IA Governance: JetBrains bet on centralized measurement instead of restrictions for developers

JetBrains is turning the key to AI tool management for development teams. Instead of starting with hard restrictions — such as blocking ChatGPT, Copilot or other assistants — the company is building a centralized measurement and governance infrastructure. The goal is to give technical leaders clear visibility on which tools are adopted, by whom and how often, before any punitive policy. The approach was recently detailed in InfoQ and signals an important trend: governing AI with data, not with fear.

IA Governance: Why did JetBrains choose to measure rather than block?

Logic is simple: you cannot manage what you do not measure. Instead of assuming that all AI tools are a risk and block them preventively, JetBrains understands that developers are already using these solutions — often on their own, with personal accounts and without supervision. Centralizing the measurement infrastructure allows companies to know exactly what tools are in use, which are ignored and which generate real value. It is a governance posture that prioritizes context intelligence before rigid control.

How to implement centralized measurement in AI governance?

The proposal is to create a single point of observability. Instead of relying on manual reports or silos per team, JetBrains platform aggregates data from different AI tools in one place. This control panel gives metric managers such as adoption rate, call frequency and even cost associated with each tool. With this data, it is possible to make more rational decisions: train teams, adjust permissions or negotiate corporate licenses — all based on evidence, not assumptions.

The approach also reduces friction with developers, who often view blocking policies as obstacles to productivity. By showing transparency and giving the team a voice in the choice of tools, governance becomes an ally, not an enemy.

AI governance: impacts on devs and technical leaders

For developers, the change is subtle, but relevant. Instead of facing barriers, they gain a more structured environment to safely use AI. Centralized measurement still allows you to identify tools that actually increase delivery speed — something that agile teams value.

For technical leaders and CTOs, the benefit is the ability to answer difficult questions with data: how much are we spending on AI? Which tools replace others? Where is there a risk of data leak? With centralized infrastructure, these answers cease to be achism and become metrics. This is particularly relevant in a scenario where the adoption of AI grows faster than the ability of companies to create governance policies.

Why is AI measurement crucial for Brazilian devs?

Brazil has a community of large developers and increasingly exposed to AI tools. Many professionals already use code assistants on a daily basis, often without formal approval from the company. The JetBrains approach serves as a reference for Brazilian companies that are beginning to deal with the same problem: how to allow innovation without giving up security and control. Instead of copying US-ready policies, the centralized measurement model can be adapted to local reality, focusing on transparency and collaboration between engineering and security teams.

The initiative also dialogues with a larger movement of AI governance, which already appears on other fronts — such as an open source AI manifesto defended by Mark Zuckerberg and the search for more control over generative models. If the trend is consolidated, the role of the technical leader ceases to be that of "policing" the use of AI and becomes that of "orquesting" the right tools for each context.

5 steps to measure and govern AI in your company

JetBrains suggests a small start. The first step is to map the AI tools already in use — no judgments. Then define which metrics are relevant: number of active users, frequency of use, cost per call, type of processed data. On this basis, it is possible to create permission policies that evolve as the team learns. The key is not to stop adoption while the measurement is not ripe. Centralised infrastructure allows governance to grow along with use, rather than arrive as a handbrake after the car has already walked.

According to InfoQ, JetBrains is building this structure for its own products and intends to offer customers a similar path. The message is clear: the future of AI governance in development teams is not to ban tools, but to understand deeply how they are used. For those leading engineering teams, this is a change of mentality that is worth following closely.

Frequently asked questions about measuring AI governance

Is JetBrains prohibiting the use of AI tools?

Nope. The company adopted the opposite approach: instead of restrictions, it is centralizing measurement and governance to give visibility and control over the use of tools.

How does centralized measurement help in safety?

It allows technical leaders to identify which tools process which data, facilitating the application of security policies based on real risk, rather than blocking everything just in case.

Can this practice be applied outside the JetBrains ecosystem?

Yes, the concept of centralized governance by measurement is independent of the brand. Companies can adopt the same logic with internal dashboards or observability solutions to monitor the use of AI among their developers.

The original source of this news is available in InfoQ, which brings more technical details about the implementation of JetBrains.