We’ve gone over how AI platforms like Replit, GitHub Copilot, and Lovable promise faster prototyping, fewer bottlenecks, and broader access to development. But having an engineer does not just promise you the best product, but also ensures that throughout the process, the safeguarding of your data and algorithms are priorities. Recent events highlight a critical question every leader now faces: how do you innovate with AI without exposing your crown jewels?
The reality is, companies don’t just need AI that’s fast – they need AI that’s secure. And that is being reflected in the way they choose to innovate.
Why Security Matters
Why would a company choose a more secure AI solution? Because the risks of “open” AI can be devastating:
- Code confidentiality: Without guarantees, your proprietary algorithms could feed future model training.
- Data privacy: Sensitive customer or financial data shared with public tools can leak or breach regulations.
- Product secrecy: Paste in an unreleased feature, and it could surface outside your company before launch.
Case in Point: When Speed Outruns Oversight
Earlier this year, investor Jason Lemkin ran a 12-day experiment to see how far an AI coding agent could take him in building an app on Replit. On day nine, things went sideways: the agent ignored his instructions, deleted a live production database, and even fabricated data to cover its tracks.
Replit’s CEO apologised and called it “a catastrophic failure”, promising safeguards to prevent it happening again. The real lesson is about what happens when AI is left to run without experienced oversight. When the language is complex and convoluted, it is unreasonable to expect someone who is not skilled in engineering to be able to unpick and detect data leakages.
Lemkin was deliberately stress-testing the system. In a production environment, no team would (or should) rely on AI autonomy without an engineer in the loop. That’s the difference between a clever demo and a sustainable workflow: AI moves quickly, but engineers make sure it moves safely
The Trade-Off: Speed vs. Safety
For many executives, the choice feels binary:
- Use public AI tools and risk leaks, downtime, or worse.
- Avoid AI altogether and protect your assets, but miss out on productivity gains.
Neither extreme is ideal. As one CTO put it: “I’d rather stay secure and slower, than faster and exposed.”
But there are alternatives. And we have begun exploring these with our clients.
Safer Ways Forward
- On-prem AI: Hosting LLMs inside your infrastructure ensures no data leaves your environment.
- Enterprise-grade contracts: Vendors like Azure OpenAI or Anthropic Enterprise provide legal clauses guaranteeing your data won’t be stored or used for training.
- Hybrid approaches: Keep sensitive work on private models, use public ones only for low-risk tasks and testing.
These options put companies back in control, striking a balance between innovation and protection.
The Engineer’s Role
A machine can’t weigh legal exposure, reputational damage, or product context. They can’t understand the intricacies of your systems, or which algorithm, code or datapoint is of the highest value. Engineers can.
- They design the safeguards.
- They integrate AI into secure workflows.
- They spot the risks before tools go rogue.
Without engineers in the loop, you don’t have oversight, you just have speed. And as Replit’s case showed, speed without safety can do real harm. That’s why at MWS we interrogate our methods rigorously alongside our clients to find the most efficient, yet secure method of development possible. Our engineers are regularly updated on safeguarding practices in an ever-evolving technological landscape to keep building faster and safer.
How Do I Choose?
The choice for companies should never be – should I use AI or not? It’s about which AI, under what conditions, and with what oversight. How can you take a tool so powerful, and fine tune it for your specific needs. Engineers act as the bridge.
Read the full Replit article here
