The Importance of Trust in AI-Generated Code

A race car isn’t fast because of the engine alone—it’s the brakes that make speed safe and controlled. Trust enables acceleration.

Artificial intelligence is changing how we build software. It speeds up development and helps teams ship faster. But with that speed comes a big question: Can we trust the software AI creates?

In a world of AI-powered code, trust isn’t a bonus—it’s a must.

Why Trust Matters Now

AI coding tools like GitHub Copilot and Gemini Code Assist are everywhere. Developers are using them to build faster and automate more. But AI also brings new risks.

AI doesn’t just help write code. It changes how software is built. It changes who builds it. And it changes what’s possible—both good and bad.

I sat down with Danny Allan, CTO of Snyk, to talk about how software development is evolving and what we need to do to ensure we can trust it. “We’re in a perfect storm right now,” he declared.

Allan described the three converging fronts of the perfect storm: AI is creating more code than ever. That code is often less secure than what senior developers would write. And AI-native applications have a larger attack surface, especially when large language models are involved.

A recent study by Snyk found that 96% of CISOs are worried about how AI is being used in development. That concern is well-placed.

AI Security Is Different

AI-generated code may look like regular code—but it’s not. The risks are different. That’s why we need a new approach.

LLMs add new dangers. Prompt injection, model theft, data leaks and poisoned training sets are all part of the picture. Allan noted we are also still not logging prompt history or tracking model outputs in most organizations.

He compared today’s AI rush to the early days of cloud. “Back then, no one was locking down instances or logging access,” he said. “Now, we’re doing the same with AI models.”

AI isn’t just another tool. It’s a new layer of infrastructure. And right now, it’s going mostly unsecured.

AI Trust Platforms

That’s where AI trust platforms come in. These tools aim to secure the entire AI pipeline—from how the code is written to how the models behave.

Snyk announced the launch of its own AI Trust Platform to help address this. It includes:

• Secure scanning for AI-generated code
• AI context enrichment for better accuracy
• Learning modules to teach developers secure practices
• Guardrails for prompts and responses
• Tools to manage model licenses and provenance

Allan explained the platform’s goal: “Technology can never achieve its full potential unless we trust the technology that we’re using.”

Developers Won’t Be Replaced—They’ll Evolve

The rise of AI coding assistants has sparked fears that software engineers might soon be obsolete. But that vision misses the bigger picture. AI doesn’t eliminate the need for developers—it changes what they do and how they add value.

Danny Allan sees a future where developers fall into three evolving categories:

1. General users: These are non-developers—business analysts, marketers, even executives—who use AI to build simple apps or automate tasks. With the right prompt, they can generate dashboards, create workflows or spin up web apps without writing a line of traditional code.
2. Experienced developers: These are the engineers who guide the AI, not just use it. They understand system architecture, application logic and how software behaves at scale. Their role is shifting from writing code line-by-line to designing prompts, validating outputs and assembling systems with reusable AI-generated components. They’re also responsible for spotting edge cases, reviewing AI-generated suggestions and providing critical oversight.
3. Low-level specialists: This group will continue to write the code that powers the tools the rest of us use—whether it’s compiler logic, cryptographic functions or model runtime environments. They may work in assembly, Rust or other performant languages and their expertise will remain essential for maintaining infrastructure and solving complex problems that AI can’t yet handle. Much like today’s COBOL engineers, these specialists will be rare, in high demand and central to mission-critical systems.

In this model, AI doesn’t shrink the developer community—it expands it. Everyone becomes a builder, but with different levels of sophistication and responsibility. And as AI-generated code becomes more common, the need for oversight, security and skilled guidance only grows.

AI is a powerful tool. But human judgment—especially when it comes to security, ethics and edge-case logic—remains irreplaceable. The challenge isn’t how to replace developers. It’s how to re-skill and redefine them for the AI era.

Trust Is the Competitive Edge

As AI tools become more connected, through systems like Model Context Protocol, companies must make sure those connections are safe. Snyk, for example, is offering both integrations and security guidance for MCP. That’s key. Every new tool is also a new attack surface.

Allan shared a quote from his CEO to drive the point home: “The reason why racers can go fast is because they have brakes. It’s not because of the engine. You can go faster. And so if you want to trust it, it’s the brakes that you’re trusting. It’s not the engine itself.”

Put simply, speed without safety leads to disaster. But trust lets you go faster with confidence.

What Comes Next

AI will keep changing how we work. That’s a good thing. But trust needs to grow with it.

The companies that succeed will be the ones who build trust into every layer—from the models they use to the code they ship. That means educating developers, adopting secure tools and setting clear standards.

AI is the engine. Trust is the brake.

And both are needed if we want to go the distance.

Conclusion

In conclusion, trust is a critical component of AI-generated code. As AI continues to change the way we build software, it’s essential to prioritize trust and security. By adopting AI trust platforms, re-skilling developers, and building trust into every layer of the AI pipeline, companies can ensure that their AI-powered software is both fast and safe.

Frequently Asked Questions

Here are some frequently asked questions about AI-generated code and trust:

Q: What is AI-generated code?

A: AI-generated code is code that is written or generated by artificial intelligence tools, such as GitHub Copilot or Gemini Code Assist.

Q: Why is trust important in AI-generated code?

A: Trust is important in AI-generated code because it ensures that the code is secure, reliable, and functions as intended.

Q: How can companies build trust into their AI-powered software?

A: Companies can build trust into their AI-powered software by adopting AI trust platforms, re-skilling developers, and setting clear standards for security and transparency.

Q: Will AI replace human developers?

A: No, AI will not replace human developers. Instead, it will change the way they work and the skills they need to be successful.