Build secure MVPs and transform faster with AI assistance

The market is flooded with coding assistants and AI-assisted coding has shifted from novelty to the new norm in a matter of months. Critics of the practice will condemn AI’s spaghetti code and fall behind the curve, while early adopters will build new MVPs faster and more securely, transforming their development practices and beating their competitors to the finish line. In this blog, we’ll show you how to avoid the common pitfalls of AI-assisted coding and provide Claranet’s method for empowering engineers and developers to do their best work with AI.

Why rapid delivery matters

Rapid does not mean rushed. In app development, it means smaller batches, tighter feedback loops, and automation doing the heavy lifting so engineers can focus on building the best product they can.

Here’s why rapid application delivery helps your business:

• Get real feedback sooner: An MVP in production gives you feedback from real users. That allows you to validate or disprove your assumptions and prioritise your next actions based on evidence.
• Reduce wasted work: Shorter iterations and more testing avoids months of building features nobody needs. You spend budget where it moves the needle.
• Outpace competitors: The team that ships first learns fastest. That learning compounds over time with later updates to the product.
• Improve quality earlier: Shorter cycles reveal integration, performance, and usability issues when they’re cheap to fix.

Why use a coding assistant

Coding assistants are accelerators, not replacements. Used well, they help teams move faster while preserving design intent and code quality. The early advantage of using code assistants doesn’t come from picking the perfect tool; it comes from using whatever you choose with strong engineering practices, security guardrails, and clear governance. Get standards, reviews, automation, and data controls in place first, then when you are ready, you can optimise the tool choice over time.

How AI-assisted coding improves development practices

The conventional wisdom is to let AI do the hard work, while getting your developers and engineers to focus on tasks that require human ingenuity and oversight. But what exactly should AI do and where should humans take responsibility?

• Rapid scaffolding: Use your coding assistant to generate APIs, data models, test skeletons, and infrastructure templates aligned to best practice standards.
• Safer refactoring: Suggest refactors, migrations, and app transformations with engineers in the loop to verify changes.
• Test coverage: Propose unit and integration tests that engineers review and extend, lifting baseline quality.
• Documentation: Summarise Pull Requests, produce Architecture Decision Record drafts, and keep README and runbooks current.
• Repetitive tasks: Create boilerplate code, data fixtures, and glue code so teams can spend more time on behaviour and UX.

Where to draw the line

• Architecture and security posture: Assistants inform, engineers decide. Keep threat models, data flows, and trust boundaries a human responsibility.
• Sensitive data: Don't paste secrets or personally identifiable information into prompts. Use enterprise-grade tools with governance or run models in a controlled environment.
• Direct-to-main changes: Never auto-commit. Like human-written code, everything produced by a coding assistant must go through peer review, continuous integration, and policy checks.

The payoff of getting this process right is faster throughput with fewer idle delivery cycles, while maintaining clear ownership and accountability.

Claranet’s approach to AI-assisted, engineer-led MVPs

Following the guidelines above, the applications team at Claranet have created a simple approach that enables developers and engineers to get MVPs ready faster and more securely, with AI assistance:

• Discovery with humans: Use short workshops to define goals, non-functional requirements, constraints, and success measures. We write the value hypothesis and acceptance criteria before code.
• Golden paths and templates: We start from secure, standardised foundations for code, cloud, CI/CD, and observability. Assistants generate within those constraints.
• Small, testable slices: We deliver regular and small increments that deliver something demonstrable each time.
• Automation first: Continuous Integration from day one, with automated builds, tests, Software Composition Analysis, Static Application Security Testing,  Infrastructure as Code (IAC) scanning, and policy checks on every change.
• Continuous feedback: Weekly user reviews, telemetry from the running system, and security checks baked into the pipeline.

We all know that pushing security to the end of the process delays product and feature releases if developers have to rewrite code or change application architecture. But building security or DevSecOps into the AI-assisted coding process is easy. Here’s Claranet’s approach:  

• Data classification and boundaries: We map data flows early and isolate services and environments, using the principle of least privilege by default.
• Policy as code: Implement guardrails for dependencies, containers, and IaC scanning. No secrets in code. Failing policy checks blocks merges.
• Supply chain integrity: Pinned dependencies (i.e, specifying exact versions of libraries in your project, where dependent software packages are pinned to a known-good version of that library to control dependency updates and to ensure reproducible builds), Software Bill of Materials (SBOM) generation, and signed artifacts. We track and remediate vulnerabilities.
• Privacy-first development: Pseudonymised data in non-production. No sensitive data in LLM prompts and only use LLM coding assistants approved by your security team.
• Observability and response: Logging, metrics, traces, and alerting are active from the first deploy. Runbooks and on-call ready for go-live.

Common pitfalls to avoid

• Over-architecting upfront: Over-architecting an MVP delays learning. Design for change, not for every eventuality.
• Uncontrolled assistant use: Letting tools write code without standards, reviews, or data controls creates security and licensing risks.
• Skipping/overlooking observability: Observability means monitoring and logging the behaviour of your application once it is deployed. Skipping this stage after a pilot launch makes troubleshooting errors and user feedback slow and noisy.
• Demo-ware over product foundations: Cutting corners on authentication, audits, and error handling leads to costly rework.
• No clear success criteria: Without measurable goals, teams debate opinions rather than follow evidence.
• Hand-off without enablement: If operations, security, or product teams aren’t prepared, the MVP stalls after the launch.

AI-assisted transformation

AI-driven development isn’t just for new projects. Many teams face the challenge of modernising or extending the life of legacy applications, often with technical debt or outdated components. Our approach applies the same engineering discipline, security guardrails, and AI-powered accelerators to:

• Refactor and upgrade legacy codebases, guided by coding assistants and automated testing.
• Safely migrate or modularise systems while maintaining business continuity.
• Address security, compliance, and architectural gaps as part of the transformation.
• Bring observability and automation to older environments, reducing operational risk.

Whether building a new MVP or bringing legacy systems into the modern era, the same principles apply: small, testable changes, continuous feedback, and security-by-design.

Why partner with Claranet

If you want to validate a new product or internal tool quickly, without compromising security or quality, here’s how Claranet can help:

• Engineer-led, AI-assisted: We combine seasoned software and cloud engineers with coding assistants under strong governance.
• Secure by default: Security and compliance guardrails are embedded from the first commit to production.
• Proven delivery discipline: We use small batches, automation, and clear metrics to ship value quickly and safely.
• Cross-functional teams: Product, design, engineering, and security working together, not in sequence.
• Built for what’s next: MVPs are designed to evolve, clean architecture, CI/CD, and observability in place from day one.

Ready to move from idea to secure, working software?

• Share your goals and constraints in a short discovery call.
• We’ll propose an approach, timeline, and success measures.
• We’ll deliver an MVP you can put in front of users in weeks, with the foundations to scale.

Get in touch to book a call with our applications team.