AI Coding Assistants: With Maximum Productivity Comes Amplified Risks

Your AI Coding Assistant might be your fastest and most productive teammate, but it can also be your biggest security risk. 

In our latest whitepaper, our co-founders Pieter Danhieux and Dr. Matias Madou, Ph.D., explore the double-edged sword that is AI Coding Assistants and how they can be a welcome addition and a significant security liability at the same time. 

AI Coding Assistants – The New Normal? 

There is no escaping the rise of LLM coding assistants. From GitHub, Copilot, to the newly minted Deepseek, they’re pretty much everywhere, with new ones popping up almost every week. They became an extra team member, producing code in record time, even paving the way for ‘vibe coding’ where one without qualified programming skills can create an entire application in seconds by just giving the right prompts. AI coding assistants became so invaluable that by mid-2023, 92% of developers surveyed by GitHub reported using AI tools on the job or even outside of work. 

But there’s a hitch, AI-generated code can’t be blindly trusted and left entirely on its own.

Despite their rapid adoption, organizations must remain vigilant about the risks. While these tools accelerate delivery, they can also introduce significant flaws if left unchecked. The convenience they offer often comes with hidden risks, that’s where we took a deeper look into how these assistants perform in our latest whitepaper.

‍When Speed And Risk Go Hand In Hand 

AI Coding Assistant tools are trained on billions of lines of open source code, which often contain unsafe patterns. When copied, those weaknesses don’t just land in your codebase, they spread, creating a ripple effect of vulnerabilities in the wider SDLC. 

Alarmingly, a recent Snyk survey found that 80% of developers admit that they don’t apply AI Code security policies, while 76% of respondents believed that AI-generated code was more secure than code written by humans. These are numbers we can’t quite ignore. 

Setting AI guidelines is a good start, but without measured and verified security skills, they won’t stop insecure code from slipping into production. Traditional guard rails just can’t keep pace with the sheer volume of code that AI can produce. 

Arming Your Devs For The Future

The only scalable solution? Equip developers with the ability to spot and fix vulnerabilities before code ever goes live and always remain one step ahead of AI. 

A strong AI-era developer risk management program should have the following:

• Benchmark security skills: Establish a baseline, track progress and identify skill gaps. 
• Verify AI Trustworthiness: Vet the backend LLM and the tool itself. 
• Validate every commit: Embed security checks into the workflow.
• Maintain continuous observability: Monitor all repositories for insecure patterns and automatically enforce policies. 
• Offer role and language-specific learning: Focus training on the exact frameworks, platforms, and tools your team uses. 
• Stay agile: Update training as new tech and business needs evolve. 

Security and AI, Moving Forward 

AI Coding assistants are here to stay, and their productivity benefits are too big to ignore. But if security isn’t built into the process from day one, organisations risk trading short-term speed and convenience for long-term (and more complicated) vulnerability and security issues.

The future of software security isn’t just about choosing between AI and human developers. It’s about combining their strengths, with security-first thinking as the glue. That means every developer, whether they’re writing code or prompting the AI, needs the skills to know what to look for and ensure code safety. 

AI-driven productivity boosts productivity, but don’t forget, secure code safeguards the future.

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Download the full whitepaper today!