Boosting SecOps Productivity: How to Use Microsoft Sentinel Inside Visual Studio Code

In modern Security Operations Centers (SOC), teams are increasingly expected to deliver engineering‑level quality while maintaining operational speed. As organizations shift toward the Unified Security Operations Platform, combining Microsoft Sentinel with Visual Studio Code (VS Code) has become one of the most efficient ways to build detections, automate workflows, and perform threat hunting at scale.

This blog outlines why Sentinel + VS Code is such a powerful combination, which extensions you need, and how to build an enterprise‑ready SecOps workflow using a development‑as‑code approach

Why Use Visual Studio Code With Microsoft Sentinel?

While the Sentinel web portal is great for daily operations, VS Code unlocks development capabilities that large security teams rely on for consistency, automation, and collaboration.

Visual Studio Code has become the primary development and engineering environment for many Microsoft Sentinel customers because it enables a more scalable, automated, and professional way to manage your SIEM content.
Below are the key reasons, broken down in a way that reflects real‑world SOC, MSSP, and SecOps workflows.

1.1. Full CI/CD and Infrastructure-as-Code Support

In large environments or MSSP operations, Sentinel content (analytics rules, workbooks, hunting queries, playbooks, etc.) must be managed programmatically.

Using VS Code enables:

Bicep/ARM templates for deploying Sentinel resources
GitOps workflows for version control
Automated deployments using pipelines (GitHub Actions, Azure DevOps, GitLab CI)
Repeatable onboarding across multiple tenants

This is critical if you maintain dozens or hundreds of customer environments.

1.2. KQL Development Is Easier and More Efficient

VS Code has powerful extensions for Kusto Query Language (KQL), including:

Syntax highlighting
IntelliSense and autocomplete
Query validation
Snippet libraries
Local KQL file management

This dramatically speeds up development of:

Analytics rules
Hunting queries
Data transformation queries for logs
Watchlist preprocessing

You get a developer-grade experience that is far faster than writing queries inside the portal.

1.3. Sentinel-as-Code Becomes Possible

Using VS Code, you can treat Sentinel artifacts like software artifacts:

Store rules as YAML
Store queries as .kql files
Store playbooks as JSON
Apply pull requests and peer reviews
Run automated testing scripts
Apply linting and quality control

This is essential for enterprise-grade SOC maturity and MSSP service quality.

1.4. Better Automation for Playbooks (Logic Apps)

VS Code supports:

Logic App Standard development
Workflow development in JSON
Dev/test/prod branching
Automated deployment of playbooks with parameters

This replaces manual click-based deployment and reduces risk of misconfiguration.

1.5. Multi-Tenant Engineering Becomes Easy (Perfect for MSSPs)

Inside VS Code you can:

Connect to multiple tenants
Switch contexts instantly
Deploy rules across tenants programmatically
Use Azure CLI or PowerShell modules
Automate onboarding scripts

This dramatically reduces overhead compared to working through multiple portals.

1.6. Offline Development

With VS Code, you can:

Develop rules, playbooks, and workbooks offline
Package changes into Git commits
Deploy when ready

This improves security and supports environments with strict access policies.

1.7. Extensions Designed Specifically for Sentinel

Several VS Code extensions directly support Sentinel workflows, such as:

Azure Tools
Azure Resource Manager tools
Bicep extension
KQL extension
GitHub Codespaces for cloud-based development

These integrations streamline engineering work and reduce time-to-deploy.

1.8. More Secure and Controlled Change Management

Because Sentinel is part of a SOC, every change must be controlled.

VS Code enables:

Peer review workflows
Git commit history
Change approval gates
Full auditability of rule modifications
Automated rollback

Portal-only configuration cannot provide this level of governance.

1.9. Reusable Templates and Content Standardization

VS Code makes it easy to maintain template libraries for:

Analytics rules
Log ingestion patterns
Workbook baselines
Playbook bundles
Custom connectors
Watchlist ingestion workflows

This is a major advantage for large-scale operations.

Installing the Microsoft Sentinel Extension in Visual Studio Code

To get the full development experience for Microsoft Sentinel inside Visual Studio Code, you should install the Microsoft Sentinel extension. This extension provides templates, rule authoring support, and tools that streamline detection engineering and Sentinel‑as‑Code workflows.

How to Install the Microsoft Sentinel Extension in VS Code

Follow these steps:

1. Open Visual Studio Code

Launch VS Code on your machine.

2. Go to the Extensions Marketplace

Click on the Extensions icon on the left sidebar
or press:

Ctrl + Shift + X

3. Search for “Microsoft Sentinel”

In the search bar, type:

Microsoft Sentinel

The official extension will appear with the Microsoft publisher badge.

4. Install the Extension

Click Install.

Once installed, you will see Sentinel-specific templates and tools available inside your VS Code environment.

Install the GitHub Copilot extension for Visual Studio Code to enable code completion and suggestions in notebooks.

Search for GitHub Copilot in the Extensions Marketplace and install it.
After installation, sign in to GitHub Copilot using your GitHub account.

Explore data lake tier tables

After installing the Microsoft Sentinel extension, you can start exploring data lake tier tables and creating Jupyter notebooks to analyze the data.

Select the Microsoft Sentinel shield icon in the left toolbar.
A dialog appears with the following text The extension “Microsoft Sentinel” wants to sign in using Microsoft. Select Allow.
Select your account name to complete the sign in.

View data lake tables and jobs

Once you sign in, the Sentinel extension displays a list of Lake tables and Jobs in the left pane. The tables are grouped by the database and category. Select a table to see the column definitions.

For information on Jobs, see Jobs and Scheduling.

A screenshot showing the list of tables, jobs, and the selected table's metadata.

Create a new notebook

To create a new notebook, use one of the following methods.
Enter > in the search box or press Ctrl+Shift+P and then enter Create New Jupyter Notebook.
Select File > New File, then select Jupyter Notebook from the dropdown.
In the new notebook, paste the following code into the first cell.Pythonfrom sentinel_lake.providers import MicrosoftSentinelProvider data_provider = MicrosoftSentinelProvider(spark) table_name = "EntraGroups" df = data_provider.read_table(table_name) df.select("displayName", "groupTypes", "mail", "mailNickname", "description", "tenantId").show(100, truncate=False)

The editor provides intellisense code completion for both the MicrosoftSentinelProvider class and the table names in the data lake.

Select the Run triangle to execute the code in the notebook. The results are displayed in the output pane below the code cell.
Select Microsoft Sentinel from the list for a list of runtime pools.
Select Medium to run the notebook in the medium sized runtime pool. For more information on the different runtimes, see Selecting the appropriate Microsoft Sentinel runtime.

Selecting the kernel starts the Spark session and runs the code in the notebook. After selecting the pool, it can take 3-5 mins for the session to start. Subsequent runs a faster as the session is already active.

When the session is started, the code in the notebook runs and the results are displayed in the output pane below the code cell, for example:

For sample notebooks that demonstrate how to interact with the Microsoft Sentinel data lake, see Sample notebooks for Microsoft Sentinel data lake.

Status bar

The status bar at the bottom of the notebook provides information about the current state of the notebook and the Spark session. The status bar includes the following information:

The vCore utilization percentage for the selected Spark pool. Hover over the percentage to see the number of vCores used and the total number of vCores available in the pool. The percentages represent the current usage across interactive and job workloads for the logged in account.
The connection status of the Spark session for example Connecting, Connected, or Not Connected.

A screenshot showing the status bar at the bottom of the notebook.

Set session timeouts

You can set the session timeout and timeout warnings for interactive notebooks. To change the timeout, select the connection status in the status bar at the bottom of the notebook. Choose from the following options:

Set session timeout period: Sets the time in minutes before the session times out. The default is 30 minutes.
Reset session timeout period: Resets the session timeout to the default value of 30 minutes.
Set session timeout warning period: Sets the time in minutes before the timeout that a warning is displayed that the session is about to time out. The default is 5 minutes.
Reset session timeout warning period: Resets the session timeout warning to the default value of 5 minutes.

Use GitHub Copilot in notebooks

Use GitHub Copilot to help you write code in notebooks. GitHub Copilot provides code suggestions and autocompletion based on the context of your code. To use GitHub Copilot, ensure that you have the GitHub Copilot extension installed in Visual Studio Code.

Copy code from the Sample notebooks for Microsoft Sentinel data lake and save it in your notebooks folder to provide context for GitHub Copilot. GitHub Copilot will then be able to suggest code completions based on the context of your notebook.

The following example shows GitHub Copilot generating a code review.

A screenshot showing GitHub Copilot generating a code review.

Platform solutions prerequisites

Before you create and publish a platform solution, make sure you have:

Platform solutions prerequisites
Before you create and publish a platform solution, make sure you have:
A Microsoft Sentinel data lake to analyze data and write to the data lake:
If you haven’t onboarded yet, see Onboard to Microsoft Sentinel data lake.
After onboarding, ingest enough data to support notebook analysis.
Visual Studio Code with:
The Microsoft Sentinel extension:
In VS Code, open the Extensions Marketplace, search for Sentinel, select Microsoft Sentinel, and choose Install.
After installation, the Microsoft Sentinel shield icon appears in the left toolbar.

What the Sentinel Extension Adds

The extension provides:

Analytics rule templates (YAML-based)
Hunting query templates
Parser templates (ASIM)
Content Hub pack scaffolding
Metadata schema validation
Support for folder structures aligned with Sentinel-as-Code best practices

It essentially turns VS Code into a specialized environment for building, testing, and packaging Sentinel content.

1. Enhanced KQL Authoring

VS Code significantly improves the KQL development experience by providing:

IntelliSense
Syntax highlighting
Snippets and reusable templates
Local libraries for frequently used queries

This speeds up building analytics rules, hunting queries, and incident investigations.

2. Source Control Integration (GitHub / Azure DevOps)

Detection engineering is increasingly treated as code, and VS Code makes this natural.
You can:

Version your detections and automation
Use pull requests for peer review
Track history and changes
Support multi‑environment deployments

This ensures consistency across tenants and aligns with compliance requirements.

3. Sentinel-as-Code and CI/CD

VS Code is ideal for building Sentinel content that is automatically validated and deployed through pipelines.
Typical CI/CD actions include:

YAML validation
Deployment of analytics rules
Content Hub pack creation
Publishing playbooks
Updating parsers and workbooks

This turns SOC operations into a scalable, repeatable, high‑quality engineering process.

As detection engineering increasingly adopts a Detection‑as‑Code model, Visual Studio Code naturally becomes the central tool for managing Microsoft Sentinel content. Source control systems like GitHub and Azure DevOps enhance this by enabling structured, auditable, and automated workflows.

3.1. Version Your Detections and Automation

Storing analytics rules, hunting queries, watchlists, workbooks, and playbooks in a Git repository ensures that every change is tracked, recoverable, and fully auditable.
This eliminates risks of accidental portal changes and supports regulated environments.

3.2. Use Pull Requests for Peer Review

All changes can go through a pull request (PR) workflow, enabling:

Technical review by peers
Change approval by senior analysts or architects
Automated validation (linting, schema checks, KQL validation)

This dramatically improves quality and reduces false positives/negatives in production SOC environments.

3.3. Track History and Changes

Git provides a complete record of:

Who changed a rule
Why the change was made
What the change introduced
When it was deployed

This is essential for forensics, compliance, and SOC accountability.

3.4. Support Multi‑Environment Deployments

Repositories can store environment‑specific configurations (e.g., dev/test/prod).
CI/CD pipelines in GitHub Actions or Azure DevOps can automatically deploy:

Analytics rules
Playbooks
Data connectors
Workbooks
Sentinel configurations

This ensures complete consistency across environments and tenants.

4. Developing Sentinel Content Hub Packs

With the right folder structure and templates, you can fully develop:

Analytics rules
Hunting queries
Parsers (ASIM-compatible)
Workbooks
Playbooks
Threat Intelligence providers

All from your local environment.

4.1.Automation Workflows (Logic Apps Standard)

VS Code provides an excellent environment for building, testing, and maintaining automation workflows using Logic Apps Standard. Instead of creating and modifying playbooks through the portal, teams can design entire workflows as JSON files, integrate custom API logic, and maintain automation standards through branching strategies and code reviews. This approach ensures that every automation change—whether it’s a new enrichment step, a response workflow, or a custom connector—is validated, documented, and reviewed before reaching production.

By shifting automation authoring into VS Code, organizations reduce the risk of misconfiguration, strengthen their governance process, and improve their ability to scale automation consistently across tenants. It also empowers engineers to build large, reusable automation libraries that support rapid incident response and drive maturity across SOC operations.

VS Code is perfect for:

Editing playbooks
Writing custom API integrations
Deploying Logic Apps Standard
Maintaining enterprise automation libraries

This accelerates incident response and reduces manual analyst work.

4.2 Recommended Extensions for VS Code

To get the best development experience for Microsoft Sentinel, install these extensions:

Azure Tools – Manage subscriptions and deploy resources
Kusto (KQL) – Query editing with IntelliSense
Bicep – Declarative deployment of Sentinel resources
YAML – Rules, ASIM parsers, Content Hub packs
PowerShell – Scripting and automation
GitHub / Azure Repos – Source control
REST Client – Test API calls directly inside VS Code

To unlock the full power of Sentinel‑as‑Code, VS Code relies on a set of essential extensions. These tools transform the editor into a fully integrated engineering environment capable of handling infrastructure, detections, parsers, playbooks, and large‑scale deployments. Azure Tools and Bicep enable seamless provisioning of Sentinel resources. The Kusto extension improves KQL development with IntelliSense, syntax validation, and execution support. YAML support is crucial for rules, Content Hub metadata, and ASIM parsers, while PowerShell and the REST Client provide automation and API testing capabilities.

With these extensions, VS Code becomes a complete development environment for SecOps engineering.

4.3 Example SecOps Workflow in VS Code

A realistic workflow for a security engineer might look like this:

Clone the central detection repository from GitHub.
Create a new detection using a Sentinel rule template.
Write and test the KQL query directly in VS Code.
Validate YAML structure and metadata.
Commit changes to a feature branch.
Open a pull request for peer review.
Pipeline automatically deploys validated rules to Sentinel.

This approach reduces errors, improves quality, and standardizes detection content across the organization.

A practical Detection‑as‑Code workflow for Microsoft Sentinel / Defender XDR

4.4. Open VS Code and Sync With Your Repository

A typical SOC repository structure might look like:

/sentinel
   /analytics
   /hunting
   /workbooks
   /watchlists
   /playbooks
   /data-connectors
   /infrastructure

Steps:

Pull the latest changes from GitHub or Azure DevOps
Switch to your working branch (e.g., feature/new-MFA-detection)
Ensure all extensions are loaded:
- Kusto (KQL)
- Bicep
- Azure CLI
- YAML support
- GitHub / Azure DevOps integration

4.5. Develop or Update a Detection Rule (KQL + YAML)

Open the detection folder:

/sentinel/analytics/SignInRiskHigh.yaml

Update the rule’s KQL:

SecurityIncident
| where AlertName == "Suspicious sign-in activity"
| where RiskLevel == "High"

Validate your KQL using the VS Code KQL extension:

Syntax highlighting
Query linting
Local formatting

Update metadata:

severity: High
tactics: [CredentialAccess, InitialAccess]
requiredDataConnectors: [AzureActiveDirectory]

4.6. Test the Detection Locally

Use one of these methods:

✔ KQL Execution via the Kusto Explorer plug‑in

Run the query against:

A dev Log Analytics workspace
A sample dataset
A local test harness (if using mock data)

✔ Automated testing (if configured)

CI workflow checks:

KQL syntax
Schema compatibility
Tactics/techniques mapping
YAML schema validation

4.7. Commit Your Changes and Open a Pull Request

Using VS Code Git UI:

Once the detection and metadata have been validated locally, engineers commit changes and prepare a pull request. The VS Code Git UI simplifies this process by allowing users to stage files, write descriptive commit messages, and push changes to the remote repository. Pull requests serve as a formal review step, ensuring that all modifications undergo peer review, automated testing, and cross‑team validation.

During this stage, pipelines automatically analyze the detection for errors, enforce schema standards, and run any organization‑specific validation scripts. Reviewers can provide feedback, suggest improvements, or request alignment with existing detection patterns. This collaborative process enhances detection quality and drives continuous improvement within the SOC

Stage changes
Write a meaningful commit message (e.g. "Updated high-risk sign-in detection with stricter conditions")
Push branch to remote
Create a PR directly in VS Code

PR triggers:

Automated tests (KQL, YAML, Bicep)
Sentinel-as-Code validation scripts
Peer review from another detection engineer

4.8. CI/CD Pipeline Deploys to the DEV Workspace

After approval, CI/CD pipelines begin deploying updated content to the development workspace. The pipeline authenticates using secure credentials and applies changes across analytics rules, hunting queries, playbooks, watchlists, or workbooks, depending on what has been modified. By automating deployment, organizations eliminate manual steps and reduce the likelihood of human error.

Each deployment is fully logged, giving engineers insight into whether deployments succeeded or failed. This feedback loop is essential for verifying that content behaves as expected before pushing changes further into the lifecycle.

4.9. Functional Testing in DEV

A SOC engineer validates the deployment:

Trigger test events
Verify the rule fires correctly
Check alert grouping, entity mapping, and severity
Confirm any connected playbooks operate correctly

If needed, they push fixes into the same branch and repeat the PR cycle.

4.10. Promote to TEST and Then to PROD

Once functional testing is complete, detections are promoted to TEST and eventually to PROD through controlled CI/CD stages. Pipelines handle environment‑specific parameters, perform pre‑deployment validation, and publish detailed logs. For MSSPs, this stage may include deploying content to dozens or hundreds of customer tenants via Azure Lighthouse, GDAP, or custom multi‑tenant automation scripts.

This structured progression ensures consistent deployments across all environments and dramatically reduces operational complexity for large SOCs and MSSPs.

Once approved:

Merge into main branch
CD pipeline automatically:
- Deploys to TEST tenant
- Runs validation checks
- Deploys to PROD tenant(s)
- Writes deployment logs
- Notifies the team via Teams/Slack

For MSSPs, this may deploy rules to hundreds of customer tenants using:

Azure Lighthouse
GDAP
Multi-tenancy deployment scripts

4.11. Continuous Monitoring and Improvement

Detection engineering is never a one‑time task. Every detection is versioned, traceable, and frequently improved based on new intelligence, operational feedback, or emerging threats. Teams maintain documentation, track false positive tuning, resolve data latency issues, and incorporate new indicators or coverage improvements as part of ongoing SOC evolution.

With VS Code and Git, teams can easily iterate on existing rules, maintain historical context, and quickly roll back changes if needed. This continuous improvement cycle strengthens detection quality and ensures the SOC adapts to evolving attack landscapes.

Every detection:

Is versioned
Has historical traceability
Can be rolled back automatically
Has linked documentation inside the repo
Can be improved via further PRs

The SOC team can also open backlog tickets for:

False positive tuning
Data latency issues
New threat intelligence indicators
New attack surface coverage

4.12. Incident Investigation Feedback Loop

VS Code workVS Code integrates tightly with SOC operations by enabling a closed‑loop feedback system. During investigations, analysts often uncover gaps, noisy rules, or missing enrichment. Instead of manually adjusting detections in the portal, they open issues in GitHub or Azure DevOps. Detection engineers then update the rule in VS Code, submit a PR, and use CI/CD pipelines to deploy improvements.
This creates a powerful connection between detection engineering, SOC analysts, threat intelligence teams, and automation engineers. Each group contributes to improving content quality, ensuring detections evolve rapidly as new threats and attack techniques emerge.

flow integrates with SOC operations:

Analysts find gaps or noise in alerts
They open GitHub/Azure DevOps issues
Detection engineers update rules in VS Code
PR → CI/CD → deployment → validation
Improved coverage is rolled out across tenants

This closes the loop between:

Detection Engineering → SOC Analysts → Threat Intelligence → Automation Engineers

This structure ensures consistency, modularity, and supports multi-team collaboration.

Key Benefits for Enterprise SOC Teams

Using Microsoft Sentinel inside Visual Studio Code unlocks several advantages:

Faster development workflow
Higher-quality analytics and automation
Full traceability through source control
Reduced operational friction
Stronger alignment with engineering best practices
Better scalability and governance

In large organizations or MSSPs, this approach is rapidly becoming the standard.

Conclusion

Using Microsoft Sentinel with Visual Studio Code elevates SecOps work from manual, portal-based operations to a truly modern engineering practice. It enables security teams to automate more, collaborate better, and deliver high-quality detections and playbooks consistently across environments.

If you’re building a Sentinel-as-Code practice or want to standardize detection engineering across teams, VS Code is the best place to start.