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This unit explores ways that you can maximize your interaction with GitHub Copilot in your development environment. By understanding the service’s features and capabilities, you learn how to use it effectively.

The following sections describe the various ways to trigger and use GitHub Copilot, along with examples and shortcuts to help you get the most out of it.

Inline suggestions

Inline suggestions are the most immediate form of assistance in Copilot. As you type, Copilot analyzes your code and context to offer real-time code completions. This feature predicts what you might want to write next and displays suggestions in a subtle, unobtrusive way.

The suggestions that Copilot offers appear as grayed-out text ahead of your cursor.

• To accept a suggestion, select the Tab key or the > (right arrow) key.
• To reject a suggestion, keep typing or select the Esc key.

Inline suggestions are especially useful when you’re working on repetitive tasks or you need quick boilerplate code.

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It’s no secret that AI is disrupting the technology industry. AI is shaping how development teams work and build software. These advancements in AI can enhance the productivity of developers around the world.

The addition of AI features to the developer tools that you use and love helps you collaborate, develop, test, and ship your products faster and more efficiently than ever before. GitHub Copilot is a service that provides you with an AI pair programmer that works with all of the popular programming languages.

In recent research, GitHub and Microsoft found that developers experience a significant productivity boost when they use GitHub Copilot to work on real-world projects and tasks. In fact, in the three years since its launch, developers have experienced the following benefits while using GitHub Copilot:

• 46% of new code now written by AI
• 55% faster overall developer productivity
• 74% of developers feeling more focused on satisfying work

Microsoft developed GitHub Copilot in collaboration with OpenAI. GitHub Copilot is powered by the OpenAI Codex system. OpenAI Codex has broad knowledge of how people use code and is more capable than GPT-3 in code generation. OpenAI Codex is more capable, in part, because it was trained on a dataset that included a larger concentration of public source code.

GitHub Copilot is available as an extension for VS Code, Visual Studio, Vim/Neovim, and the JetBrains suite of IDEs.

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A common scenario in an AI application is to connect to a generative AI model and use prompts to engage in a chat-based dialog with it.

While you can use the Azure OpenAI SDK, to connect “directly” to a model using key-based or Microsoft Entra ID authentication; when your model is deployed in an Azure AI Foundry project, you can also use the Azure AI Foundry SDK to retrieve a project client, from which you can then get an authenticated OpenAI chat client for any models deployed in the project’s Azure AI Foundry resource. This approach makes it easy to write code that consumes models deployed in your project, switching between them easily by changing the model deployment name parameter.

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Each Azure AI Foundry project includes connected resources, which are defined both at the parent (Azure AI Foundry resource or hub) level, and at the project level. Each resource is a connection to an external service, such as Azure storage, Azure AI Search, Azure OpenAI, or another Azure AI Foundry resource.

With the Azure AI Foundry SDK, you can connect to a project and retrieve connections; which you can then use to consume the connected services.

For example, the AIProjectClient object in Python has a connections property, which you can use to access the resource connections in the project. Methods of the connections object include:

• connections.list(): Returns a collection of connection objects, each representing a connection in the project. You can filter the results by specifying an optional connection_type parameter with a valid enumeration, such as ConnectionType.AZURE_OPEN_AI.
• connections.get(connection_name, include_credentials): Returns a connection object for the connection with the name specified. If the include_credentials parameter is True (the default value), the credentials required to connect to the connection are returned – for example, in the form of an API key for an Azure AI services resource.

The connection objects returned by these methods include connection-specific properties, including credentials, which you can use to connect to the associated resource.

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Azure AI Foundry provides a REST API that you can use to work with AI Foundry projects and the resources they contain. Additionally, multiple language-specific SDKs are available, enabling developers to write code that uses resources in an Azure AI Foundry project in their preferred development language. With an Azure AI Foundry SDK, developers can create applications that connect to a project, access the resource connections and models in that project, and use them to perform AI operations, such as sending prompts to a generative AI model and processing the responses.

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Defender for Cloud is a monitoring tool for security posture management and threat protection. It monitors your cloud, on-premises, hybrid, and multicloud environments to provide guidance and notifications aimed at strengthening your security posture.

Defender for Cloud provides the tools needed to harden your resources, track your security posture, protect against cyber attacks, and streamline security management. Deployment of Defender for Cloud is easy, it’s already natively integrated to Azure.

Protection everywhere you’re deployed

Because Defender for Cloud is an Azure-native service, many Azure services are monitored and protected without needing any deployment. However, if you also have an on-premises datacenter or are also operating in another cloud environment, monitoring of Azure services may not give you a complete picture of your security situation.

When necessary, Defender for Cloud can automatically deploy a Log Analytics agent to gather security-related data. For Azure machines, deployment is handled directly. For hybrid and multicloud environments, Microsoft Defender plans are extended to non-Azure machines with the help of Azure Arc. Cloud security posture management (CSPM) features are extended to multicloud machines without the need for any agents.

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he objective of defense-in-depth is to protect information and prevent it from being stolen by those who aren’t authorized to access it.

A defense-in-depth strategy uses a series of mechanisms to slow the advance of an attack that aims at acquiring unauthorized access to data.

Layers of defense-in-depth

You can visualize defense-in-depth as a set of layers, with the data to be secured at the center and all the other layers functioning to protect that central data layer.

Each layer provides protection so that if one layer is breached, a subsequent layer is already in place to prevent further exposure. This approach removes reliance on any single layer of protection. It slows down an attack and provides alert information that security teams can act upon, either automatically or manually.

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Zero Trust is a security model that assumes the worst case scenario and protects resources with that expectation. Zero Trust assumes breach at the outset, and then verifies each request as though it originated from an uncontrolled network.

Today, organizations need a new security model that effectively adapts to the complexity of the modern environment; embraces the mobile workforce; and protects people, devices, applications, and data wherever they’re located.

To address this new world of computing, Microsoft highly recommends the Zero Trust security model, which is based on these guiding principles:

• Verify explicitly – Always authenticate and authorize based on all available data points.
• Use least privilege access – Limit user access with Just-In-Time and Just-Enough-Access (JIT/JEA), risk-based adaptive policies, and data protection.
• Assume breach – Minimize blast radius and segment access. Verify end-to-end encryption. Use analytics to get visibility, drive threat detection, and improve defenses.

Adjusting to Zero Trust

Traditionally, corporate networks were restricted, protected, and generally assumed safe. Only managed computers could join the network, VPN access was tightly controlled, and personal devices were frequently restricted or blocked.

The Zero Trust model flips that scenario. Instead of assuming that a device is safe because it’s within the corporate network, it requires everyone to authenticate. Then grants access based on authentication rather than location.

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When you have multiple IT and engineering teams, how can you control what access they have to the resources in your cloud environment? The principle of least privilege says you should only grant access up to the level needed to complete a task. If you only need read access to a storage blob, then you should only be granted read access to that storage blob. Write access to that blob shouldn’t be granted, nor should read access to other storage blobs. It’s a good security practice to follow.

However, managing that level of permissions for an entire team would become tedious. Instead of defining the detailed access requirements for each individual, and then updating access requirements when new resources are created or new people join the team, Azure enables you to control access through Azure role-based access control (Azure RBAC).

Azure provides built-in roles that describe common access rules for cloud resources. You can also define your own roles. Each role has an associated set of access permissions that relate to that role. When you assign individuals or groups to one or more roles, they receive all the associated access permissions.

So, if you hire a new engineer and add them to the Azure RBAC group for engineers, they automatically get the same access as the other engineers in the same Azure RBAC group. Similarly, if you add additional resources and point Azure RBAC at them, everyone in that Azure RBAC group will now have those permissions on the new resources as well as the existing resources.

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Conditional Access is a tool that Microsoft Entra ID uses to allow (or deny) access to resources based on identity signals. These signals include who the user is, where the user is, and what device the user is requesting access from.

Conditional Access helps IT administrators:

• Empower users to be productive wherever and whenever.
• Protect the organization’s assets.

Conditional Access also provides a more granular multifactor authentication experience for users. For example, a user might not be challenged for second authentication factor if they’re at a known location. However, they might be challenged for a second authentication factor if their sign-in signals are unusual or they’re at an unexpected location.

During sign-in, Conditional Access collects signals from the user, makes decisions based on those signals, and then enforces that decision by allowing or denying the access request or challenging for a multifactor authentication response.

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