Coding on intuition: Exploring the rise and risks of AI-powered vibe coding

A new era is emerging in the world of software engineering: vibe coding. Instead of following rigid, step-by-step rules, developers are working more fluidly – using AI tools to generate code on-the-fly and shaping it based on instinct and context. This approach is quickly gaining momentum as developers learn to collaborate with AI to reduce the friction of traditional workflows and build faster. 

So what’s behind the rising popularity of vibe coding, and how is it reshaping the development process? More importantly, how can businesses and development teams begin integrating this approach into their workflows while avoiding the major cybersecurity pitfalls that come with it? 

All vibes 

Vibe coding has captured attention across the software landscape by addressing a long-standing dilemma: how to accelerate software delivery to meet customer demand. By offloading repetitive and boilerplate tasks, it frees developers to focus more on the creative and architectural elements of coding. 

Driving this trend, AI tools like ChatGPT and Copilot are fast evolving from passive assistants to collaborative partners. This marks a shift from traditional logic-first development to a hybrid approach blending experimentation and automation, while offering a fast-track for software from the development phase to digital shelves.  

Getting started with AI-augmented development 

One of the biggest draws of vibe coding is its accessibility – it doesn’t require advanced technical skills or expensive software to get started. Open-access tools like ChatGPT offer powerful capabilities even at the basic subscription level. Users can describe their goals in natural language, such as "build a dashboard to visualise this data" or "create a Python script to automate file sorting" and receive step-by-step guidance instantly. 

From Windows to macOS to Linux, almost any platform supports this approach. Personally, I’ve used GenAI to create scripts that conduct complex local searches and visualise datasets in minutes. With clear prompts and a collaborative mindset, it’s easy to build something functional, fast. 

Because of this accessibility, vibe coding offers value across skill levels – from seasoned developers to curious novices. Beginners can benefit from real-time guidance and reduced errors, while professionals can accelerate time-consuming processes or explore new ideas without being bogged down by syntax and setup. 

This approach can be especially empowering for non-traditional developers who may not code regularly but need to build custom tools or automate workflows. By removing technical friction, vibe coding allows more people within an organisation to contribute to software innovation, reducing reliance on overstretched dev teams or costly contractors. 

Solo developers and small teams can also gain an edge, with AI tools acting like silent teammates offering code suggestions, testing support, and idea validation. The result is streamlined development cycles with more time spent on strategic innovation rather than repetitive tasks. 

The hidden risks behind the flow 

Despite its promise, vibe coding is not without significant risks – particularly when it comes to security. AI-generated code is often functional, but not always robust. Inexperienced developers may accept suggestions without understanding the security implications, missing flaws that could expose applications to exploitation. 

One of the most dangerous aspects of this is code reuse. When AI introduces insecure snippets that are copied across multiple projects, vulnerabilities can proliferate through internal systems and third-party libraries. In fact, our research shows over 70% of critical security debt originates from third-party components. 

An emerging threat compounding this issue is slopsquatting, a tactic where attackers upload malicious packages to public repositories, hoping developers unknowingly use them via AI-suggested dependencies. If developers skip due diligence, these rogue components can quietly infiltrate otherwise legitimate projects. 

The bottom line: while vibe coding can supercharge productivity, it doesn’t replace secure coding practices. Developers must still apply rigorous testing and security scanning to ensure AI-generated outputs meet enterprise-grade safety standards. 

What’s next for vibe coding? 

AI-powered development is still in its early stages, but its trajectory is clear as it moves toward greater autonomy and more intelligent automation. In the near future, we can expect GenAI to take on broader responsibilities, from building entire application modules to conducting automated testing and even orchestrating deployments with limited human oversight. 

However, innovation must be matched with responsibility. Security can’t be an afterthought. Organisations need to embed security scanning, peer review and policy-based guardrails into their development pipelines to prevent AI from becoming an unchecked vulnerability source. 

The goal is not just faster software – it’s building smarter, safer systems that can scale reliably in an increasingly complex digital world.