How Nirmata Replaces Manual YAML with AI-Driven Policy Automation

Kubernetes policy has always promised more control, better consistency, and fewer security mistakes. Policy work often falls on platform teams that are already very busy. These teams have to write YAML by hand, track drift across clusters, and turn security or compliance requests into rules that developers will actually follow.

Twain Taylor, editor at Software Plaza, talked to Jim Bugwadia, founder and CEO of Nirmata, on the Software Plaza podcast about how AI is changing that model by moving policy-as-code away from authoring by hand and toward automating based on outcomes.

Kubernetes governance doesn’t usually break down because no one wants it to. Usually, teams know what they want: to stop dangerous configurations, enforce standards, get rid of old resources, and show that they are following the rules. The idea behind Nirmata is that AI should do more of the work of translating and checking, and people should decide what outcomes are important and where the limits should be.

Why does the manual policy work stop scaling

At first, policy as code seems like a simple plan, but then the daily tasks start to pile up. Someone on the team needs to make the rules, write the YAML, test it, put it out there, and keep an eye out for problems. They should also go over the process again if a new version of Kubernetes or a change in how things are run breaks the assumption. 

After that, this process has to be repeated for each cluster, team, and compliance requirement. This becomes a problem for managing the backlog. This is the gap Nirmata is trying to close.

There is an AI Platform Assistant that writes policy as code across the infrastructure, runs it, and ensures it works. There is also a Control Hub in the tool, which makes AI part of your ecosystem’s centralized governance workflow instead of just a feature that you see on the dashboard. The solution focuses on reducing the amount of manual translation between policy goals and operational action.

From syntax work to intent-driven policy

While the AI tool can make policy files, that’s not the most interesting thing about their approach. What’s more useful is that AI can help platform teams start with a goal, which then creates policy logic that understands the situation.

Nirmata’s product includes natural language policy creation, generated compliant YAML and CEL, remediation workflows, and a team-based CLI assistant. So teams no longer start with syntax, but with the problem they need to solve.

One platform engineer might want to demand image provenance, stop dangerous pod settings, or even clear out unused resources after a deployment cycle. Because of Nirmata’s intent-driven workflow, the AI assistant will help turn that request into policy, check it against the surrounding environment, and also support validation before the rule goes live.

Because AI can handle a large share of repetitive authoring, your team has more room to focus on operational standards, exception design, and architecture choices. This raises the value of expertise and increases the importance of domain knowledge, not the other way around.

Why guardrails beat gates

The difference between guardrails and gates was one of the most useful ideas that was talked about in the webinar. Most teams want strict enforcement because they think that stopping bad configurations before they go live is the best thing to do. However, strict policy can easily become frustrating, which makes developers look for ways to get around the policy and hurts the policy program’s credibility.

The guardrail model that Nirmata uses seems to work better for most businesses because it still lets platform teams set rules for what is and isn’t acceptable behavior. On the other hand, it allows for more gradual rollouts, guided fixes, and more clear ways to comply.

In Nirmata, this approach is supported by Kyverno’s dry-run feature, AI-generated remediation, handling of exceptions with owners and expiration, and workflows that connect policy to real engineering processes instead of a separate control layer.

Compliance as an ongoing workflow

Compliance still turns into a repeated scramble in many Kubernetes environments: gather evidence, check clusters, map controls, find gaps, then do it all again a few months later. That is expensive and hard to maintain.

Nirmata is trying to make that more continuous. Its Control Hub ties policies to standards such as CIS, PCI, HIPAA, and SOC 2, with reporting and evidence built into the process. That makes compliance less of a one-off audit exercise and more of an ongoing operational task.

Why outcome-driven platforms matter

Another theme from the discussion is the move away from dashboards as the center of the product. Platform teams do not need more charts. They need fewer policy violations, cleaner clusters, and less manual work.

Nirmata’s position reflects that shift. The focus is on detecting issues, suggesting fixes, and keeping policies aligned across environments. The value is not faster text generation. It is reducing the work between policy intent and usable action.

What changes for platform teams

This does not remove platform engineers from the process. It changes where they spend their time. Less effort goes into writing every rule by hand. More goes into deciding what to enforce, where exceptions belong, and how to keep guardrails from becoming blockers.

For developers, that can mean quicker feedback and fewer opaque failures. For security and compliance teams, it can mean more consistent enforcement and less manual evidence gathering.

Where this is headed next

Running Kubernetes isn’t getting easier. The policy now covers operations, cost control, security, and compliance, and drift can show up in more and more places. With the old model, skilled engineers write and keep up with each policy document by hand, which is hard to do on a large scale.

Nirmata suggests a different way of doing things: begin with the goal, let AI create and test the policy path, keep people in the approval loop, and see how well things are going by showing that operations are cleaner instead of more reports. For platform teams trying to keep control without becoming a bottleneck, that is the part worth watching.

For readers who want to hear the whole conversation, the Software Plaza webinar episode with Jim Bugwadia is available for viewing.