data exposure and privacy risks: AI thrives on data. But its ability to store, recall, and reuse data raises a major privacy risk. Just like the ProtonVPN review done by Cybernews emphasizes, privacy and multi-level data security is none negotiable.

• Poor permissions control: poorly generated code often leaves vulnerable backdoors like weak input validation and credential leaks that bad actors can exploit. 

• Review cost: while AI-generated code may be fast, it often has a higher percentage of vulnerabilities, which you must spend time carefully reviewing. As such, the time saved by speedy coding is eventually lost in the need for careful code review. And while it’s easy to scream at a serially errant engineer, it’s impossible to “scream” at AI, and that only worsens the frustration.

When AI Coding, Context is King!

Given AI’s propensity to confidently hallucinate false code in trying to produce results, it’s only logical to monitor and guide its efforts. This is why context is important. 

As most AI prompt engineers know, the more specific details and step-by-step guidelines you provide, the better the results with AI. As such, generic prompts like “design an app” will produce subpar results since AI is designed to work fast with its given command. This means that irrespective of AI’s access to useful resources, it will not operate outside its prompts.

Unlike humans who naturally make assumptions and consider varying factors before carrying out an instruction, AI doesn’t make assumptions. It strictly works with what it is given. As such, if you’re looking for a more secure app, you need to insert your security parameters into the prompt. It’s not enough to prompt AI with lines like “pretend you’re an expert developer with core security considerations….” 

Besides providing detailed context for AI to work with, code review remains fundamental. Code review is standard practice for software development. It’s the only way to carefully detect errors and vulnerabilities.

For most developers who rely on AI, there’s a tendency to prompt the same AI to review its code. But this is much like the fabled “being a judge in your own case.” No matter how critically objective you wish to be, you will not always clearly see your mistakes.

The same applies to AI self-assessment. While self-assessment isn’t entirely wrong, the standard practice is external code review – especially when you’re working in a team. As such, reviewing AI code is a task for an independent system – be it a different AI or a human.

Checklist for Secure AI-Assisted Coding

AI-assisted coding can accelerate software development, but secure software still depends on disciplined engineering practices. Use the following checklist to help ensure AI-generated code is reliable, secure, and ready for production.

AI only works within the context it receives. Include security expectations such as authentication, authorization, encryption, input validation, and compliance requirements in your prompts instead of focusing solely on functional requirements.

Avoid relying on the same AI model to review the code it generated. Independent code reviews, whether performed by another AI system or a human reviewer, are more effective at identifying logic errors, security vulnerabilities, and compliance issues.

Automate security validation with static application security testing (SAST), software composition analysis (SCA), secret scanning, infrastructure-as-code scanning, and dynamic application security testing (DAST). These controls help identify vulnerabilities before software reaches production.

Passing functional tests does not guarantee secure software. Validate authentication flows, authorization rules, error handling, API security, data validation, and edge cases to uncover vulnerabilities that AI-generated code may introduce.

Avoid exposing proprietary source code, customer data, credentials, or confidential business information to public AI services. Establish governance policies for approved AI tools, data handling, and prompt management across engineering teams.

Security does not end when code is deployed. Regularly monitor applications for new vulnerabilities, update dependencies, refine AI prompting practices, and incorporate lessons learned into future development workflows.

Bottom Lines

The issue of security in AI software development is an ongoing concern. While most of the security risks associated with AI-generated code may be rectified by detailed context and explicit security parameters, others need a more hands-on approach.

At the moment, AI isn’t ready for fully autonomous software development. AI is best suited for its current assistive role, and users must treat it accordingly. Rather than leave AI to figure things out, careful code review and step-by-step guidance remain the best way to work with AI.

At KMS Technology, we help enterprises move beyond AI-assisted coding by integrating AI into the entire software delivery lifecycle through:

• AI-native software engineering to accelerate development while maintaining quality and security
• Quality engineering and AI testing to validate both deterministic software and probabilistic AI systems
• Secure SDLC to identify vulnerabilities early and strengthen software resilience
• Agentic AI and workflow orchestration to automate complex engineering processes

Talk to our AI experts to learn how KMS can help you deliver secure, AI-native software at enterprise scale.