GCP Basic Roles: Owner, Editor, Viewer Permissions

When you start working with Google Cloud Platform, one of your first decisions involves choosing how to grant permissions to team members and service accounts. The GCP basic roles owner editor viewer model represents the simplest approach to access control, but understanding when to use these broad permissions versus more granular alternatives is critical for both security and operational efficiency.

This decision impacts how quickly you can onboard team members, how well you can enforce the principle of least privilege, and how you manage compliance requirements. Many organizations start with basic roles because they're straightforward, only to discover later that they need finer control. Understanding this trade-off from the beginning helps you make informed choices that scale with your projects.

What Are GCP Basic Roles

Basic roles in Google Cloud are the original permission model that existed before IAM (Identity and Access Management) introduced predefined and custom roles. The three basic roles are Owner, Editor, and Viewer, and they apply across all Google Cloud services within a project.

The Viewer role grants read-only access to resources. Someone with Viewer permissions can see configurations, view data, and examine settings, but cannot make any changes. Think of this as the ability to observe everything happening in a project without the power to modify anything.

The Editor role includes all Viewer permissions plus the ability to create, modify, and delete resources. An Editor can deploy applications, modify Cloud Storage buckets, execute queries in BigQuery, and perform nearly all operational tasks. However, Editors can't manage access control or billing.

The Owner role includes all Editor permissions plus full administrative control. Owners can grant and revoke IAM permissions, set up billing, and delete the entire project. This role represents complete authority over a Google Cloud project.

Here's a concrete example to illustrate the differences. Imagine a furniture retailer running an ecommerce platform on GCP with inventory data in BigQuery, product images in Cloud Storage, and application code running on Cloud Run.

A data analyst with the Viewer role could run queries against the inventory tables in BigQuery to generate reports, but couldn't create new tables or modify existing data. They could browse the Cloud Storage buckets to see what images exist, but couldn't upload new product photos or delete existing ones.

A backend engineer with the Editor role could deploy new versions of the application to Cloud Run, create new BigQuery datasets for analytics experiments, and upload updated product images to Cloud Storage. However, they couldn't add new team members to the project or change who has access to sensitive customer data.

The engineering manager with the Owner role could do everything the Editor can do, plus grant the data analyst Viewer access, assign Editor permissions to the backend engineer, and configure billing alerts for the project.

The Appeal of Basic Roles

Basic roles offer significant advantages that explain why they remain popular, especially in smaller teams and development environments. The primary benefit is simplicity. You don't need to understand the hundreds of specific permissions that exist across Google Cloud services. Instead, you make a straightforward choice based on what someone needs to accomplish.

When you bring a new developer onto a project, assigning them the Editor role gives them immediate productivity. They can start working with Compute Engine instances, deploy to Cloud Functions, query data in BigQuery, and interact with Cloud Storage without waiting for you to identify and grant dozens of specific permissions.

This simplicity also reduces cognitive load during the early stages of a project. When you're prototyping an application or validating a business idea, spending time crafting detailed permission policies can feel like premature optimization. Basic roles let you focus on building rather than administering.

Another advantage appears in learning environments. When someone is preparing for Google Cloud certification exams or learning the platform, basic roles provide a clear mental model. You understand that some people can view, others can edit, and owners can do everything including access management.

Critical Limitations of Basic Roles

The same broad scope that makes basic roles convenient also creates serious security and operational problems. The fundamental issue is that basic roles violate the principle of least privilege, which states that users should have only the minimum permissions necessary to perform their job functions.

Consider a mobile game studio using Google Cloud to process player telemetry data. The game sends millions of records per day about player actions, achievements, and in-game purchases to BigQuery for analysis. A data scientist needs to analyze this data to improve game balance and identify engagement patterns.

Granting this data scientist the Viewer role seems reasonable at first. However, Viewer permissions extend to the entire project. This means the data scientist can also view production application secrets in Secret Manager, see configuration details for Kubernetes clusters running the game servers, and examine billing information. None of these capabilities are necessary for their analytics work, and each represents a potential security risk.

The Editor role creates even larger problems. Suppose you have a contractor helping to build data pipelines in Dataflow. Assigning them the Editor role gives them the ability to create and run pipelines, but it also allows them to delete production BigQuery datasets, modify Cloud Storage buckets containing critical backup data, and spin up expensive Compute Engine instances for cryptocurrency mining.

These aren't hypothetical concerns. Organizations regularly experience security incidents because someone with Editor permissions accidentally deleted production resources, or because a compromised account with broad permissions gave an attacker extensive access to Google Cloud infrastructure.

Another limitation appears when you need to demonstrate compliance with security frameworks. Many compliance standards require documented proof that access follows the principle of least privilege. Auditors want to see that a database administrator has specific database permissions, not broad Editor access that also includes compute, storage, and networking capabilities.

The Alternative: Predefined and Custom Roles

Google Cloud offers two more granular alternatives to basic roles. Predefined roles provide curated sets of permissions for specific job functions across various services. Custom roles let you define exactly which permissions a user or service account should have.

Predefined roles address common use cases with appropriate permission boundaries. Instead of granting someone Editor access, you might assign them the BigQuery Data Editor role, which allows them to create, update, and delete data in BigQuery but provides no access to other Google Cloud services. Or you could use the Storage Object Viewer role to grant read access to Cloud Storage without giving visibility into other resources.

These predefined roles align with actual job functions. A data engineer might have BigQuery Data Editor, BigQuery Job User, and Dataflow Developer roles. This combination lets them query data, run BigQuery jobs, create and manage Dataflow pipelines, and accomplish their work without unnecessary access to compute instances or networking configurations.

Custom roles go further by letting you assemble exactly the permissions your situation requires. Suppose you have a service account that runs a scheduled job to export data from BigQuery to Cloud Storage. You can create a custom role containing only bigquery.jobs.create, bigquery.tables.export, storage.objects.create, and storage.objects.delete if the job needs to clean up old exports.

This service account can perform its designated function but nothing else. If an attacker compromises the credentials, the blast radius is limited to data exports rather than extending to the entire project.

How IAM in Google Cloud Handles Permission Complexity

Google Cloud IAM provides tools that make working with granular permissions more manageable than it might initially appear. The permission structure follows a consistent pattern across services, which helps you build mental models for how access control works.

Every permission in GCP follows the format service.resource.verb. For example, bigquery.datasets.create allows creating datasets in BigQuery, while storage.buckets.delete allows deleting Cloud Storage buckets. This naming convention makes permissions discoverable and understandable.

The IAM policy hierarchy also helps you manage complexity. You can assign roles at the organization level, folder level, project level, or on individual resources. Policies are inherited down the hierarchy, which means you can set broad permissions at higher levels while adding specific permissions at lower levels.

For the furniture retailer example mentioned earlier, you might structure permissions this way. At the organization level, a security team has Organization Viewer to audit all resources. At the folder level, different business units have Editor access to their respective folders. At the project level, individual developers have predefined roles scoped to their specific work. At the resource level, a sensitive BigQuery dataset containing customer payment information has additional restrictions limiting access to only the finance team.

This hierarchy lets you balance convenience with security. You don't need to manually assign the security team Viewer access to every single project because they inherit it from the organization level.

Google Cloud also provides policy troubleshooting tools that help you understand why someone does or doesn't have access to a resource. The Policy Troubleshooter shows you which roles grant specific permissions and where in the hierarchy those assignments occur. This visibility makes it easier to audit permissions and identify overly broad access.

Another helpful feature is IAM recommendations, which analyze actual usage patterns and suggest removing permissions that principals haven't used in recent months. If someone has Editor access but only performs read operations, the system can recommend downgrading them to Viewer plus specific write permissions for the resources they actually modify.

A Realistic Scenario: Evolution from Basic to Granular Roles

Consider a telehealth platform that started as a small startup and grew into a company serving thousands of patients. Initially, the three-person engineering team all had Owner access to their single Google Cloud project. This worked fine when everyone knew each other and trusted implicitly.

As the company grew, they hired specialized roles. They brought on a data analyst to study patient engagement patterns, a DevOps engineer to improve deployment processes, and several contractors to build new features. At this point, they stopped giving everyone Owner access and started using basic roles more carefully.

The data analyst received Viewer access, which let them query patient engagement data in BigQuery. However, this also gave them visibility into production application configurations, encryption keys in Cloud Key Management Service, and billing details. During a compliance audit for HIPAA certification, auditors flagged this as a violation of least privilege principles.

The contractors received Editor access so they could deploy code to Cloud Run and test their work. One contractor accidentally deleted a Cloud Storage bucket while cleaning up their development resources, not realizing they were looking at the production bucket that stored patient medical documents. The data was recovered from backups, but the incident consumed hours of engineering time and raised serious questions about their access control model.

The company decided to migrate away from basic roles. Here's how they restructured permissions using predefined and custom roles.

The data analyst received BigQuery Data Viewer (can query tables), BigQuery Job User (can run queries), and a custom role with storage.objects.get only for the specific Cloud Storage bucket containing exported analytics reports.

The DevOps engineer received Cloud Run Developer, Kubernetes Engine Developer, Logging Admin, and Monitoring Metric Writer.

Contractors received Source Repository Writer (can commit code), Cloud Build Editor (can trigger builds), and Cloud Run Developer (can deploy to designated development environments only, controlled via resource-level IAM).

The production deployment service account received a custom role with only these permissions: cloudrun.services.update, cloudrun.services.get, and cloudrun.revisions.create.

This restructuring required upfront work. The DevOps engineer spent several days identifying which permissions each role needed, testing that everyone could still perform their jobs, and documenting the new permission model. However, the ongoing benefits were substantial.

When the next compliance audit occurred, they could demonstrate that the data analyst had access only to analytics data, not to production secrets or billing information. When they hired additional contractors, they confidently assigned narrow permissions without worrying about accidental deletions. When an engineer left the company, they could quickly verify that the departing employee had no Owner-level access that could pose a risk.

Comparing Basic Roles vs. Predefined/Custom Roles

The choice between basic roles and more granular permission models involves clear trade-offs. Understanding these helps you make the right decision for your situation.

Factor	Basic Roles	Predefined/Custom Roles
Setup Complexity	Very simple, three clear options	Requires understanding specific permissions and job functions
Time to Grant Access	Immediate, no research needed	Requires identifying appropriate roles and testing
Security Posture	Poor, violates least privilege	Strong, follows least privilege principle
Blast Radius	Large, compromised account affects entire project	Limited to specific services and operations
Compliance	Difficult to justify in audits	Aligns with compliance requirements
Maintenance	Low, roles rarely need adjustment	Higher, may need updates as job functions evolve
Troubleshooting	Simple, either you have basic role or you don't	More complex, requires understanding which roles grant needed permissions
Appropriate For	Small teams, prototypes, learning environments	Production systems, larger teams, regulated industries

The decision often depends on your project maturity and organizational context. When you're learning Google Cloud or building a proof of concept with a small trusted team, basic roles provide appropriate simplicity. The security risks are manageable because you have few resources and limited sensitive data.

As you move toward production and add team members, the balance shifts. The incremental effort of identifying appropriate predefined roles becomes worthwhile compared to the security and compliance risks of broad permissions.

An effective middle ground involves using basic roles initially but planning the transition to granular roles before you reach production. During development, grant your core team Editor access. Before launch, audit who has which permissions and assign predefined roles that match actual responsibilities. Reserve Owner access for a very small number of administrators who genuinely need project-level control.

For service accounts, avoid basic roles entirely. Service accounts represent programmatic access, and their credentials can be compromised through application vulnerabilities, leaked configuration files, or stolen keys. Every service account should have a custom role containing only the specific permissions its workload requires.

Making the Right Choice for Your Situation

The GCP basic roles owner editor viewer model serves specific purposes well but creates significant problems when misapplied. Your decision should be guided by several questions.

First, what's the maturity of your project? A weekend hackathon project where you and a friend are experimenting with Cloud Functions doesn't need granular IAM policies. Assign each other Editor access and focus on building. A production system processing customer transactions requires careful access control regardless of team size.

Second, what are your compliance requirements? If you operate in healthcare, finance, or other regulated industries, you likely need documented evidence of least privilege access. This effectively eliminates basic roles as an option for production systems.

Third, how much do you trust your team? This question is uncomfortable but important. A small team of senior engineers who have worked together for years can safely operate with broader permissions. A team that includes contractors, interns, or members you don't know well requires tighter controls.

Fourth, how complex is your Google Cloud environment? A single project with a handful of resources makes basic roles more manageable. An organization with dozens of projects, multiple environments, and complex resource dependencies demands the structure that predefined and custom roles provide.

Many organizations use a hybrid approach. Development and staging projects might use basic roles for team members to enable rapid iteration. Production projects use predefined and custom roles exclusively. Service accounts always use custom roles regardless of environment.

This hybrid model balances productivity with security. Developers can experiment freely in development environments without waiting for permission changes. Production systems maintain strict access control to prevent accidents and limit the impact of compromised credentials.

Conclusion

Understanding GCP basic roles owner editor viewer permissions gives you the foundation to make intelligent access control decisions in Google Cloud. Basic roles offer simplicity that makes them attractive for learning, prototyping, and small teams. However, their broad scope creates security vulnerabilities and compliance challenges that make them inappropriate for production systems in nearly all cases.

The evolution from basic roles to predefined and custom roles represents a natural progression as your projects mature. Starting with basic roles while learning is reasonable. Continuing to use them in production when you understand the alternatives is not. Thoughtful engineering means recognizing when simplicity becomes a liability and investing in the appropriate level of access control for your situation.

As you prepare for Google Cloud certification exams, focus on understanding not just what each role does but when and why you would choose one approach over another. Exam questions frequently test your ability to select appropriate IAM configurations for specific scenarios rather than just memorizing permission lists.

For readers looking for comprehensive exam preparation that covers IAM, security, and the many other critical topics in Google Cloud, check out the Professional Data Engineer course. The course provides structured learning paths, hands-on scenarios, and practical guidance that helps you build real-world expertise alongside certification readiness.