Hidden risks of relying on AI to write code without oversight

A consultant’s guide to responsible AI adoption in software development

Welcome to Part 2 of my 3-part blog series sharing useful insights on how scaling businesses can adopt artificial intelligence responsibly to improve productivity while avoid some common pitfalls. This article is a guide to responsible AI adoption in software development - written for consultants like me.

Part 1: Improving productivity

How AI can help speed up development: a practical guide for software engineers.

Part 2: Responsible AI Adoption

A consultant’s guide to AI adoption: insights on governance and risk management.

Part 3: Avoiding the pitfalls

Building AI-powered products and features: what founders need to know.

As a full stack developer, I work across many Blue Hat client projects implementing data-driven solutions, AI proof of concepts and embedded analytics. The insights in this blog series are collated from undertaking fast-moving, real-world projects. So, let’s kick off with Part 2.

Introduction

In March 2025, cybersecurity firm Pillar Security revealed a worrying new vulnerability in GitHub Copilot and Cursor, dubbed the “Rule Files Backdoor.” Attackers could manipulate trusted AI coding assistants by altering hidden configuration files, causing them to generate malicious-looking code that appeared legitimate, effectively weaponising the AI to bypass human scrutiny (sources: Karliner 2025 French 2025).

This vulnerability highlights an ongoing and evolving risk for software teams relying on AI tools. The risks ripple through the entire development process from broken trust in your supply chain, to audits that miss critical issues, to attackers exploiting your deployment pipelines. Recognising and managing these risks is critical to maintaining secure development practices as AI adoption grows.

This article is a hands-on guide for technical leaders and consultants seeking to harness AI-powered tools responsibly. We’ll explore the ongoing risks, categorise them into practical areas, and outline how to build a governance framework that balances innovation with security, helping development teams stay ahead of potential threats as AI becomes an integral part of how we develop software.

The double-edged sword of AI assistance

In Part 1, we saw how AI tools like Copilot and ChatGPT can dramatically speed up development, helping teams crank out boilerplate, generate tests, and debug faster. But speed alone isn’t always a win. Move too quickly without understanding the risks, and AI can just as easily accelerate you into trouble.

One common misconception is that AI-generated code is safe or “best practice” by default.

The reality is that these tools are trained on large and messy public codebases, and they sometimes surface outdated libraries, insecure patterns, or biased assumptions.

A little scepticism goes a long way. Treat AI outputs as helpful suggestions, not answers. The teams that benefit most from these tools are the ones that stay curious and ask questions.

Key risk categories in AI adoption

AI introduces a unique mix of technical, operational, and ethical challenges. Understanding these categories can help teams plan and avoid being blindsided.

Technical risks:

Security vulnerabilities in generated code
AI can suggest insecure defaults or copy insecure patterns from public repositories. Without careful review, these vulnerabilities can reach production undetected. Additionally, AI-generated code may include dependencies on outdated or vulnerable libraries, as it often draws from older public codebases. These outdated packages can carry known security flaws, creating hidden risks that can be exploited if not properly audited and updated. Careful review and regular dependency management are essential to avoid introducing such security weaknesses through AI-assisted development.

Performance problems
AI doesn’t always optimise for runtime efficiency. Developers need to evaluate generated code not just for correctness, but also for performance and scalability.

Maintenance and dependency issues
Auto-generated code often lacks documentation or context. Over time, teams may struggle to maintain or update it, especially if the original “developer” is no longer available.

Operational risks:

Over-reliance on AI
Critical thinking can start to decline when teams begin to over rely on auto-generated code. Developers might start accepting suggestions without understanding them, leading to fragile systems and knowledge gaps.

Skill degradation
When AI handles routine tasks, developers (especially Junior Developers) may skip learning not just debugging and design, but also new technologies altogether. Overreliance risks eroding their problem-solving skills and technical growth. It’s important to balance AI use with active learning to keep skills sharp.

Compliance and audit concerns
Regulatory requirements around explainability, traceability, and data handling can be difficult to meet when AI is involved in decision-making or code generation.

Ethical Risks:

Bias and fairness issues
AI models reflect the biases in their training data. This can show up in subtle ways such as non-inclusive conventions, flawed logic, favouring certain libraries and platforms, or disregarding inclusivity or localisation needs of the system.

Lack of transparency
AI tools rarely explain how or why they made a suggestion. This can create a black-box where no one fully understands the logic behind a decision.

Privacy violations
Many AI coding tools send snippets of your code to cloud servers for processing. This raises the risk of exposing sensitive information, such as API keys, user personal data, or proprietary algorithms, if not properly managed. Developers need to be cautious about what data they share and ensure compliance with privacy policies and regulations.

Building a responsible AI governance framework

To use AI effectively and safely, teams need a governance model that balances speed with scrutiny. Here's what that can look like in practice:

1. Keep humans in the loop: AI should assist, not replace, the engineering review process. Establish policies that require human validation before AI-generated code reaches production. Development teams should conduct regular audits and code reviews, particularly before new releases.

2. Define use cases and boundaries: Not all tasks are appropriate for AI. Clearly define where AI is helpful, such as test generation, and where human expertise is essential, like security-critical code or architectural decisions.

3. Build in documentation and accountability: Require developers to annotate when and how AI was used. For example, flagging commits that include AI-generated code helps with traceability and later audits.

Governance doesn’t have to be heavy-handed. Even lightweight checklists and prompts can encourage more mindful adoption.

What to do next

AI is here to stay, and when used well, it can dramatically boost developer efficiency. But without proper oversight, the risks can outweigh the benefits.

Responsible AI adoption isn’t just about avoiding mistakes; it’s about building trust in the tools and processes that power your team. With the right governance model, organizations can move fast without breaking things.

In Part 3, we’ll shift focus to the product side: what startup founders and product leaders need to know when building AI-powered features or businesses from scratch.

For now, why not try this:

Start a light-touch AI audit. Ask your team:

• Where are we using AI in our workflow today?

• Do we have a review process in place?

• What’s one area where governance could improve outcomes?

Now’s the time to build good habits - before problems scale with adoption.

How Can Blue Hat Help?

We’re an experienced collective of senior technology leaders with a mission to help scaling SaaS businesses achieve their technology and product goals faster and more cost-effectively. We work closely with our clients to effectively bolster their leadership and development teams to tackle their most pressing technology and product problems.

Thanks for reading. If you’re not already, please follow us on LinkedIn to stay tuned for insights on data and artificial intelligence. Feel free to get in touch to arrange some time to talk with one of our Partners.