In 2025, nearly every engineering team is leaning on generative AI tools like GitHub Copilot, Amazon CodeWhisperer, and Claude to accelerate Infrastructure-as-Code. These tools make it faster than ever to spin up Terraform configs or CloudFormation templates, boosting productivity across the board.
But speed comes with a catch: AI-generated code often works, it deploys successfully; while quietly skipping over critical security configurations. That gap only shows up later during audits or incident reviews, forcing teams into weeks of rework, compliance headaches, and unexpected risk exposure.
The problem isn’t that GenAI writes “bad” code. It’s that it produces functional code without the built-in guardrails teams need to stay secure.
The Training Data Reality Check
Consider what generative AI models truly learn from. They learn from millions of coding examples sourced from GitHub, Stack Overflow, documentation platforms, and open-source repositories. That might appear impressive until you realize what’s truly contained in that training data.
It includes years of quick demos, proof-of-concept snippets, and tutorial code that prioritizes “functionality” over “security”. Basic configurations that bypass encryption, use permissive access policies, and ignore compliance requirements.
When you ask Copilot to create an S3 bucket, it produces what it has encountered most frequently in its training data. And what shows up the most frequently? Basic, operational setups lacking the security measures that production environments truly require.
Your AI assistant isn’t aiming to develop insecure infrastructure. It's just enhancing the patterns it acquired - patterns designed to showcase functionality, rather than to meet SOC 2 audit standards. That’s why it’s important to maintain an eye on security as you integrate AI into your devops workflow.
Where Generative AI Hits Its Limit
Generative AI is outstanding at solving innovative and creative problems. If you request it to design a caching architecture or enhance a database query, it can combine insights from multiple sources and generate something genuinely useful.
But security configurations work differently. They don’t benefit from creative interpretations or multiple approaches. It requires accurate implementations.
For example, encryption standards. There’s a specific way to configure encryption for S3 buckets that matches your security policies. Multiple code variations don’t represent innovation – they represent risk. The same applies to firewall rules, IAM policies, and compliance configurations. These aren’t creative challenges where generative AI can explore different possibilities. They’re precision tasks where one correct answer exists.
When generative AI handles security, it treats these configurations like creative problems and suggests three different approaches. They all might be technically functional, but only one might match your compliance requirements.
This mismatch isn’t about AI capabilities. It’s about what the task demands. Infrastructure design needs creativity. Security configurations need deterministic precision.
The Consistency Problem
Here's what happens when teams rely entirely on generative AI for infrastructure security: the same misconfiguration gets "fixed" differently across different repositories, teams, and time periods.
Your AI assistant might suggest enabling encryption one way in January and a completely different way in March. Both approaches might work, but now you have inconsistent security implementations across your infrastructure. Good luck explaining that to auditors.
Worse, some of those "fixes" might introduce subtle issues that don't surface until production load hits or a specific edge case triggers. By then, you're debugging AI-generated code that looked perfect during code review.
Deterministic Security: Same Problem, Same Solution
The deterministic method isn’t about guessing. It’s about following rules. Rather than learning from various patterns, they rely on well-defined security frameworks, compliance standards, and cloud provider best practices.
When a deterministic system comes across an unencrypted S3 bucket, it doesn't brainstorm different options or creative solutions. Instead, it directly applies the required exact encryption settings outlined in your security policy, just as specified in AWS security guidelines.
This predictability matters for three reasons:
- Audit becomes straightforward. Every configuration change will trace directly back to a specific compliance requirement. If auditors question why something was configured a certain way, you can point to the exact standard it implements.
- Your engineering team knows what to expect. No surprises about how security issues get handled. The same misconfigurations get the same fix across every repository, every time.
- It ensures reliable scaling without inviting any chaos. As your infrastructure grows, you’re not managing different approaches to encrypting data. You want one solution to be applied consistently across hundreds of resources.
The Integration Reality
Most teams aren't picking one approach over the other. They use both, just at different points in their workflow.
Generative AI handles the initial build. Need a VPC with subnets and an EKS cluster? Your copilot generates the foundation in minutes.
Then deterministic systems catch what generative AI missed. Unencrypted storage. Overly permissive IAM roles. Configurations that violate your compliance requirements. These get flagged and fixed automatically.
Engineers review the security fixes the same way they review any other code change. Clear diffs, explanations tied to specific security standards, all delivered as pull requests in their existing workflow.
No jumping to security dashboards. No opening tickets. No context switching to external tools. Security validation happens where development happens.
Implementation Strategy
Teams using both approaches typically follow a similar pattern.
They start by defining security policies as code - specific rules that deterministic systems can apply the same way every time. Not vague guidelines, but exact configurations tied to compliance frameworks.
Security checks run automatically when code gets generated. No manual scanning, no breaking out of the development workflow. The analysis happens in the background while engineers keep working.
Fixes show up as pull requests. Each one comes with a clear explanation of what changed, why it changed, and which security standard it addresses. Engineers review these the same way they review any code change.
The goal isn't replacing AI tools or cutting engineers out of decisions. It's using generative AI for what it does well and deterministic systems for what they do well.
Measuring Success
What actually changes when teams get this right?
How fast security issues get fixed. Before automation, it took days of back-and-forth between security and engineering teams. After, it takes minutes from detection to merged fix.
How consistent security configurations are across the codebase. Fewer variations in how encryption gets implemented, how IAM policies are structured, how network rules are configured.
How much time audits consume. Less scrambling to gather evidence, fewer hours explaining why configurations look different across repositories.
How engineers feel about the process. Less context switching between writing code and researching security requirements. More time building, less time fixing.
The Bottom Line
Your copilot doesn't need replacing. It needs support for the parts it struggles with.
Generative AI speeds up the development by handling the creative, contextual work of infrastructure design. But security configurations require precision it can’t consistently deliver. The teams shipping secure infrastructure at scale use both – AI for speed and deterministic AI for precision.
In production environments, "close enough" security isn't good enough. Your copilot needs a security co-pilot that delivers the same fix for the same problem, every time, across every repository.
That's how you build faster without building security debt. See how Gomboc’s deterministic AI integrates with your existing workflow.
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