In 2024, the dirty little secret was out: over 60% of AI-generated security fixes still had to be torn apart and rebuilt by engineers before they were safe to ship. That’s not “helping,” that’s creating rework.
The reason? Guesswork dressed up as intelligence.
In Infrastructure as Code, “close enough” is a liability. One misplaced variable can derail a deployment, trigger a production outage, or crack open a brand-new security hole. And yet, too many tools are still happy to hand you something that looks right, but isn’t. That’s why in 2025, the conversation has shifted. It’s no longer about whether you’re using AI - it’s about whether your AI is the kind you’d trust in your pipeline. Today, two main approaches dominate the discussion: Generative AI and Deterministic AI.
Generative tools can throw out a handful of ideas for a fix. Deterministic tools will deliver consistent and validated fixes every single time. Both approaches have their uses, but in production, that difference is huge. It’s the gap between rolling the dice and knowing the outcome.
Terraform, CloudFormation, Pulumi - they’re all built for predictability. But once the alerts start stacking up, pull requests keep piling, and every “quick” security review grinds progress to a halt, your delivery speed takes a hit.
A lot of people say you can’t have both speed and safety.
I don’t buy it. You can, if you stop thinking only about shifting left and start focusing on fixing left. That’s where Deterministic AI changes the game.
Why automated security fixes aren’t as simple as they sound
On the surface, it seems easy: spot a misconfig, patch it, ship it. But those patches touch sensitive infrastructure, have to follow compliance rules, and must be safe to deploy right now without breaking something else in the process.
Get it wrong, and you’re not just introducing a bug - you’re opening a new security hole. That’s why the way a tool arrives at a fix matters just as much as the fix itself.
Generative AI: It’s Benefits and the Limitations You Can’t Ignore
Generative AI is trained on massive amounts of data and can:
- Suggest different ways to solve a problem
- Explain a vulnerability in plain language
- Offer several options for you to review
But it’s unpredictable. Give it the same problem twice, and you might get two very different answers,some of which look fine but fail when deployed. And in production, unpredictability isn’t just inconvenient, it’s a trust-killer.
What is Deterministic AI?
Deterministic AI works on a different principle: same input = same output - every time. In IaC security, this means that each detected violation leads to a known-good, policy-approved fix.
Why it fits security:
- Consistency: The same issue always gets the same fix across every repo and environment.
- Explainability: Every change is traceable to the exact policy-approved rule.
- Compliance alignment: Fixes meet defined security and regulatory requirements.
- Safety at scale: Proven to work across complex codebases and IaC frameworks
While GenAI might “guess” the right fix, Deterministic AI applies a precise, rule-bound patch that you can ship in production without hesitation.
Precision Beats Creativity in Security
In creative writing, having variations is an asset but in automated security fixes, it can mean inconsistency which can lead to risk.
With deterministic AI, engineers don’t have to review every line of the code wondering if the AI “got it right”. They review knowing the fix is consistent, policy-aligned and tested in production-like conditions. Over time, this builds trust in the automation process.
Deterministic AI vs. Generative AI
Real-World Scenarios
Imagine this: your CI/CD pipeline scans your Infrastructure as Code and detects security misconfigurations, such as an open security group that allows access from any location.
- With GenAI: The tool alerts the issue and suggests fixing it, but it also adds a few unrelated configuration changes - subtle and risky. They haven’t been tested against your policies, even though it appears harmless. Suddenly, you’re left analyzing extra changes you didn’t request, and wondering if they’ll break something downstream.
- With Deterministic AI: The same tool alerts and applies one single, exact policy-defined change. No surprises, no unnecessary changes, and no need to cross-check. Every time that misconfiguration appears, it’s fixed the same way, whether it is in one repo or a hundred.
This difference means you’re not just closing gaps, you’re doing it in a way that’s predictable, secure, and easy to trust at scale.
Why Engineers and Developers Need Deterministic AI
For developers, the real challenge in security isn’t about finding the issue - it’s the mental burden of fixing it without breaking something else. The constant jump between tickets, code reviews, and security dashboards takes away the attention from building features and delivering real value.
That’s where Deterministic AI changes the game. Rather than piling on hundreds of alerts, it delivers:
- Fixes directly in pull requests, eliminating the need to install or use third-party dashboards.
- Clear policy context behind every change in simple, developer-friendly language.
- Predictable clean diffs for engineers to verify instead of hours analyzing vague code suggestions.
- Same fix for the same issue which reduces drift and eliminates unexpected bugs.
This isn’t just about safer code — it’s about protecting developer focus so teams can progress without adding hidden security debt. Gomboc delivers this approach end-to-end: embedding deterministic, policy-aligned fixes straight into your PR workflow so security improvements happen in the background while you continue shipping features.
The Bottom Line: Precision Wins
Automated GenAI code security fixes are useful but for IaC environments, “guesswork” isn’t sufficient. Deterministic AI delivers merge-ready, policy-aligned fixes that don’t just detect risk - they remove it before it ever hits main.
If you’ve ever wondered what security automation would be like if it just worked — no drift, no bottlenecks, no second-guessing — you’re closer than you think.
Learn how Gomboc’s Deterministic AI powers reliable, production-ready security fixes →
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