AI-powered no-code platforms offer remarkable capabilities – non-technical users can now build sophisticated applications in hours rather than weeks. But with that power comes risk, as discussed in our related article. Without proper oversight, these platforms can produce applications that are insecure, noncompliant, or simply not fit for purpose.
This framework provides practical steps for organisations that want to harness the benefits of no-code development while managing the associated risks. It’s designed to add appropriate oversight without destroying the speed and accessibility that makes these platforms valuable in the first place.
Start with due diligence on the platform itself
Before you build anything, you need to understand what you’re signing up for. Read the terms and conditions – yes, actually read them. What can the platform do with your data? Can you opt out of having your inputs used for training their models? Where is your data stored, and does that meet your compliance requirements? For many organisations, discovering that their commercial data is stored in a jurisdiction they can’t work with, or that it’s being used to train someone else’s AI, is a deal-breaker. Find out early.
Key questions to answer:
- What rights does the platform have over data you input or generate?
- Where is data stored geographically?
- Can you opt out of data collection and model training?
- What happens to your data if you stop using the platform?
- Does the platform meet your industry’s compliance requirements?
Classify what you’re building
Not every application carries the same risk. A tool that tracks office coffee preferences is very different from one that handles customer payment information. Before anyone starts building, establish a simple classification system – perhaps bronze, silver, and gold tiers based on the sensitivity of data and the risk if things go wrong. This tells you how much oversight each project needs. Your coffee tracker might just need someone to glance at it occasionally, while anything touching customer data needs proper review at every stage.
Consider classifying based on:
- Data sensitivity: What type of data does the application handle? Personal data? Commercial confidential? Public information?
- User base: Who will use this? Internal staff only? External clients? The public?
- Business impact: What happens if this application fails or is compromised?
- Regulatory requirements: Does this application fall under specific regulations (GDPR, PCI-DSS, etc.)?
Assign technical oversight
This is the most important step, and the one that organisations most often skip. Nontechnical users can absolutely build with these platforms, but they need access to someone who understands the technical implications of what they’re creating. This doesn’t mean an engineer needs to write every line – that defeats the purpose – but they do need to be available to review security-critical features, advise on architecture decisions, and spot the holes that AI typically leaves. Think of it as having a technical advisor, not a gatekeeper.
The technical advisor’s role includes:
- Reviewing security-critical features before implementation
- Advising on architectural decisions and data flow
- Identifying when AI-generated code needs human review
- Being available for questions during development
- Conducting or coordinating final security reviews
- What the platform does well (UI, basic CRUD operations, integrations)
What if you don’t have anyone with appropriate technical skills? In this instance, you need to carefully assess the risk of, and impact from, the data you’re processing being made public. If you genuinely assess the risk to be something your organisation can accept, or there is literally no data being processed, then continue without this oversight. However, this is likely the minority of use-cases. My strong advice would be to seek this oversight. Ether by hiring a third-party, or better still, develop this skill in-house.
Train your citizen developers
Give people guidance on what these platforms do well and where they fall down. They’re brilliant at creating user interfaces and basic data operations, but they struggle with complex business logic, security boundaries, and error handling. Users need to know when to push forward and when to ask for help. They also need to understand that AI will happily do what
they ask, even if what they’re asking for is insecure or poorly designed.
Training should cover:
- What the platform does well (UI, basic database operations, integrations)
- Where it struggles (security, complex logic, edge cases)
- When to seek technical advice
- How to spot common security issues
- Your organisation’s classification system and what it means for their projects
- The approval and review process
Implement code review for anything important
For low-risk, non-critical applications, perhaps a quick look from someone technical is enough. For anything handling real business data or connecting to other systems, you need proper code review before it goes live. The engineer supporting your citizen developers should be checking that authentication works properly, that user permissions are enforced, that data validation is in place, and that sensitive information isn’t being logged or exposed. This isn’t about preventing people from building – it’s about ensuring what they build actually works safely. [Editor’s note: I’m keenly aware that this phrasing “This isn’t this – its’ this” is a key pointer that AI, particularly ChatGPT wrote it. But, in this case, it really didn’t. It’s entirely human written. Honest!]
Code review should verify:
- Authentication and authorization are properly implemented
- User permissions are enforced consistently
- Input validation prevents injection attacks
- Sensitive data is properly protected (encrypted, not logged)
- Error handling doesn’t leak information
- API keys and secrets are not exposed in code
- Data access follows the principle of least privilege
Conduct a Data Protection Impact Assessment (DPIA) where needed
If your application processes personal data in a way that’s likely to result in high risk to individuals, as a Data Controller, it is your responsibility to conduct a DPIA under GDPR and similar regulations. This isn’t just box-ticking – it’s a structured way to identify privacy risks and put measures in place to address them. Your no-code application that handles customer information almost certainly
needs one.
A DPIA helps you:
- Identify what personal data you’re processing and why
- Assess the necessity and proportionality of processing
- Identify risks to individuals
- Document measures to address those risks
- Demonstrate compliance with data protection law
The UK’s Information Commissioner’s Office has some good advice on DPIAs, covering what they are, why they’re needed, and how to do them.
Keep records of what you’ve built
Maintain a register of applications that includes what they do, who built them, what data they handle, who has access, and when they were last reviewed. This sounds bureaucratic, but when someone leaves the organisation or when you need to respond to a data subject access request, you’ll be glad you know what systems exist and what they’re doing. It’s also remarkably useful for identifying duplicate efforts or opportunities to consolidate.
Your application register should track:
- Application name and purpose
- Developer/owner
- Classification tier
- Data processed and stored
- User access list
The pattern
These platforms can genuinely empower non-technical users to build valuable applications, but that power needs to be balanced with appropriate oversight. Add that oversight and you get the best of both worlds – the speed and accessibility of no-code development, with the assurance that what you’re building is actually fit for purpose.
The framework outlined here isn’t theoretical – it’s based on practical experience of what works when organisations adopt no-code platforms. The key insight is that “no-code” doesn’t mean “no governance” – it means democratising development while maintaining the guardrails that keep applications secure, compliant, and effective.