Generative AI democratizes infrastructure as code (IaC) development
We’re seeing something significant happen with Infrastructure as Code (IaC). Generative AI, tools like ChatGPT and GitHub Copilot, is removing the traditional skill barrier. Engineers no longer need deep systems expertise to write operational-level code like Terraform modules or Ansible templates. That’s a big shift.
Siri Varma Vegiraju, a Security Tech Lead at Microsoft, put it bluntly: Developers who aren’t infrastructure experts are now using AI to write complete configurations. Ivan Novikov, CEO of Wallarm, added that before AI, writing production-grade Kubernetes or Terraform code was something reserved for Site Reliability Engineers or DevOps specialists. Not anymore. Now, any backend engineer can generate infrastructure logic with a single prompt.
This democratization leads to faster iteration. You reduce the friction between design and delivery. Developers working on a new project or product feature can get their infrastructure right away, no waiting on another team. According to Fergal Glynn, CMO at Mindgard, many developers quietly adopted this workflow on their own. They didn’t wait for policy or permission, they just started using AI as a faster way to get things done.
Milankumar Rana, a senior cloud engineer at FedEx, observed that what started as informal developer behavior is becoming structured. Larger organizations are adapting. They understand the benefits of speed and accessibility, but they’re also aware that structure and oversight are needed. This shift isn’t slowing things down, it’s scaling adoption properly.
If you’re in a leadership role, the takeaway here is straightforward: speed and autonomy are increasing. But with that comes the challenge of coherence. You can’t afford a fragmented infrastructure built by dozens of developers all using AI in isolation. Governance needs to match the pace of development. That means enabling fast development through shared standards, guardrails, and review processes that are built into the workflow from day one.
AI-generated IaC introduces security and misconfiguration risks
The upside of generative AI in infrastructure is speed. The risk is accuracy. It’s easy to generate config files that look correct but miss critical nuances, things a human expert would catch.
Siri Varma Vegiraju from Microsoft gave a simple but serious example: AI-generated Terraform code that creates a storage account with public network access enabled. The config passes validation. It deploys without error. And it leaves your data exposed to the public internet. According to Vegiraju, in more than 90% of real-world enterprise use cases, public access should be off by default. AI doesn’t know your intent. It doesn’t know your environment.
Ivan Novikov at Wallarm raised another critical point. AI doesn’t have context on your infrastructure, your RBAC rules, service relationships, naming policies, secrets, CI/CD flows. When a developer asks an AI to “write a config for API X,” it does exactly that, but in isolation. That isolation is where the risk emerges.
One large SaaS company Novikov spoke with now uses generative AI for 30% of their IaC. But as their AI usage scaled, their CI/CD misfires tripled. Wrong ports. Exposed S3 buckets. Misconfigured secrets. All basic mistakes, but happening more frequently due to AI-generated code being shipped too quickly.
From a C-suite perspective, this is not about stopping AI adoption, it’s about managing how it’s done. Speed is great, but invisible vulnerabilities kill reliability. If you’re scaling AI in infrastructure, you also need to scale validation. That starts with policy enforcement, automated checks for standard infrastructure patterns, and continues with code review and drift detection post-deployment. Tools like tfsec, Checkov, and custom validation layers must become part of your default development pipeline. Otherwise, the gains you make in speed will be outweighed by the time you spend resolving downstream issues that were preventable.
Organizations are evolving towards structured, governed AI adoption in IaC
As AI becomes more embedded in infrastructure workflows, companies are realizing that informal experimentation isn’t enough. What began as silent side usage, developers relying on ChatGPT to generate a config block or resolve a provider error, is now moving into structured, organization-wide frameworks. Enterprises want the power of generative AI, but with governance built in.
Fergal Glynn, CMO at Mindgard, noted a clear pattern: while startups often experiment first, larger organizations are adopting AI-augmented platforms like Torque, which include guardrails to reduce risk. The move is intentional, speed and flexibility are retained, but output is shaped by internal policy and security standards.
Ori Yemini, CTO and co-founder of ControlMonkey, shared a case from a real-world deployment. A customer tried to bulk-generate Terraform for 80 microservices using ChatGPT. The configs worked technically, but none followed the company’s tagging standards, module conventions, or access policies. It created operational chaos. The resolution was a shift to an internal wrapper, an AI interface that adds organizational context to every prompt, pulling in required metadata, conventions, and repository details. That reduced config drift and rework dramatically.
Nimisha Mehta, Senior DevOps Engineer at Confluent, emphasized that AI-forward companies are going further, investing in bespoke internal tools that connect their large language models (LLMs) with proprietary infrastructure data. This includes IDE plugins, playground environments, and custom pipelines where AI suggestions are tested safely before deployment.
If you’re leading an organization, and you expect this technology to scale, you need systems in place that match that scale. Ask if your teams are experimenting in silos or building unified workflows that reflect your company’s standards. Generative AI should not operate without context. You wouldn’t let a contractor rebuild part of your infrastructure without seeing the blueprint. The same applies to AI. Implement internal wrappers, codify team standards into prompts, and make sandbox testing mandatory. Speed is good. Order and alignment are better.
Generative AI accelerates development processes but necessitates human oversight through governance
Generative AI is accelerating development time across the board. Creating reusable Terraform modules, converting shell scripts to Ansible playbooks, scaffolding Pulumi projects in TypeScript, these are quickly becoming routine tasks handled by AI. Engineers who used to spend hours reading documentation or troubleshooting syntax now execute similar work in minutes.
Ori Yemini at ControlMonkey observed that engineers aren’t just using AI to write a security group or config template. They’re using it to reimagine architecture, especially during mid-sprint when blockers arise. In his view, the most effective teams treat AI as a first-draft engine, it gets ideas moving faster but still needs experienced engineers to refine the solution.
Nimisha Mehta from Confluent explained that guardrails are critical. When AI is part of your workflow, accidental breaking changes, whether from AI error or human error, can scale quickly. Guardrails like GitOps pipelines, peer-reviewed pull requests, and automated testing act as a control system. Fergal Glynn added that even the best AI systems, like WISDOM-ANSIBLE, still produce edge-case errors. Manual review remains essential.
AI elevates throughput. That’s clear. But higher velocity does not reduce the need for precision, it increases it. At the executive level, your responsibility isn’t just adopting AI, it’s building the systemic processes that contain and shape its output. Governance isn’t bureaucracy. It’s structure. Treat generative AI as a productive but immature contributor on the team: helpful, consistent, and fast, but blind to your internal standards unless you enforce them.
Leadership should focus on integrating code validation tools, enforcing access controls, and standardizing configuration patterns. This avoids operational drift. You get the benefits of fast development without the risks of unstable infrastructure. When speed and structure work together, you gain more than time, you gain resilience.
AI is expanding beyond code generation into infrastructure observability and automation
Generative AI is no longer confined to writing script files. It’s beginning to influence how teams monitor, analyze, and respond to real-time infrastructure conditions. This shift is pushing infrastructure management into more proactive, autonomous territory.
Siri Varma Vegiraju from Microsoft described early-stage experiments where AI systems ingest telemetry data to propose Infrastructure as Code (IaC) updates on the fly. For instance, when services consistently scale out due to CPU exhaustion, AI can identify the pattern and recommend increasing CPU limits or adjusting autoscaling thresholds. These aren’t theoretical. They’re working concepts tested in environments where telemetry data is already dense and structured.
Nimisha Mehta at Confluent expanded on this with real-world debugging scenarios. She noted that AI is being used to trace data packets through complex networking stacks to isolate causes of failure. Instead of treating symptoms, these AI systems help engineers pinpoint the root issue faster, something especially useful in multi-layered service architectures. While full-scale self-healing systems are still in development, integrating AI for real-time diagnostics and issue isolation is a solid step forward.
The tools here are evolving quickly, but the intent is clear: to shift AI from being reactive, responding to developer queries, to operational, making infrastructure smarter, more adaptive, and easier to manage at scale.
If you’re leading technology strategy, pay attention to where your teams are deploying AI beyond code generation. These telemetry-enabled systems are how AI starts to contribute to infrastructure quality in real time, not just during deployment. While these initiatives require upfront investment and focused experimentation, they offer long-term gains in availability, cost control, and operational awareness.
The balance between automation and oversight still matters. Even when AI can recommend a fix, human approval ensures it aligns with strategic intent and compliance requirements. Don’t wait until these tools become mainstream, build the capacity to test and assess these capabilities now. That’s how future-ready infrastructure is shaped.
The speed of AI deployment can outpace safety protocols if not managed carefully
Generative AI makes deployment fast. Sometimes too fast.
Ivan Novikov at Wallarm highlighted a serious pattern: At one SaaS company, 30% of their IaC was AI-generated. But along with the increase in AI use came a tripling of configuration errors in their CI/CD pipelines, misconfigured ports, incorrect secrets, open endpoints, and access policy errors. These are small oversights, but they scale with every push that bypasses proper validation.
The reason is simple. Developers trust the AI output too quickly. A line of YAML looks correct, and it passes a syntax check. So it gets shipped. And that’s when errors slip into production.
Fergal Glynn from Mindgard emphasized that even with predictive and well-trained models, human review isn’t optional. Advanced systems like WISDOM-ANSIBLE can translate plain English instructions into entire playbooks, but outputs still require manual adjustments, especially in edge cases that models haven’t seen enough times to get right.
Many companies are addressing this with automated validation tools, stuff like tfsec, Checkov, and custom policy scanners that catch problems before deployment. But the root cause is cultural. When speed becomes the goal, safety becomes an afterthought.
Executives should be clear-eyed about the trade-offs. Faster delivery is valuable, but ungoverned speed generates technical debt, security exposure, and operational volatility. If generative AI is part of your DevOps strategy, it must be implemented with defaults that slow things down at the right moments. That means required validation steps before deployment, automated security scanning, enforced standards in CI pipelines, and structured peer review.
Speed isn’t dangerous on its own. But when process discipline drops, one small misconfiguration can create customer-facing incidents, compliance failures, or service disruptions. As AI accelerates development, your governance and safety layers must scale just as fast. Anything else leaves exposure gaps that compound over time.
Key takeaways for leaders
- AI lowers the barrier to infrastructure development: Generative AI enables non-specialist developers to produce infrastructure code quickly, reducing reliance on DevOps teams. Leaders should support this shift by standardizing tool use and enforcing common practices.
- Security risks increase without context-aware checks: AI-generated configurations often lack environmental context, leading to misconfigurations like open ports or public access. Leaders must implement automated validation and human review to maintain security protocols.
- Structured adoption drives results at scale: Organizations shifting from informal AI use to structured, policy-driven frameworks are seeing reduced errors and more consistent outputs. Executives should invest in internal AI wrappers and sandbox environments to scale safely.
- Faster development still requires oversight: Generative AI boosts delivery speed but does not replace human judgment. Leaders should enforce guardrails, GitOps workflows, and peer review to ensure infrastructure quality and compliance.
- AI is moving into real-time ops and observability: Early-stage integrations are using telemetry data to let AI recommend or apply config changes in production. Forward-looking organizations should test these capabilities now to prepare for coming operational shifts.
- Speed without governance compounds risk: AI-driven infrastructure updates that bypass proper checks lead to a spike in deployment errors. Executives must prioritize governance tools and enforce policies that balance velocity with reliability.
