Securing Vibe Coding – How to Innovate Without Losing Control

Vibe Coding is where AI generates code from natural language and it’s revolutionising how we innovate and build software at speed.  But with this power comes significant risk.  Are your organisation's security, compliance, and engineering standards ready for the AI-driven shift?

In this episode of the Inspiring Tech Leaders, I discuss how we need to balance innovation and control.  I break down the major risks of Vibe Coding, from subtle security vulnerabilities and data leakage to the challenge of maintaining auditability and engineering excellence.

What you will learn:

💡 The fundamental difference between Vibe Coding and traditional development

💡 Why AI-generated code requires a "human-in-the-loop" approach

💡 Practical mitigations from sandboxed environments and secure prompt engineering to implementing robust governance frameworks

💡 How to ensure speed and efficiency don't come at the expense of security

This is a must-listen for all Tech Leaders, CISOs, and Developers navigating the AI landscape.  Don't just innovate, innovate securely.

Available on: Apple Podcasts | Spotify | YouTube | All major podcast platforms

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Chapters

• 0:00 Introduction
• 1:09 What is Vibe Coding and Why it Matters
• 1:53 The Major Risks of Vibe Coding
• 4:33 How to Secure Vibe Coding
• 5:14 [Ad] Everyday AI: Your daily guide to grown with Generative AI
• 6:02 (Cont.) How to Secure Vibe Coding
• 9:55 Governance Frameworks and Boundaries
• 10:54 Wrap Up

Transcript

Welcome to the Inspiring Tech Leaders podcast, with me Dave Roberts.  This is the podcast that talks with tech leaders from across the industry, exploring their insights, sharing their experiences, and offering valuable advice to technology professionals.  The podcast also explores technology innovations and the evolving tech landscape, providing listeners with actionable guidance and inspiration.

In today’s podcast I’m discussing Vibe Coding and how it’s rapidly reshaping how organisations develop software. Vibe coding allows teams to instruct an AI in natural language to generate, and sometimes execute, code. It is attractive due to the speed, agility, and innovation it enables. But with innovation comes risk. In this episode, I will examine the key risks associated with vibe coding, and more importantly, how Tech leaders can mitigate those risks while maintaining control. I will also discuss governance frameworks, organisational boundaries, and best practices for secure adoption. By the end of this session, you will have a clear picture of what it takes to safely integrate vibe coding into your enterprise without exposing your organisation to unnecessary risk.

So, vibe coding is fundamentally different from traditional software development. Instead of writing lines of code manually, developers or business users provide instructions in natural language. An AI agent can generate the required code, and in some systems, even deploy it in a controlled environment. The benefits are clear. Development cycles accelerate, backlogs are reduced, and prototyping becomes far faster. This approach also democratises software creation across the organisation, allowing business users and analysts to contribute directly. However, speed and efficiency cannot come at the expense of security, compliance, or governance. Tech Leaders must ask themselves what potential blind spots exist and how they can be effectively managed.

There are five primary risks that Tech Leaders need to consider. First, security vulnerabilities are a significant concern. AI-generated code can inadvertently introduce weaknesses. Libraries may be outdated or contain known vulnerabilities, and AI agents can hallucinate APIs or produce logic that is fundamentally insecure. For example, a Python script generated by an AI might query a database without input validation, exposing the system to SQL injection attacks. Security cannot be assumed; every AI-generated artifact must undergo rigorous review, scanning, and testing before deployment.

Second, data leakage and privacy risks are prevalent. Prompts often contain sensitive information, including internal databases, customer data, or proprietary business logic. If these prompts are processed in environments that are not isolated or fully enterprise-controlled, confidential data may be exposed or unintentionally used to train third-party AI models. This is particularly risky when using consumer-grade or cloud-based AI tools. Organisations need to implement strict data boundaries and prompt hygiene to protect intellectual property and personal information.

Third, over-permissioned AI agents can be dangerous. Some AI systems can execute code autonomously, modifying infrastructure, deploying software, or interacting with databases. Without clearly defined execution boundaries, AI agents can introduce errors or misconfigurations into production systems, pull external code containing malware, or create cascading effects that are difficult to trace. Autonomy must always be balanced with oversight.

Fourth, compliance and auditability gaps arise because AI-generated contributions often break traditional audit trails. Regulators and internal auditors expect clear accountability, but “AI wrote this code” is not sufficient. Organisations risk compliance breaches if there is no record of who instructed the AI, no documented review or approval, or if AI-generated code bypasses standard change management processes. Establishing a clear chain of accountability is essential.

And the fifth risk is the erosion of engineering standards and skills, which is a real concern. Over-reliance on AI can weaken human expertise. Junior engineers may defer critical thinking to AI outputs, and coding standards and best practices can erode if AI-generated code is not reviewed rigorously. The solution is to integrate AI responsibly into existing engineering culture, with mentorship, training, and human oversight.

A fundamental step in risk mitigation is building a secure vibe coding architecture. AI-generated code should never run directly in production. It is critical to use sandboxed environments where code can be tested safely, to restrict network access, and to control external API calls. Enterprise models must be licensed and secured, and sensitive data should remain encrypted at all times. This approach functions as a digital quarantine, ensuring that code is fully validated before it touches live systems.

Equally important is implementing human-in-the-loop controls. No matter how advanced AI becomes, human oversight is mandatory. Peer review or manager approval should be required before merging AI-generated code, treating AI like a junior developer that drafts, suggests, and prototypes but never makes final decisions. Integrating AI outputs into standard change management workflows ensures compliance and auditability, so speed does not compromise security.

Secure prompt engineering and data handling are also critical. Organisations must remove sensitive information from prompts, use templates that enforce secure coding patterns, and automate filters to prevent restricted or regulated data from entering AI systems. Policies should explicitly prohibit submitting personal data without anonymisation.

In addition, adopting guardrails and policy enforcement tools helps catch issues that may otherwise go unnoticed. AI-generated code should be scanned using static and dynamic analysis tools. AI-aware security platforms such as SonarQube for AI can identify vulnerabilities, unsafe dependencies, or logic errors. Continuous monitoring in production ensures that any anomalies are detected even after human review.

Skills, training, and culture change are also essential. Teams must be trained in AI security, prompt hygiene, and ethical coding practices. Developers should validate AI outputs rigorously, and mentorship programs should preserve core engineering skills while leveraging AI acceleration. Culture ensures that technical controls are consistently effective across the organisation.

In addition to these technical and process-based measures, organisations should implement practical, field-tested security controls. AI code review tools should examine every pull request. These tools can help catch SQL injection, exposed credentials, and broken authentication before code goes live, and when combined with human review, they provide multiple layers of protection. Rate limiting should be applied from day one, with a conservative starting point, such as 100 requests per hour per IP, to prevent bot attacks, database abuse, and excessive costs. Row-Level Security should be enabled in databases from the start to ensure that users can only access their own data. AI can generate policies, but human testing is critical. API keys should never be stored in code; instead, tools such as Azure Key Fault or AWS Secrets Manager should be used, with keys rotated regularly. Invisible CAPTCHA should be deployed on forms, registrations, and login endpoints to block bots without disrupting legitimate users. HTTPS must be enforced on all endpoints to protect session tokens, passwords, and API keys.

Every input should also be validated on both frontend and backend, and dependencies should be updated regularly, with security patches applied immediately. AI accelerates code creation, but security requires multiple layers such as AI writing, AI auditing, and human review, combined with monitoring and rate limiting. These practices can help prevent the vast majority of attacks and costly breaches.

Strong governance ensures that AI is a controlled tool rather than a liability. Developers should align with recognised frameworks such as the NIST AI Risk Management Framework to identify and mitigate AI risks, ISO 23894 for guidance on AI governance, OWASP Top 10 for LLMs to focus on AI-specific security issues, and CIS Benchmarks for AI and MLOps to provide operational guardrails.

Organisational boundaries should be clearly defined. Usage should be limited to internal tools unless approved, execution should never allow autonomous production deployment, data must be carefully controlled to exclude personal or sensitive information in prompts, and only approved enterprise AI models should be used. Accountability must be assigned clearly to roles such as AI Product Owner, AI Risk Officer, and Engineering Lead, with complete audit logs maintained of prompts, code generation, review, and deployment.

So, to summarise, vibe coding drives innovation, but it carries significant risks. Mitigation requires sandboxed environments, human oversight, secure prompt practices, AI auditing, and practical security controls. Strong governance frameworks and organisational boundaries are non-negotiable. Maintaining skills and fostering a culture of critical thinking ensures that these controls are effective. For senior technology leaders, the message is clear, innovation must be balanced with security, oversight, and accountability.

Well, that is all for today. Thanks for tuning in to the Inspiring Tech Leaders podcast. If you enjoyed this episode, don’t forget to subscribe, leave a review, and share it with your network.  You can find more insights, show notes, and resources at www.inspiringtechleaders.com

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Thanks for listening, and until next time, stay curious, stay connected, and keep pushing the boundaries of what is possible in tech.