Microsoft Purview: The Ultimate AI Data Security Solution
Introduction AI is transforming the way enterprises operate, however with great innovation comes great responsibility. I’ve spent the last few years helping organizations secure their data with tools like Azure Information Protection, Data Loss Prevention, and now Microsoft Purview. As generative AI tools like Microsoft Copilot become embedded in everyday workflows, the need for clear governance and robust data protection is more urgent than ever. Through this blog post, let's explore how Microsoft Purview can help organizations stay ahead of securing AI interactions without slowing down innovation. What’s the Issue? AI agents are increasingly used to process sensitive data, often through natural language prompts. Without proper oversight, this can lead to data oversharing, compliance violations, and security risks. Why It’s Urgent? According to the recent trends of 2025, over half of corporate users bring their own AI tools to work, often consumer-grade apps like ChatGPT or DeepSeek. These tools bypass enterprise protections, making it difficult to monitor and control data exposure. Use Cases Enterprise AI Governance: Apply consistent policies across Microsoft and third-party AI tools. Compliance Auditing: Generate audit logs for AI interactions to meet regulatory requirements. Risk Mitigation: Block risky uploads and enforce adaptive protection based on user behavior. How Microsoft Purview Solves It Data Security Posture Management (DSPM) for AI Purview’s DSPM for AI provides a centralized dashboard to monitor AI activity, assess data risks, and enforce compliance policies across Copilots, agents, and third-party AI apps. It correlates data classification, user behavior, and policy coverage to surface real-time risks, such as oversharing via AI agents, and generates actionable recommendations to remediate gaps. DSPM integrates with tools like Microsoft Security Copilot for AI-assisted investigations and supports automated scanning, trend analytics, and posture reporting. It also extends protection to third-party AI tools like ChatGPT through endpoint DLP and browser extensions, ensuring consistent governance across both managed and unmanaged environments 2. Unified Protection Across AI Agents Whether you're using Microsoft 365 Copilot, Security Copilot, or Azure AI services, Purview applies consistent security and compliance controls. Agents inherit protection from their parent apps, including sensitivity labels, data loss prevention (DLP), and Insider Risk Management. Real-Time Risk Detection Purview enables real-time monitoring of prompts and responses, helping security teams detect oversharing and policy violations instantly. From Microsoft Learn – Insider Risk 4. One-Click Policy Activation Administrators can leverage Microsoft Purview’s Data Security Posture Management (DSPM) for AI to rapidly deploy comprehensive security and compliance controls via one-click policy activation. This streamlined mechanism enables organizations to enforce prebuilt policy templates across AI ecosystems, ensuring prompt implementation of data loss prevention (DLP), sensitivity labeling, and Insider Risk Management on both Microsoft and third-party AI services. Through DSPM’s unified policy orchestration layer, security teams gain granular telemetry into prompt and response flows, real-time policy enforcement, and detailed incident reporting. Automated analytics continuously assess risk posture, enabling adaptive policy adjustments and scalable governance as new AI tools and user workflows are introduced into the enterprise environment. Please note: After implementing policy changes, it can take up to 24 hours for changes to become visible and take full effect across your environment. From Microsoft Learn – Purview Data Security Posture Management (DSPM) portal 5. Support for Third-Party AI Apps Purview extends robust data security and compliance to browser-based AI tools such as ChatGPT and Google Gemini by employing endpoint Data Loss Prevention (DLP) and browser extensions that monitor and control data flows in real time. Through Microsoft Purview’s Data Security Posture Management (DSPM) for AI, organizations can implement granular controls for sensitive data accessed during both Microsoft-native and third-party AI interactions. DSPM offers continuous discovery and classification of data assets, linking AI prompts and responses to their original data sources to automatically enforce data protection policies, including sensitivity labeling, adaptive access controls, and comprehensive content inspection, contextually for each AI transaction. For unsanctioned AI services reached via browsers, the Purview browser extension inspects both input and output, enabling endpoint DLP to block, alert, or redact sensitive material instantly, thus preventing unauthorized uploads, downloads, or copy/paste activities. Security teams benefit from rich telemetry on AI usage patterns, which integrate with user risk profiles and anomaly detection to identify and flag suspicious attempts to extract confidential information. Close integration with Microsoft Security Copilot and automated analytics further enhances visibility across all AI data flows, supporting incident response, audit, and compliance reporting needs. Purview’s adaptive policy orchestration ensures that evolving AI services and workflows are continuously assessed for risk, and that controls are dynamically aligned with business, regulatory, and security requirements, enabling scalable, policy-driven governance for the expanding enterprise AI ecosystem. Pros and Cons The following table outlines the key advantages and potential limitations of implementing AI and agent data security controls within Microsoft Purview. Pros Cons License Needed Centralized AI governance Requires proper licensing and setup Microsoft 365 E5 or equivalent Purview add-on license Real-time risk detection May need browser extensions for full coverage Microsoft 365 E5 or Purview add-on Supports both Microsoft and third-party AI apps Some features limited to enterprise versions Microsoft 365 E5, E5 Compliance, or equivalent Purview add-on Conclusion Microsoft Purview offers a comprehensive solution for securing AI agents and their data interactions. By leveraging DSPM for AI, organizations can confidently adopt AI technologies while maintaining control over sensitive information. Explore Microsoft Purview’s DSPM for AI here. Start by assessing your current AI usage and activate one-click policies to secure your environment today! FAQ 1. What is the purpose of Microsoft Purview’s AI and agent data security controls? The purpose is to ensure that sensitive data accessed or processed by AI systems and agents is governed, protected, and monitored using Microsoft Purview’s compliance and security capabilities. Microsoft Purview data security and compliance protection 2. How does Microsoft Purview help secure AI-generated content? Microsoft Purview applies data loss prevention (DLP), sensitivity labels, and information protection policies to AI-generated content, ensuring it adheres to organizational compliance standards. Microsoft Purview Information Protection 3. Can Microsoft Purview track and audit AI interactions with sensitive data? Yes. Microsoft Purview provides audit logs and activity explorer capabilities that allow organizations to monitor how AI systems and agents interact with sensitive data. Search the audit log 4. What role do sensitivity labels play in AI data governance? Sensitivity labels classify and protect data based on its sensitivity level. When applied, they enforce encryption, access restrictions, and usage rights, even when data is processed by AI. Learn about sensitivity labels 5. How does Microsoft Purview integrate with Copilot and other AI tools? Microsoft Purview extends its data protection and compliance capabilities to Microsoft 365 Copilot and other AI tools by ensuring that data accessed by these tools is governed under existing policies. Microsoft 365 admin center Microsoft 365 Copilot usage 6. Are there specific controls for third-party AI agents? Yes. Microsoft Purview supports conditional access, DLP, and access reviews to manage and monitor third-party AI agents that interact with organizational data. What is Conditional Access in Microsoft Entra ID? 7. How can organizations ensure AI usage complies with regulatory requirements? By using Microsoft Purview’s compliance manager, organizations can assess and manage regulatory compliance risks associated with AI usage. Microsoft Purview Compliance Manager About the Author: Hi! Jacques “Jack” here, I’m a Microsoft Technical Trainer at Microsoft. I wanted to share a topic that is often top of mind, AI governance. I’ve been working with Microsoft Purview since its launch in 2022, building on prior experience with Azure Information Protection and Data Loss Prevention. I also have great expertise with Generative AI technologies since their public release in November 2022, including Microsoft Copilot and other enterprise-grade AI solutions.Intune AI Agent: Instant Threat Defense, Invisible Protection
From Microsoft Learn - Vulnerability Remediation Agent In today’s threat landscape, security teams require more than traditional tools—they need automation that can adapt in real time. Microsoft Intune’s integration with Security Copilot agents addresses this need. This blog introduces the Vulnerability Remediation Agent, an AI-based solution for managing endpoint security. By leveraging Microsoft’s threat intelligence alongside large language models (LLMs), these agents provide insights, automate policy enforcement, and simplify remediation workflows. Whether responding to compromised devices, updating compliance policies, or applying Zero Trust principles, Intune’s Security Copilot agents offer a centralized approach to endpoint protection. This article outlines the functionality of these agents, their features, and implementation strategies, enabling organizations to address threats and enhance their overall security posture. Why Intune Needs AI Agents Vulnerability Remediation Agent in Microsoft Intune are AI-driven tools developed to support security teams in endpoint management and protection. These agents utilize large language models (LLMs) and Microsoft's threat intelligence to deliver insights, automate tasks, and assist with decision-making. With factors such as hybrid work, Bring Your Own Device (BYOD), and changing threat vectors, managing endpoint security has become more complex. Security Copilot agents in Intune address these challenges by: Automating threat detection and response Offering contextual device risk insights Recommending and applying policy modifications Reducing remediation time for compromised devices These agents function within the Security Copilot framework, using Microsoft’s threat intelligence and AI models to provide real-time guidance. From Microsoft Learn - Vulnerability Remediation Agent Key Capabilities of Vulnerability Remediation Agent in Microsoft Intune Microsoft Intune’s Vulnerability Remediation Agent uses AI to scan devices for vulnerabilities, assess their risk, and provide clear remediation steps. It automates or recommends policy changes to guide IT teams from detection through resolution, focusing on the most critical issues. The Compromise Recovery Agent automatically identifies compromised devices using Defender and other signals, then runs recovery actions like isolation, password resets, and policy enforcement to streamline response. Device Compliance Optimization Agent reviews compliance policies and telemetry, highlights gaps, and suggests improvements. It enables gradual policy rollout via report-only mode for safer deployments. Security Posture Insights present dashboards with device risks, policy effectiveness, and remediation history, helping security teams prioritize responses. Security Copilot agents integrate into the Intune admin center, letting administrators use natural language queries to receive recommendations and make changes directly in one platform. Copilot-Driven Recommendations deliver bespoke guidance for strengthening endpoint security, complete with projected impact analyses prior to execution. Collectively, these agents offer several core capabilities: Real-time threat detection and response Automated policy recommendations Endpoint configuration optimization Integration with Microsoft Defender and other security solutions Context-aware insights informed by organizational data Step-by-step vulnerability remediation guidance leveraging Intune’s native tools From Microsoft Learn - Agent suggestions How It Works Vulnerability Remediation Agent in Microsoft Intune operates through an ongoing improvement process: Scan & Evaluate: Review device telemetry and policy coverage. Recommend: Suggest policy adjustments or remediation actions. Remediate: Implement fixes in report-only mode or enforce immediately. Observe & Iterate: Track outcomes and adjust policies accordingly. Utilizing AI Agent in endpoint management allows security teams to: Shorten response time to threats Enhance policy compliance Reduce manual configuration errors Increase visibility into endpoint status and security Learn More Microsoft Vulnerability Remediation Agent in Microsoft Intune From Microsoft Learn - Vulnerability Remediation Agent Setup Use Cases Rapid Response to Compromised Devices: Endpoints identified as infected are automatically isolated and remediated. Policy Optimization: Overlapping compliance policies are consolidated to minimize complexity. Zero Trust Enforcement: Only devices that meet compliance and security standards are permitted access to corporate resources. Operational Efficiency: Manual troubleshooting is reduced, and visibility into operations is improved. Requirements This list outlines the requirements, licensing conditions, user roles, permissions, and a comparison of advantages and disadvantages for deploying Vulnerability Remediation Agent in Microsoft Intune. Licensing: Microsoft Intune and Microsoft Security Copilot Secure Compute Units (SCU) may apply). Roles: Intune Admin, Security Admin, or Global Admin. Permissions: Role-based access controls ensure secure execution. Pros and Cons Pros Cons Notes Automated threat detection and remediation Requires SCUs and proper licensing Plan SCU usage Simplifies compliance policy management Limited customization of agent suggestions Use report-only mode for testing Improves visibility into device risk Initial setup may require training Leverage dashboards and logs Supports Zero Trust principles Preview features may evolve Monitor Microsoft Learn for updates Getting Started with Intune’s AI-Powered Security Agents Step 1: Access the Intune Admin Center Go to the https://intune.microsoft.com. Navigate to Endpoint Security > Security Copilot Agents. Step 2: Enable the Vulnerability Remediation Agent Locate the Vulnerability Remediation Agent tile on the home page. Click Start Agent to begin setup. Follow the guided steps to configure scanning, policy recommendations, and remediation workflows. Step 3: Review Licensing and Permissions Ensure your organization has the required Microsoft Intune licensing and Security Copilot Secure Compute Units (SCUs). Assign appropriate role-based access controls (e.g., Intune Admin, Security Admin, Global Admin) to manage agent capabilities securely. Step 4: Configure Agent Settings Define scanning intervals and telemetry sources. Enable report-only mode for safe testing of policy changes before enforcement. Set up dashboards and logs to monitor agent activity and remediation outcomes. Step 5: Integrate with Microsoft Defender Link Intune with Microsoft Defender for Endpoint to enhance threat detection and response. Use Defender signals to support Compromise Recovery Agent actions like isolation and password resets. Step 6: Use Natural Language Queries In the Intune Admin Center, use Security Copilot to ask questions like: o “Which devices are at risk?” o “What policy changes are recommended?” o “Show remediation history for compromised endpoints.” Step 7: Monitor and Optimize Track device compliance and risk posture using Security Posture Insights. Use the Device Compliance Optimization Agent to identify gaps and suggest improvements. Adjust policies based on observed outcomes and agent recommendations. About the Author Hi! Jacques “Jack” here, Lead Technical Trainer. I help learners and customers adopt Microsoft Intune, Defender, and Security Copilot. This blog post reflects the practical guidance I share in workshops to accelerate secure endpoint management. From my perspective as a trainer, what truly sets Intune apart is how seamlessly it leverages AI-driven agents to automate responses, detect advanced threats, and provide actionable insights in real time. This empowers organizations to proactively defend their environments, reduce manual workloads, and build a culture of security resilience through intelligent automation. With these capabilities, Intune and Security Copilot together not only elevate protection but also simplify the learning curve for IT professionals managing complex digital landscapes. #MicrosoftLearn #SkilledByMTT
Announcing Microsoft Sentinel Model Context Protocol (MCP) server – Public Preview
Security is being reengineered for the AI era—moving beyond static, rulebound controls and after-the-fact response toward platform-led, machine-speed defense. The challenge is clear: fragmented tools, sprawling signals, and legacy architectures that can’t match the velocity and scale of modern attacks. What’s needed is an AI-ready, data-first foundation—one that turns telemetry into a security graph, standardizes access for agents, and coordinates autonomous actions while keeping humans in command of strategy and high-impact investigations. Security teams already center operations on their SIEM for end-to-end visibility, and we’re advancing that foundation by evolving Microsoft Sentinel into both the SIEM and the platform for agentic defense—connecting analytics and context across ecosystems. And now, we’re introducing new platform capabilities that build on Sentinel data lake: Sentinel graph for deeper insight and context; an MCP server and tools to make data agent ready; new developer capabilities; and Security Store for effortless discovery and deployment—so protection accelerates to machine speed while analysts do their best work. Introducing Sentinel MCP server We’re excited to announce the public preview of Microsoft Sentinel MCP (Model Context Protocol) server, a fully managed cloud service built on an open standard that lets AI agents seamlessly access the rich security context in your Sentinel data lake. Recent advances in large language models have enabled AI agents to perform reasoning—breaking down complex tasks, inferring patterns, and planning multistep actions, making them capable of autonomously performing business processes. To unlock this potential in cybersecurity, agents must operate with your organization’s real security context, not just public training data. Sentinel MCP server solves that by providing standardized, secure access to that context—across graph relationships, tabular telemetry, and vector embeddings—via reusable, natural language tools, enabling security teams to unlock the full potential of AI-driven automation and focus on what matters most. Why Model Context Protocol (MCP)? Model Context Protocol (MCP) is a rapidly growing open standard that allows AI models to securely communicate with external applications, services, and data sources through a well-structured interface. Think of MCP as a bridge that lets an AI agents understand and invoke an application’s capabilities. These capabilities are exposed as discrete “tools” with natural language inputs and outputs. The AI agent can autonomously choose the right tool (or combination of tools) for the task it needs to accomplish. In simpler terms, MCP standardizes how an AI talks to systems. Instead of developers writing custom connectors for each application, the MCP server presents a menu of available actions to the AI in a language it understands. This means an AI agent can discover what it can do (search data, run queries, trigger actions, etc.) and then execute those actions safely and intelligently. By adopting an open standard like MCP, Microsoft is ensuring that our AI integrations are interoperable and future-proof. Any AI application that speaks MCP can connect. Security Copilot offers built-in integration, while other MCP-compatible platforms can leverage your Sentinel data and services can quickly connect by simply adding a new MCP server and typing Sentinel’s MCP server URL. How to Get Started Sentinel MCP server is a fully managed service now available to all Sentinel data lake customers. If you are already onboarded to Sentinel data lake, you are ready to begin using MCP. Not using Sentinel data lake yet? Learn more here. Currently, you can connect to the Sentinel MCP server using Visual Studio Code (VS Code) with the GitHub Copilot add-on. Here’s a step-by-step guide: Open VS Code and authenticate with an account that has at least Security Reader role access (required to query the data lake via the Sentinel MCP server) Open the Command Palette in VS Code (Ctrl + Shift + P) Type or select “MCP: Add Server…” Choose “HTTP” (HTTP or Server-Sent Event) Enter the Sentinel MCP server URL: “https://sentinel.microsoft.com/mcp/data-exploration" When prompted, allow authentication with the Sentinel MCP server by clicking “Allow” Once connected, GitHub Copilot will be linked to Sentinel MCP server. Open the agent pane, set it to Agent mode (Ctrl + Shift + I), and you are ready to go. GitHub Copilot will autonomously identify and utilize Sentinel MCP tools as necessary. You can now experience how AI agents powered by the Sentinel MCP server access and utilize your security context using natural language, without knowing KQL, which tables to query and wrangle complex schemas. Try prompts like: “Find the top 3 users that are at risk and explain why they are at risk.” “Find sign-in failures in the last 24 hours and give me a brief summary of key findings.” “Identify devices that showed an outstanding amount of outgoing network connections.” To learn more about the existing capabilities of Sentinel MCP tools, refer to our documentation. Security Copilot will also feature native integration with Sentinel MCP server; this seamless connectivity will enhance autonomous security agents and the open prompting experience. Check out the Security Copilot blog for additional details. What’s coming next? The public preview of Sentinel MCP server marks the beginning of a new era in cybersecurity—one where AI agents operate with full context, precision, and autonomy. In the coming months, the MCP toolset will expand to support natural language access across tabular, graph, and embedding-based data, enabling agents to reason over both structured and unstructured signals. This evolution will dramatically boost agentic performance, allowing them to handle more complex tasks, surface deeper insights, and accelerate protection to machine speed. As we continue to build out this ecosystem, your feedback will be essential in shaping its future. Be sure to check out the Microsoft Secure for a deeper dive and live demo of Sentinel MCP server in action.Secure Model Context Protocol (MCP) Implementation with Azure and Local Servers
Introduction The Model Context Protocol (MCP) enables AI systems to interact with external data sources and tools through a standardized interface. While powerful, MCP can introduce security risks in enterprise environments. This tutorial shows you how to implement MCP securely using local servers, Azure OpenAI with APIM, and proper authentication. Understanding MCP's Security Risks There are a couple of key security concerns to consider before implementing MCP: Data Exfiltration: External MCP servers could expose sensitive data. Unauthorized Access: Third-party services become potential security risks. Loss of Control: Unknown how external services handle your data. Compliance Issues: Difficulty meeting regulatory requirements with external dependencies. The solution? Keep everything local and controlled. Secure Architecture Before we dive into implementation, let's take a look at the overall architecture of our secure MCP solution: This architecture consists of three key components working together: Local MCP Server - Your custom tools run entirely within your local environment, reducing external exposure risks. Azure OpenAI + APIM Gateway - All AI requests are routed through Azure API Management with Microsoft Entra ID authentication, providing enterprise-grade security controls and compliance. Authenticated Proxy - A lightweight proxy service handles token management and request forwarding, ensuring seamless integration. One of the key benefits of this architecture is that no API key is required. Traditional implementations often require storing OpenAI API keys in configuration files, environment variables, or secrets management systems, creating potential security vulnerabilities. This approach uses Azure Managed Identity for backend authentication and Azure CLI credentials for client authentication, meaning no sensitive API keys are ever stored, logged, or exposed in your codebase. For more security, APIM and Azure OpenAI resources can be configured with IP restrictions or network rules to only accept traffic from certain sources. These configurations are available for most Azure resources and provide an additional layer of network-level security. This security-forward approach gives you the full power of MCP's tool integration capabilities while keeping your implementation completely under your control. How to Implement MCP Securely 1. Local MCP Server Implementation Building the MCP Server Let's start by creating a simple MCP server in .NET Core. 1. Create a web application dotnet new web -n McpServer 2.Add MCP packages dotnet add package ModelContextProtocol --prerelease dotnet add package ModelContextProtocol.AspNetCore --prerelease 3. Configure Program.cs var builder = WebApplication.CreateBuilder(args); builder.Services.AddMcpServer() .WithHttpTransport() .WithToolsFromAssembly(); var app = builder.Build(); app.MapMcp(); app.Run(); WithToolsFromAssembly() automatically discovers and registers tools from the current assembly. Look into the C# SDK for other ways to register tools for your use case. 4. Define Tools Now, we can define some tools that our MCP server can expose. here is a simple example for tools that echo input back to the client: using ModelContextProtocol.Server; using System.ComponentModel; namespace Tools; [McpServerToolType] public static class EchoTool { [McpServerTool] [Description("Echoes the input text back to the client in all capital letters.")] public static string EchoCaps(string input) { return new string(input.ToUpperInvariant()); } [McpServerTool] [Description("Echoes the input text back to the client in reverse.")] public static string ReverseEcho(string input) { return new string(input.Reverse().ToArray()); } } Key components of MCP tools are the McpServerToolType class decorator indicating that this class contains MCP tools, and the McpServerTool method decorator with a description that explains what the tool does. Alternative: STDIO Transport If you want to use STDIO transport instead of SSE (implemented here), check out this guide: Build a Model Context Protocol (MCP) Server in C# 2. Create a MCP Client with Cline Now that we have our MCP server set up with tools, we need a client that can discover and invoke these tools. For this implementation, we'll use Cline as our MCP client, configured to work through our secure Azure infrastructure. 1. Install Cline VS Code Extension Install the Cline extension in VS Code. 2. Deploy secure Azure OpenAI Endpoint with APIM Instead of connecting Cline directly to external AI services (which could expose the secure implementation to external bad actors), we will route through Azure API Management (APIM) for enterprise security. With this implementation, all requests go through Microsoft Entra ID and we use managed identity for all authentications. Quick Setup: Deploy the Azure OpenAI with APIM solution. Ensure your Azure OpenAI resources are configured to allow your APIM's managed identity to make calls. The APIM policy below uses managed identity authentication to connect to Azure OpenAI backends. Refer to the Azure OpenAI documentation on managed identity authentication for detailed setup instructions. 3. Configure APIM Policy After deploying APIM, configure the following policy to enable Azure AD token validation, managed identity authentication, and load balancing across multiple OpenAI backends: <!-- Azure API Management Policy for OpenAI Endpoint --> <!-- Implements Azure AD Token validation, managed identity authentication --> <!-- Supports round-robin load balancing across multiple OpenAI backends --> <!-- Requests with 'gpt-5' in the URL are routed to a single backend --> <!-- The client application ID '04b07795-8ddb-461a-bbee-02f9e1bf7b46' is the official Azure CLI app registration --> <!-- This policy allows requests authenticated by Azure CLI (az login) when the required claims are present --> <policies> <inbound> <!-- IP Allow List Fragment (external fragment for client IP restrictions) --> <include-fragment fragment-id="YourCompany-IPAllowList" /> <!-- Azure AD Token Validation for Azure CLI app ID --> <validate-azure-ad-token tenant-id="YOUR-TENANT-ID-HERE" header-name="Authorization" failed-validation-httpcode="401" failed-validation-error-message="Unauthorized. Access token is missing or invalid."> <client-application-ids> <application-id>04b07795-8ddb-461a-bbee-02f9e1bf7b46</application-id> </client-application-ids> <audiences> <audience>api://YOUR-API-AUDIENCE-ID-HERE</audience> </audiences> <required-claims> <claim name="roles" match="any"> <value>YourApp.User</value> </claim> </required-claims> </validate-azure-ad-token> <!-- Acquire Managed Identity access token for backend authentication --> <authentication-managed-identity resource="https://cognitiveservices.azure.com" output-token-variable-name="managed-id-access-token" ignore-error="false" /> <!-- Set Authorization header for backend using the managed identity token --> <set-header name="Authorization" exists-action="override"> <value>@("Bearer " + (string)context.Variables["managed-id-access-token"])</value> </set-header> <!-- Check if URL contains 'gpt-5' and set backend accordingly --> <choose> <when condition="@(context.Request.Url.Path.ToLower().Contains("gpt-5"))"> <set-variable name="selected-backend-url" value="https://your-region1-oai.openai.azure.com/openai" /> </when> <otherwise> <cache-lookup-value key="backend-counter" variable-name="backend-counter" /> <choose> <when condition="@(context.Variables.ContainsKey("backend-counter") == false)"> <set-variable name="backend-counter" value="@(0)" /> </when> </choose> <set-variable name="current-backend-index" value="@((int)context.Variables["backend-counter"] % 7)" /> <choose> <when condition="@((int)context.Variables["current-backend-index"] == 0)"> <set-variable name="selected-backend-url" value="https://your-region1-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 1)"> <set-variable name="selected-backend-url" value="https://your-region2-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 2)"> <set-variable name="selected-backend-url" value="https://your-region3-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 3)"> <set-variable name="selected-backend-url" value="https://your-region4-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 4)"> <set-variable name="selected-backend-url" value="https://your-region5-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 5)"> <set-variable name="selected-backend-url" value="https://your-region6-oai.openai.azure.com/openai" /> </when> <when condition="@((int)context.Variables["current-backend-index"] == 6)"> <set-variable name="selected-backend-url" value="https://your-region7-oai.openai.azure.com/openai" /> </when> </choose> <set-variable name="next-counter" value="@(((int)context.Variables["backend-counter"] + 1) % 1000)" /> <cache-store-value key="backend-counter" value="@((int)context.Variables["next-counter"])" duration="300" /> </otherwise> </choose> <!-- Always set backend service using selected-backend-url variable --> <set-backend-service base-url="@((string)context.Variables["selected-backend-url"])" /> <!-- Inherit any base policies defined outside this section --> <base /> </inbound> <backend> <base /> </backend> <outbound> <base /> </outbound> <on-error> <base /> </on-error> </policies> This policy creates a secure gateway that validates Azure AD tokens from your local Azure CLI session, then uses APIM's managed identity to authenticate with Azure OpenAI backends, eliminating the need for API keys. It automatically load-balances requests across multiple Azure OpenAI regions using round-robin distribution for optimal performance. 4. Create Azure APIM proxy for Cline This FastAPI-based proxy forwards OpenAI-compatible API requests from Cline through APIM using Azure AD authentication via Azure CLI credentials, eliminating the need to store or manage OpenAI API keys. Prerequisites: Python 3.8 or higher Azure CLI (ensure az login has been run at least once) Ensure the user running the proxy script has appropriate Azure AD roles and permissions. This script uses Azure CLI credentials to obtain bearer tokens. Your user account must have the correct roles assigned and access to the target API audience configured in the APIM policy above. Quick setup for the proxy: Create this requirements.txt: fastapi uvicorn requests azure-identity Create this Python script for the proxy source code azure_proxy.py: import os import requests from fastapi import FastAPI, Request from fastapi.responses import StreamingResponse import uvicorn from azure.identity import AzureCliCredential # CONFIGURATION APIM_BASE_URL = <APIM BASE URL HERE> AZURE_SCOPE = <AZURE SCOPE HERE> PORT = int(os.environ.get("PORT", 8080)) app = FastAPI() credential = AzureCliCredential() # Use a single requests.Session for connection pooling from requests.adapters import HTTPAdapter session = requests.Session() session.mount("https://", HTTPAdapter(pool_connections=100, pool_maxsize=100)) import time _cached_token = None _cached_expiry = 0 def get_bearer_token(scope: str) -> str: """Get an access token using AzureCliCredential, caching until expiry is within 30 seconds.""" global _cached_token, _cached_expiry now = int(time.time()) if _cached_token and (_cached_expiry - now > 30): return _cached_token try: token_obj = credential.get_token(scope) _cached_token = token_obj.token _cached_expiry = token_obj.expires_on return _cached_token except Exception as e: raise RuntimeError(f"Could not get Azure access token: {e}") @app.api_route("/{path:path}", methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"]) async def proxy(request: Request, path: str): # Assemble the destination URL (preserve trailing slash logic) dest_url = f"{APIM_BASE_URL.rstrip('/')}/{path}".rstrip("/") if request.url.query: dest_url += "?" + request.url.query # Get the Bearer token bearer_token = get_bearer_token(AZURE_SCOPE) # Prepare headers (copy all, overwrite Authorization) headers = dict(request.headers) headers["Authorization"] = f"Bearer {bearer_token}" headers.pop("host", None) # Read body body = await request.body() # Send the request to APIM using the pooled session resp = session.request( method=request.method, url=dest_url, headers=headers, data=body if body else None, stream=True, ) # Stream the response back to the client return StreamingResponse( resp.raw, status_code=resp.status_code, headers={k: v for k, v in resp.headers.items() if k.lower() != "transfer-encoding"}, ) if __name__ == "__main__": # Bind the app to 127.0.0.1 to avoid any Firewall updates uvicorn.run(app, host="127.0.0.1", port=PORT) Run the setup: pip install -r requirements.txt az login # Authenticate with Azure python azure_proxy.py Configure Cline to use the proxy: Using the OpenAI Compatible API Provider: Base URL: http://localhost:8080 API Key: <any random string> Model ID: <your Azure OpenAI deployment name> API Version: <your Azure OpenAI deployment version> The API key field is required by Cline but unused in our implementation - any random string works since authentication happens via Azure AD. 5. Configure Cline to listen to your MCP Server Now that we have both our MCP server running and Cline configured with secure OpenAI access, the final step is connecting them together. To enable Cline to discover and use your custom tools, navigate to your installed MCP servers on Cline, select Configure MCP Servers, and add in the configuration for your server: { "mcpServers": { "mcp-tools": { "autoApprove": [ "EchoCaps", "ReverseEcho", ], "disabled": false, "timeout": 60, "type": "sse", "url": "http://<your localhost url>/sse" } } } Now, you can use Cline's chat interface to interact with your secure MCP tools. Try asking Cline to use your custom tools - for example, "Can you echo 'Hello World' in capital letters?" and watch as it calls your local MCP server through the infrastructure you've built. Conclusion There you have it: A secure implementation of MCP that can be tailored to your specific use case. This approach gives you the power of MCP while maintaining enterprise security. You get: AI capabilities through secure Azure infrastructure. Custom tools that never leave your environment. Standard MCP interface for easy integration. Complete control over your data and tools. The key is keeping MCP servers local while routing AI requests through your secure Azure infrastructure. This way, you gain MCP's benefits without compromising security. Disclaimer While this tutorial provides a secure foundation for MCP implementation, organizations are responsible for configuring their Azure resources according to their specific security requirements and compliance standards. Ensure proper review of network rules, access policies, and authentication configurations before deploying to production environments. Resources MCP SDKs and Tools: MCP C# SDK MCP Python SDK Cline SDK Cline User Guide Azure OpenAI with APIM Azure API Management Network Security: Azure API Management - restrict caller IPs Azure API Management with an Azure virtual network Set up inbound private endpoint for Azure API Management Azure OpenAI and AI Services Network Security: Configure Virtual Networks for Azure AI services Securing Azure OpenAI inside a virtual network with private endpoints Add an Azure OpenAI network security perimeter az cognitiveservices account network-ruleIntroducing developer solutions for Microsoft Sentinel platform
Security is being reengineered for the AI era, moving beyond static, rule-bound controls and toward after-the-fact response toward platform-led, machine-speed defense. The challenge is clear: fragmented tools, sprawling signals, and legacy architectures that can’t match the velocity and scale of modern attacks. What’s needed is an AI-ready, data-first foundation - one that turns telemetry into a security graph, standardizes access for agents, and coordinates autonomous actions while keeping humans in command of strategy and high-impact investigations. Security teams already center operations on their SIEM for end-to-end visibility, and we’re advancing that foundation by evolving Microsoft Sentinel into both the SIEM and the platform for agentic defense—connecting analytics and context across ecosystems. And today, we’re introducing new platform capabilities that build on Sentinel data lake: Sentinel graph for deeper insight and context; Sentinel MCP server and tools to make data agent ready; new developer capabilities; and Security Store for effortless discovery and deployment—so protection accelerates to machine speed while analysts do their best work. Today, customers use a breadth of solutions to keep themselves secure. Each solution typically ingests, processes, and stores the security data it needs which means applications maintain identical copies of the same underlying data. This is painful for both customers and partners, who don’t want to build and maintain duplicate infrastructure and create data silos that make it difficult to counter sophisticated attacks. With today’s announcement, we’re directly addressing those challenges by giving partners the ability to create solutions that can reason over the single copy of the security data that each customer has in their Sentinel data lake instance. Partners can create AI solutions that use Sentinel and Security Copilot and distribute them in Microsoft Security Store to reach audiences, grow their revenue, and keep their customers safe. Sentinel already has a rich partner ecosystem with hundreds of SIEM solutions that include connectors, playbooks, and other content types. These new platform capabilities extend those solutions, creating opportunities for partners to address new scenarios and bring those solutions to market quickly since they don’t need to build complex data pipelines or store and process new data sets in their own infrastructure. For example, partners can use Sentinel connectors to bring their own data into the Sentinel data lake. They can create Jupyter notebook jobs in the updated Sentinel Visual Studio Code extension to analyze that data or take advantage of the new Model Context Protocol (MCP) server which makes the data understandable and accessible to AI agents in Security Copilot. With Security Copilot’s new vibe-coding capabilities, partners can create their agent in the same Sentinel Visual Studio Code extension or the environment of their choice. The solution can then be packaged and published to the new Microsoft Security Store, which gives partners an opportunity to expand their audience and grow their revenue while protecting more customers across the ecosystem. These capabilities are being embraced across our ecosystem by mature and emerging partners alike. Services partners such as Accenture and ISVs such as Zscaler and ServiceNow are already creating solutions that leverage the capabilities of the Sentinel platform. Partners have already brought several solutions to market using the integrated capabilities of the Sentinel platform: Illumio. Illumio for Microsoft Sentinel combines Illumio Insights with Microsoft Sentinel data lake and Security Copilot to revolutionize detection and response to cyber threats. It fuses data from Illumio and all the other sources feeding into Sentinel to deliver a unified view of threats, giving SOC analysts, incident responders, and threat hunters visibility and AI-driven breach containment capabilities for lateral traffic threats and attack paths across hybrid and multi-cloud environments. To learn more, visit Illumio for Microsoft Sentinel. OneTrust. OneTrust’s AI-ready governance platform enables 14,000 customers globally – including over half of the Fortune 500 – to accelerate innovation while ensuring responsible data use. Privacy and risk teams know that undiscovered personal data in their digital estate puts their business and customers at risk. OneTrust’s Privacy Risk Agent uses Security Copilot, Purview scan logs, Entra ID data, and Jupyter notebook jobs in the Sentinel data lake to automatically discover personal data, assess risk, and take mitigating actions. To learn more, visit here. Tanium. The Tanium Security Triage Agent accelerates alert triage using real-time endpoint intelligence from Tanium. Tanium intends to expand its agent to ingest contextual identity data from Microsoft Entra using Sentinel data lake. Discover how Tanium’s integrations empower IT and security teams to make faster, more informed decisions. Simbian. Simbian’s Threat Hunt Agent makes hunters more effective by automating the process of validating threat hunt hypotheses with AI. Threat hunters provide a hypothesis in natural language, and the Agent queries and analyzes the full breadth of data available in Sentinel data lake to validate the hypothesis and do deep investigation. Simbian's AI SOC Agent investigates and responds to security alerts from Sentinel, Defender, and other alert sources and also uses Sentinel data lake to enhance the depth of investigations. Learn more here. Lumen. Lumen’s Defender℠ Threat Feed for Microsoft Sentinel helps customers correlate known-bad artifacts with activity in their environment. Lumen’s Black Lotus Labs® harnesses unmatched network visibility and machine intelligence to produce high-confidence indicators that can be operationalized at scale for detection and investigation. Currently Lumen’s Defender℠ Threat Feed for Microsoft Sentinel is available as an invite only preview. To request an invite, reach out to the Lumen Defender Threat Feed Sales team. The updated Sentinel Visual Studio Code extension for Microsoft Sentinel The Sentinel Extension for Visual Studio code brings new AI and packaging capabilities on top of existing Jupyter notebook jobs to help developers efficiently create new solutions. Building with AI Impactful AI security solutions need access and understanding of relevant security data to address a scenario. The new Microsoft Sentinel Model Context Protocol (MCP) server makes data in Sentinel data lake AI-discoverable and understandable to agents so they can reason over it to generate powerful new insights. It integrates with the Sentinel VS Code extension so developers can use those tools to explore the data in the lake and have agents use those tools as they do their work. To learn more, read the Microsoft Sentinel MCP server announcement. Microsoft is also releasing MCP tools to make creating AI agents more straightforward. Developers can use Security Copilot’s MCP tools to create agents within either the Sentinel VS Code extension or the environment of their choice. They can also take advantage of the low code agent authoring experience right in the Security Copilot portal. To learn more about the Security Copilot pro code and low code agent authoring experiences visit the Security Copilot blog post on Building your own Security Copilot agents. Jupyter Notebook Jobs Jupyter notebooks jobs are an important part of the Sentinel data lake and were launched at our public preview a couple of months ago. See the documentation here for more details on Jupyter notebooks jobs and how they can be used in a solution. Note that when jobs write to the data lake, agents can use the Sentinel MCP tools to read and act on those results in the same way they’re able to read any data in the data lake. Packaging and Publishing Developers can now package solutions containing notebook jobs and Copilot agents so they can be distributed through the new Microsoft Security Store. With just a few clicks in the Sentinel VS Code extension, a developer can create a package which they can then upload to Security Store. Distribution and revenue opportunities with Security Store Sentinel platform solutions can be packaged and offered through the new Microsoft Security Store, which gives partners new ways to grow revenue and reach customers. Learn more about the ways Microsoft Security Store can help developers reach customers and grow revenue by visiting securitystore.microsoft.com. Getting started Developers can get started building powerful applications that bring together Sentinel data, Jupyter notebook jobs, and Security Copilot today: Become a partner to publish solutions to Microsoft Security Store Onboarding to Sentinel data lake Downloading the Sentinel Visual Studio Code extension Learn about Security Copilot news Learn about Microsoft Security StoreCybersecurity: What Every Business Leader Needs to Know Now
As a Senior Cybersecurity Solution Architect, I’ve had the privilege of supporting organisations across the United Kingdom, Europe, and the United States—spanning sectors from finance to healthcare—in strengthening their security posture. One thing has become abundantly clear: cybersecurity is no longer the sole domain of IT departments. It is a strategic imperative that demands attention at board-level. This guide distils five key lessons drawn from real-world engagements to help executive leaders navigate today’s evolving threat landscape. These insights are not merely technical—they are cultural, operational, and strategic. If you’re a C-level executive, this article is a call to action: reassess how your organisation approaches cybersecurity before the next breach forces the conversation. In this article, I share five lessons (and quotes) from the field that help demystify how to enhance an organisation’s security posture. 1. Shift the Mindset “This has always been our approach, and we’ve never experienced a breach—so why should we change it?” A significant barrier to effective cybersecurity lies not in the sophistication of attackers, but in the predictability of human behaviour. If you’ve never experienced a breach, it’s tempting to maintain the status quo. However, as threats evolve, so too must your defences. Many cyber threats exploit well-known vulnerabilities that remain unpatched or rely on individuals performing routine tasks in familiar ways. Human nature tends to favour comfort and habit—traits that adversaries are adept at exploiting. Unlike many organisations, attackers readily adopt new technologies to advance their objectives, including AI-powered ransomware to execute increasingly sophisticated attacks. It is therefore imperative to recognise—without delay—that the advent of AI has dramatically reduced both the effort and time required to compromise systems. As the UK’s National Cyber Security Centre (NCSC) has stated: “AI lowers the barrier for novice cyber criminals, hackers-for-hire and hacktivists to carry out effective access and information gathering operations. This enhanced access will likely contribute to the global ransomware threat over the next two years.” Similarly, McKinsey & Company observed: “As AI quickly advances cyber threats, organisations seem to be taking a more cautious approach, balancing the benefits and risks of the new technology while trying to keep pace with attackers’ increasing sophistication.” To counter this evolving threat landscape, organisations must proactively leverage AI in their cyber defence strategies. Examples include: Identity and Access Management (IAM): AI enhances IAM by analysing real-time signals across systems to detect risky sign-ins and enforce adaptive access controls. Example: Microsoft Entra Agents for Conditional Access use AI to automate policy recommendations, streamlining access decisions with minimal manual input. Figure 1: Microsoft Entra Agents Threat Detection: AI accelerates detection, response, and recovery, helping organisations stay ahead of sophisticated threats. Example: Microsoft Defender for Cloud’s AI threat protection identifies prompt injection, data poisoning, and wallet attacks in real time. Incident Response: AI facilitates real-time decision-making, removing emotional bias and accelerating containment and recovery during security incidents. Example: Automatic Attack Disruption in Defender XDR, which can automatically contain a breach in progress. AI Security Posture Management AI workloads require continuous discovery, classification, and protection across multi-cloud environments. Example: Microsoft Defender for Cloud’s AI Security Posture Management secures custom AI apps across Azure, AWS, and GCP by detecting misconfigurations, vulnerabilities, and compliance gaps. Data Security Posture Management (DSPM) for AI AI interactions must be governed to ensure privacy, compliance, and insider risk mitigation. Example: Microsoft Purview DSPM for AI enables prompt auditing, applies Data Loss Prevention (DLP) policies to third-party AI apps like ChatGPT, and supports eDiscovery and lifecycle management. AI Threat Protection Organisations must address emerging AI threat vectors, including prompt injection, data leakage, and model exploitation. Example: Defender for AI (private preview) provides model-level security, including governance, anomaly detection, and lifecycle protection. Embracing innovation, automation, and intelligent defence is the secret sauce for cyber resilience in 2026. 2. Avoid One-Off Purchases – Invest with a Strategy “One MDE and one Sentinel to go, please.” Organisations often approach me intending to purchase a specific cybersecurity product—such as Microsoft Defender for Endpoint (MDE)—without a clearly articulated strategic rationale. My immediate question is: what is the broader objective behind this purchase? Is it driven by perceived value or popularity, or does it form part of a well-considered strategy to enhance endpoint security? Cybersecurity investments should be guided by a long-term, holistic strategy that spans multiple years and is periodically reassessed to reflect evolving threats. Strengthening endpoint protection must be integrated into a wider effort to improve the organisation’s overall security posture. This includes ensuring seamless integration between security solutions and avoiding operational silos. For example, deploying robust endpoint protection is of limited value if identities are not safeguarded with multi-factor authentication (MFA), or if storage accounts remain publicly accessible. A cohesive and forward-looking approach ensures that all components of the security architecture work in concert to mitigate risk effectively. Security Adoption Journey (Based on Zero Trust Framework) Assess – Evaluate the threat landscape, attack surface, vulnerabilities, compliance obligations, and critical assets. Align – Link security objectives to broader business goals to ensure strategic coherence. Architect – Design integrated and scalable security solutions, addressing gaps and eliminating operational silos. Activate – Implement tools with robust governance and automation to ensure consistent policy enforcement. Advance – Continuously monitor, test, and refine the security posture to stay ahead of evolving threats. Security tools are not fast food—they work best as part of a long-term plan, not a one-off order. This piecemeal approach runs counter to the modern Zero Trust security model, which assumes no single tool will prevent every breach and instead implements layered defences and integration. 3. Legacy Systems Are Holding You Back “Unfortunately, we are unable to implement phishing-resistant MFA, as our legacy app does not support integration with the required protocols.” A common challenge faced by many organisations I have worked with is the constraint on innovation within their cybersecurity architecture, primarily due to continued reliance on legacy applications—often driven by budgetary or operational necessity. These outdated systems frequently lack compatibility with modern security technologies and may introduce significant vulnerabilities. A notable example is the deployment of phishing-resistant multi-factor authentication (MFA)—such as FIDO2 security keys or certificate-based authentication—which requires advanced identity protocols and conditional access policies. These capabilities are available exclusively through Microsoft Entra ID. To address this issue effectively, it is essential to design security frameworks based on the organisation’s future aspirations rather than its current limitations. By adopting a forward-thinking approach, organisations can remain receptive to emerging technologies that align with their strategic cybersecurity objectives. Moreover, this perspective encourages investment in acquiring the necessary talent, thereby reducing reliance on extensive change management and staff retraining. I advise designing for where you want to be in the next 1–3 years—ideally cloud-first and identity-driven—essentially adopting a Zero Trust architecture, rather than being constrained by the limitations of legacy systems. 4. Collaboration Is a Security Imperative “This item will need to be added to the dev team's backlog. Given their current workload, they will do their best to implement GitHub Security in Q3, subject to capacity.” Cybersecurity threats may originate from various parts of an organisation, and one of the principal challenges many face is the fragmented nature of their defence strategies. To effectively mitigate such risks, cybersecurity must be embedded across all departments and functions, rather than being confined to a single team or role. In many organisations, the Chief Information Security Officer (CISO) operates in isolation from other C-level executives, which can limit their influence and complicate the implementation of security measures across the enterprise. Furthermore, some teams may lack the requisite expertise to execute essential security practices. For instance, an R&D lead responsible for managing developers may not possess the necessary skills in DevSecOps. To address these challenges, it is vital to ensure that the CISO is empowered to act without political or organisational barriers and is supported in implementing security measures across all business units. When the CISO has backing from the COO and HR, initiatives such as MFA rollout happen faster and more thoroughly. Cross-Functional Security Responsibilities Role Security Responsibilities R&D - Adopt DevSecOps practices - Identify vulnerabilities early - Manage code dependencies - Detect exposed secrets - Embed security in CI/CD pipelines CIO - Ensure visibility over organizational data - Implement Data Loss Prevention (DLP) - Safeguard sensitive data lifecycle - Ensure regulatory compliance CTO - Secure cloud environments (CSPM) - Manage SaaS security posture (SSPM) - Ensure hardware and endpoint protection COO - Protect digital assets - Secure domain management - Mitigate impersonation threats - Safeguard digital marketing channels and customer PII Support & Vendors - Deliver targeted training - Prevent social engineering attacks - Improve awareness of threat vectors HR - Train employees on AI-related threats - Manage insider risks - Secure employee data - Oversee cybersecurity across the employee lifecycle Empowering the CISO to act across departments helps organisations shift towards a security-first culture—embedding cybersecurity into every function, not just IT. 5. Compliance Is Not Security “We’re compliant, so we must be secure.” Many organisations mistakenly equate passing audits—such as ISO 27001 or SOC 2—with being secure. While compliance frameworks help establish a baseline for security, they are not a guarantee of protection. Determined attackers are not deterred by audit checklists; they exploit gaps, misconfigurations, and human error regardless of whether an organisation is certified. Moreover, due to the rapidly evolving nature of the cyber threat landscape, compliance frameworks often struggle to keep pace. By the time a standard is updated, attackers may already be exploiting new techniques that fall outside its scope. This lag creates a false sense of security for organisations that rely solely on regulatory checkboxes. Security is a continuous risk management process—not a one-time certification. It must be embedded into every layer of the enterprise and treated with the same urgency as other core business priorities. Compliance may be the starting line, not the finish line. Effective security goes beyond meeting regulatory requirements—it demands ongoing vigilance, adaptability, and a proactive mindset. Conclusion: Cybersecurity Is a Continuous Discipline Cybersecurity is not a destination—it is a continuous journey. By embracing strategic thinking, cross-functional collaboration, and emerging technologies, organisations can build resilience against today’s threats and tomorrow’s unknowns. The lessons shared throughout this article are not merely technical—they are cultural, operational, and strategic. If there is one key takeaway, it is this: avoid piecemeal fixes and instead adopt an integrated, future-ready security strategy. Due to the rapidly evolving nature of the cyber threat landscape, compliance frameworks alone cannot keep pace. Security must be treated as a dynamic, ongoing process—one that is embedded into every layer of the enterprise and reviewed regularly. Organisations should conduct periodic security posture reviews, leveraging tools such as Microsoft Secure Score or monthly risk reports, and stay informed about emerging threats through threat intelligence feeds and resources like the Microsoft Digital Defence Report, CISA (Cybersecurity and Infrastructure Security Agency), NCSC (UK National Cyber Security Centre), and other open-source intelligence platforms. As Ann Johnson aptly stated in her blog: “The most prepared organisations are those that keep asking the right questions and refining their approach together.” Cyber resilience demands ongoing investment—in people (through training and simulation drills), in processes (via playbooks and frameworks), and in technology (through updates and adoption of AI-driven defences). To reduce cybersecurity risk over time, resilient organisations must continually refine their approach and treat cybersecurity as an ongoing discipline. The time to act is now. Resources: https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat Defend against cyber threats with AI solutions from Microsoft - Microsoft Industry Blogs Generative AI Cybersecurity Solutions | Microsoft Security Require phishing-resistant multifactor authentication for Microsoft Entra administrator roles - Microsoft Entra ID | Microsoft Learn AI is the greatest threat—and defense—in cybersecurity today. Here’s why. Microsoft Entra Agents - Microsoft Entra | Microsoft Learn Smarter identity security starts with AI https://www.microsoft.com/en-us/security/blog/2025/06/12/cyber-resilience-begins-before-the-crisis/ https://www.microsoft.com/en-us/security/security-insider/threat-landscape/microsoft-digital-defense-report-2023-critical-cybersecurity-challenges https://www.microsoft.com/en-us/security/blog/2025/06/12/cyber-resilience-begins-before-the-crisis/No More Guesswork—Copilot Makes Azure Security Crystal Clear
Elevating Azure Security and Compliance In today’s rapidly evolving digital landscape, security and compliance are more critical than ever. As organizations migrate workloads to Azure, the need for robust security frameworks and proactive compliance strategies grows. Security Copilot, integrated with Azure, is transforming how technical teams approach these challenges, empowering users to build secure, compliant environments with greater efficiency and confidence. As a security expert, I’d like to provide clear guidance on how to effectively utilize Security Copilot in the ever-evolving landscape of security and compliance. Security Copilot is a premium offering; it includes advanced capabilities that go beyond standard Azure security tools. These features may require specific licensing or subscription tiers. It provides deeper insights, enhanced automation, and tailored guidance for complex security scenarios. Below, I’ll highlight a range of security topics with sample Copilot prompts that you can use to help create a more secure and compliant environment. Getting Started with Microsoft Security Copilot Before leveraging the advanced capabilities of Security Copilot, it's important to understand the foundational requirements and setup steps: Azure Subscription Requirement Security Copilot is not automatically available in all Azure subscriptions. To use it, your organization must have an active Azure subscription. This is necessary to provision Security Compute Units (SCUs), which are the core resources that power Copilot workloads. Provisioning Security Compute Units (SCUs) SCUs are billed hourly and can be scaled based on workload needs. At least one SCU must be provisioned to activate Security Copilot. You can manage SCUs via the Azure portal or the Security Copilot portal, adjusting capacity as needed for performance and cost optimization. Role-Based Access Control To set up and manage Security Copilot: You need to be an Azure Owner or Contributor to provision SCUs. Users must be assigned appropriate Microsoft Entra roles (e.g., Security Administrator) to access and interact with Copilot features. Embedded Experience Security Copilot can be used as a standalone tool or embedded within other Microsoft services like Defender for Endpoint, Intune, and Purview, offering unified security management experience. Data Privacy and Security: Foundational Best Practices Why settle for generic security advice when Security Copilot delivers prioritized, actionable guidance backed by Microsoft’s best practices? Copilot doesn’t just recommend security measures, it actively helps you implement them, leveraging advanced features like encryption and granular access controls to safeguard every layer of your Azure environment. While Security Copilot doesn’t directly block threats like a firewall or Web Application Firewall (WAF), it enhances data integrity and confidentiality by analyzing security signals across Azure, identifying vulnerabilities, and guiding teams with prioritized, actionable recommendations. It helps implement encryption, access controls, and compliance-aligned configurations, while integrating with existing security tools to interpret logs and suggest containment strategies. By automating investigations and supporting secure-by-design practices, Copilot empowers organizations to proactively reduce breach risks and maintain a strong security posture. Secure Coding and Developer Productivity While Security Copilot supports secure coding by identifying vulnerabilities like SQL injection, Cross-Site Scripting (XSS), and buffer overflows, it is not a direct replacement for traditional code scanning tools, instead, it complements these tools by leveraging telemetry from integrated Microsoft services and applying AI-driven insights to prioritize risks and guide remediation. Copilot enhances developer productivity by interpreting signals, offering tailored recommendations, and embedding security practices throughout the software lifecycle. Understanding Security Protocols and Mechanisms Azure’s security stands on robust protocols and mechanisms but understanding them shouldn’t require a cryptography degree. Security Copilot demystifies encryption, authentication, and secure communications—making complex concepts accessible and actionable. With Security Copilot as your guide, teams can confidently configure Azure resources and respond to threats with informed, best-practice decisions. Compliance and Regulatory Alignment Regulatory requirements such as GDPR, HIPAA, and PCI-DSS don’t have to slow you down. Security Copilot streamlines Azure compliance with ready-to-use templates, clear guidelines, and robust documentation support. From maintaining audit logs to generating compliance reports, Security Copilot keeps every action tracked and organized—reducing non-compliance risk and making audits a breeze. Incident Response Planning No security strategy is complete without a solid incident response plan. Security Copilot equips Azure teams with detailed protocols for identifying, containing, and mitigating threats. It enhances Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) solutions through ready-made playbooks tailored to diverse scenarios. With built-in incident simulations, Copilot enables teams to rehearse and refine their responses—minimizing breach impact and accelerating recovery. Security Best Practices for Azure Staying ahead of threats means never standing still. Security Copilot builds on Azure’s proven security features—like multi-factor authentication, regular updates, and least privilege access—by automating their implementation, monitoring usage patterns, and surfacing actionable insights. It connects with tools like Microsoft Defender and Entra ID to interpret signals, recommend improvements, and guide teams in real time. With Copilot, your defenses don’t just follow best practices, they evolve dynamically to meet emerging threats, keeping your team sharp and your environment secure. Integrating Copilot into Your Azure Security Strategy Security Copilot isn’t just a technical tool—it’s your strategic partner for Azure security. By weaving Copilot into your workflows, you unlock advanced security enhancements, optimized code, and robust privacy protection. Its holistic approach ensures security and compliance are seamlessly integrated into every corner of your Azure environment. Conclusion Security Copilot is changing the game for Azure security and compliance. By blending secure coding, advanced security expertise, regulatory support, incident response playbooks, and best practices, Copilot empowers technical teams to build resilient, compliant cloud environments. As threats evolve, Copilot keeps your data protected and your organization ahead of the curve. Ready to take your Azure security and compliance to the next level? Start leveraging Security Copilot today to empower your team, streamline operations, and stay ahead of evolving threats. Dive deeper into best practices, hands-on tutorials, and expert guidance to maximize your security posture and unlock the full potential of Copilot in your organization. Explore, learn, and secure your cloud—your journey starts now! Further Reading & Resources Microsoft Security Copilot documentation Get started with Microsoft Security Copilot Microsoft Copilot in Azure Overview Security best practices and patterns - Microsoft Azure Azure compliance documentation Copilot Learning Hub Microsoft Security Copilot Blog Author: Microsoft Principal Technical Trainer, https://www.linkedin.com/in/eliasestevao/ #MicrosoftLearn #SkilledByMTT
