Automating Microsoft 365 with PowerShell Second Edition
The Office 365 for IT Pros team are thrilled to announce the availability of Automating Microsoft 365 with PowerShell (2nd edition). This completely revised 350-page book delivers the most comprehensive coverage of how to use Microsoft Graph APIs and the Microsoft Graph PowerShell SDK with Microsoft 365 workloads (Entra ID, Exchange Online, SharePoint Online, Teams, Planner, and more). Existing subscribers can download the second edition now free of charge. https://office365itpros.com/2025/06/30/automating-microsoft-365-with-powershell2/Architecting Microsoft 365 Environments for Multi-National Enterprises: Lessons from the Field
Introduction In today’s global economy, enterprises rely on Microsoft 365 to empower seamless collaboration across borders. However, deploying and securing multi-national M365 environments introduces complex technical, operational, and compliance challenges. With over two decades architecting cloud environments across the Americas, EMEA and APAC, I’ve led numerous deployments and migrations requiring hybrid identity resilience, data sovereignty compliance, and global operational continuity. This article presents field-tested lessons and strategic best practices to guide architects and IT leaders in designing robust, compliant, and scalable Microsoft 365 environments for multi-national operations. Key Challenges in Multi-National M365 Deployments 1. Hybrid Identity Complexity Managing synchronization between on-premises Active Directory and Azure AD becomes exponentially complex across regions. https://learn.microsoft.com/en-us/azure/active-directory/hybrid/whatis-hybrid-identity can introduce replication delays and login failures if not properly planned. Tip: Always assess latency impact on Kerberos authentication, token issuance, and Azure AD Connect synchronization cycles. 2. Data Residency and Compliance Many countries enforce strict data sovereignty laws restricting where personal and sensitive data can reside. Selecting tenant regions and enabling https://learn.microsoft.com/en-us/microsoft-365/enterprise/microsoft-365-multi-geo?view=o365-worldwide become critical to avoid compliance violations. Impact Example: A financial institution with European operations faced potential GDPR breaches until Multi-Geo was implemented to ensure Exchange Online and OneDrive data remained within EU boundaries. 3. Licensing and Cost Control Balancing E3, E5, and F3 licenses across countries with varying user roles and local currencies adds administrative and financial complexity. Best Practice: Implement https://learn.microsoft.com/en-us/azure/active-directory/enterprise-users/licensing-groups-assign, aligning assignments with security groups mapped to user personas. 4. Secure Collaboration Across Borders External sharing in SharePoint, OneDrive, and Teams federation introduces security risks if not precisely configured. Default sharing settings often exceed local compliance requirements, risking data leakage. Lesson Learned: Always validate external sharing policies against each country’s data protection laws and client contractual agreements. 5. Operational Support and SLA Alignment Global operations require support models beyond single-region business hours, demanding proactive incident response and escalation planning. Example: Implementing follow-the-sun support with regional admins trained on Microsoft 365 admin centers and PowerShell mitigates downtime risks. Strategic Solutions and Best Practices 1. Architect Hybrid Identity with Redundancy Deploy https://learn.microsoft.com/en-us/entra/identity/hybrid/connect/how-to-connect-sync-staging-server in alternate datacenters. Implement Password Hash Sync to reduce dependency on VPN and WAN availability for authentication. 2. Utilize Microsoft 365 Multi-Geo Capabilities Leverage https://learn.microsoft.com/en-us/microsoft-365/enterprise/microsoft-365-multi-geo?view=o365-worldwide to meet data residency requirements per geography. Validate licensing implications and admin configurations for each satellite location. 3. Segment Licensing by User Persona Define clear user personas (executives, knowledge workers, frontline staff). Map license types accordingly, optimizing costs while ensuring productivity needs are met. Use https://learn.microsoft.com/en-us/azure/active-directory/enterprise-users/licensing-groups-assign for scalable management. 4. Design Conditional Access Policies by Geography Create https://learn.microsoft.com/en-us/azure/active-directory/conditional-access/location-condition. Integrate with Intune compliance policies to block or limit access for non-compliant devices. 5. Implement a Global Governance Model Establish clear local vs. global admin roles to maintain accountability. Enforce https://learn.microsoft.com/en-us/azure/active-directory/privileged-identity-management/pim-configure to control and audit privileged access. Lessons Learned from the Field Latency is a silent killer – Always test Microsoft Teams and OneDrive performance across regions before production rollouts. Communication is critical – Local IT teams must align early with global security and compliance strategies. Compliance first – Never assume Microsoft’s default data location suffices for local regulations. Cost optimization is ongoing – Conduct license audits and adjust assignments every six months. Conclusion Architecting Microsoft 365 for a multi-national enterprise demands strategic integration of compliance, hybrid identity resilience, secure collaboration, and cost optimization. Cloud success in a global enterprise is not an accident – it is architected. By applying these best practices validated against Microsoft recommendations and real-world deployments, organizations can empower global collaboration without sacrificing governance or security. About the Author Gonzalo Brown Ruiz is a Senior Office 365 Engineer with over 21 years architecting secure, compliant cloud environments across North America, Latin America, EMEA and APAC. He specializes in Microsoft Purview, Entra ID, Exchange Online, eDiscovery, and enterprise cloud security.Securing the Modern Workplace: Transitioning from Legacy Authentication to Conditional Access
Authored by: Gonzalo Brown Ruiz, Senior Microsoft 365 Engineer & Cloud Security Specialist Date: July 2025 Introduction In today’s threat landscape, legacy authentication is one of the weakest links in enterprise security. Protocols like POP, IMAP, SMTP Basic, and MAPI are inherently vulnerable — they don’t support modern authentication methods like MFA and are frequently targeted in credential stuffing and password spray attacks. Despite the known risks, many organizations still allow legacy authentication to persist for “just one app” or “just a few users.” This article outlines a real-world, enterprise-tested strategy for eliminating legacy authentication and implementing a Zero Trust-aligned Conditional Access model using Microsoft Entra ID. Why Legacy Authentication Must Die No support for MFA: Enables attackers to bypass the most critical security control Password spray heaven: Common vector for brute-force and scripted login attempts Audit blind spots: Limited logging and correlation in modern SIEM tools Blocks Zero Trust progress: Hinders enforcement of identity- and device-based policies Removing legacy auth isn’t a nice-to-have — it’s a prerequisite for a modern security strategy. Phase 1: Auditing Your Environment A successful transition starts with visibility. Before blocking anything, I led an environment-wide audit to identify: All sign-ins using legacy protocols (POP, IMAP, SMTP AUTH, MAPI) App IDs and service principals requesting basic auth Users with outdated clients (Office 2010/2013) Devices and applications integrated via PowerShell, Azure Sign-In Logs, and Workbooks Tools used: Microsoft 365 Sign-In Logs Conditional Access insights workbook PowerShell (Get-SignInLogs, Get-CASMailbox, etc.) Phase 2: Policy Design and Strategy The goal is not just to block — it’s to transform authentication securely and gradually. My Conditional Access strategy included: Blocking legacy authentication protocols while allowing scoped exceptions Report-only mode to assess potential impact Role-based access rules (admins, execs, vendors, apps) Geo-aware policies and MFA enforcement Service account handling and migration to Graph or Modern Auth-compatible apps Key considerations: Apps that support legacy auth only Delegates and shared mailbox access scenarios BYOD and conditional registration enforcement Phase 3: Staged Rollout and Enforcement A phased approach reduced friction: Pilot group enforcement (IT, InfoSec, willing users) Report-only monitoring across business units Clear communications to stakeholders and impacted users User education campaigns on legacy app retirement Gradual enforcement by department, geography, or risk tier We used Microsoft Entra’s built-in messaging and Service Health alerts to notify users of policy triggers. Phase 4: Monitoring, Tuning, and Incident Readiness Once policies were in place: Monitored Sign-in logs for policy match rates and unexpected denials Used Microsoft Defender for Identity to correlate legacy sign-in attempts Created alerts and response playbooks for blocked sign-in anomalies Results: 100% of all user and app traffic transitioned to Modern Auth Drastic reduction in brute force traffic from foreign IPs Fewer support tickets around password lockouts and MFA prompts Lessons Learned Report-only mode is your best friend. Avoids surprise outages. Communication beats configuration. Even a perfect policy fails if users are caught off guard. Legacy mail clients still exist in vendor tools and old mobile apps. Service accounts can break silently. Replace or modernize them early. CA exclusions are dangerous. Every exception must be time-bound and documented. Conclusion Eliminating legacy authentication is not just a policy update — it’s a cultural shift toward Zero Trust. By combining deep visibility, staged enforcement, and a user-centric approach, organizations can securely modernize their identity perimeter. Microsoft Entra Conditional Access is more than a policy engine — it is the architectural pillar of enterprise-grade identity security. Author’s Note: This article is based on my real-world experience designing and enforcing Conditional Access strategies across global hybrid environments with Microsoft 365 and Azure AD/Entra ID. Copyright © 2025 Gonzalo Brown Ruiz. All rights reserved.Primer: How to Use RBAC for Applications to Control App Use of the Mail.Send Permission
The temptation to use the Mail.Send application permission in scripts can lead PowerShell developers into trouble because the permission allows access to all mailboxes, including sensitive executive and financial mailboxes. Fortunately, RBAC for Applications allows tenants to control the access that apps have to mailboxes and other Exchange content. All explained here with an example script to test RBAC of Applications. https://office365itpros.com/2026/02/17/mail-send-rbac-for-applications/How to Deactivate an Entra ID Application
This article explores how to deactivate applications (aka disable apps) in Entra ID. Everything is done through PowerShell and the Microsoft Graph PowerShell SDK because the feature isn’t currently available in the Entra admin center. We’ve even included a fully functional example script to show you how the process works. Feel free to fix or enhance our code in GitHub! https://office365itpros.com/2026/02/11/deactivate-application-entra/Building with Azure OpenAI Sora: A Complete Guide to AI Video Generation
In this comprehensive guide, we'll explore how to integrate both Sora 1 and Sora 2 models from Azure OpenAI Service into a production web application. We'll cover API integration, request body parameters, cost analysis, limitations, and the key differences between using Azure AI Foundry endpoints versus OpenAI's native API. Table of Contents Introduction to Sora Models Azure AI Foundry vs. OpenAI API Structure API Integration: Request Body Parameters Video Generation Modes Cost Analysis per Generation Technical Limitations & Constraints Resolution & Duration Support Implementation Best Practices Introduction to Sora Models Sora is OpenAI's groundbreaking text-to-video model that generates realistic videos from natural language descriptions. Azure AI Foundry provides access to two versions: Sora 1: The original model focused primarily on text-to-video generation with extensive resolution options (480p to 1080p) and flexible duration (1-20 seconds) Sora 2: The enhanced version with native audio generation, multiple generation modes (text-to-video, image-to-video, video-to-video remix), but more constrained resolution options (720p only in public preview) Azure AI Foundry vs. OpenAI API Structure Key Architectural Differences Sora 1 uses Azure's traditional deployment-based API structure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/... Parameters: Uses Azure-specific naming like n_seconds, n_variants, separate width/height fields Job Management: Uses /jobs/{id} for status polling Content Download: Uses /video/generations/{generation_id}/content/video Sora 2 adapts OpenAI's v1 API format while still being hosted on Azure: Endpoint Pattern: https://{resource-name}.openai.azure.com/openai/deployments/{deployment-name}/videos Parameters: Uses OpenAI-style naming like seconds (string), size (combined dimension string like "1280x720") Job Management: Uses /videos/{video_id} for status polling Content Download: Uses /videos/{video_id}/content Why This Matters? This architectural difference requires conditional request formatting in your code: const isSora2 = deployment.toLowerCase().includes('sora-2'); if (isSora2) { requestBody = { model: deployment, prompt, size: `${width}x${height}`, // Combined format seconds: duration.toString(), // String type }; } else { requestBody = { model: deployment, prompt, height, // Separate dimensions width, n_seconds: duration.toString(), // Azure naming n_variants: variants, }; } API Integration: Request Body Parameters Sora 1 API Parameters Standard Text-to-Video Request: { "model": "sora-1", "prompt": "Wide shot of a child flying a red kite in a grassy park, golden hour sunlight, camera slowly pans upward.", "height": "720", "width": "1280", "n_seconds": "12", "n_variants": "2" } Parameter Details: model (String, Required): Your Azure deployment name prompt (String, Required): Natural language description of the video (max 32000 chars) height (String, Required): Video height in pixels width (String, Required): Video width in pixels n_seconds (String, Required): Duration (1-20 seconds) n_variants (String, Optional): Number of variations to generate (1-4, constrained by resolution) Sora 2 API Parameters Text-to-Video Request: { "model": "sora-2", "prompt": "A serene mountain landscape with cascading waterfalls, cinematic drone shot", "size": "1280x720", "seconds": "12" } Image-to-Video Request (uses FormData): const formData = new FormData(); formData.append('model', 'sora-2'); formData.append('prompt', 'Animate this image with gentle wind movement'); formData.append('size', '1280x720'); formData.append('seconds', '8'); formData.append('input_reference', imageFile); // JPEG/PNG/WebP Video-to-Video Remix Request: Endpoint: POST .../videos/{video_id}/remix Body: Only { "prompt": "your new description" } The original video's structure, motion, and framing are reused while applying the new prompt Parameter Details: model (String, Optional): Your deployment name prompt (String, Required): Video description size (String, Optional): Either "720x1280" or "1280x720" (defaults to "720x1280") seconds (String, Optional): "4", "8", or "12" (defaults to "4") input_reference (File, Optional): Reference image for image-to-video mode remix_video_id (String, URL parameter): ID of video to remix Video Generation Modes 1. Text-to-Video (Both Models) The foundational mode where you provide a text prompt describing the desired video. Implementation: const response = await fetch(endpoint, { method: 'POST', headers: { 'Content-Type': 'application/json', 'api-key': apiKey, }, body: JSON.stringify({ model: deployment, prompt: "A train journey through mountains with dramatic lighting", size: "1280x720", seconds: "12", }), }); Best Practices: Include shot type (wide, close-up, aerial) Describe subject, action, and environment Specify lighting conditions (golden hour, dramatic, soft) Add camera movement if desired (pans, tilts, tracking shots) 2. Image-to-Video (Sora 2 Only) Generate a video anchored to or starting from a reference image. Key Requirements: Supported formats: JPEG, PNG, WebP Image dimensions must exactly match the selected video resolution Our implementation automatically resizes uploaded images to match Implementation Detail: // Resize image to match video dimensions const targetWidth = parseInt(width); const targetHeight = parseInt(height); const resizedImage = await resizeImage(inputReference, targetWidth, targetHeight); // Send as multipart/form-data formData.append('input_reference', resizedImage); 3. Video-to-Video Remix (Sora 2 Only) Create variations of existing videos while preserving their structure and motion. Use Cases: Change weather conditions in the same scene Modify time of day while keeping camera movement Swap subjects while maintaining composition Adjust artistic style or color grading Endpoint Structure: POST {base_url}/videos/{original_video_id}/remix?api-version=2024-08-01-preview Implementation: let requestEndpoint = endpoint; if (isSora2 && remixVideoId) { const [baseUrl, queryParams] = endpoint.split('?'); const root = baseUrl.replace(/\/videos$/, ''); requestEndpoint = `${root}/videos/${remixVideoId}/remix${queryParams ? '?' + queryParams : ''}`; } Cost Analysis per Generation Sora 1 Pricing Model Base Rate: ~$0.05 per second per variant at 720p Resolution Scaling: Cost scales linearly with pixel count Formula: const basePrice = 0.05; const basePixels = 1280 * 720; // Reference resolution const currentPixels = width * height; const resolutionMultiplier = currentPixels / basePixels; const totalCost = basePrice * duration * variants * resolutionMultiplier; Examples: 720p (1280×720), 12 seconds, 1 variant: $0.60 1080p (1920×1080), 12 seconds, 1 variant: $1.35 720p, 12 seconds, 2 variants: $1.20 Sora 2 Pricing Model Flat Rate: $0.10 per second per variant (no resolution scaling in public preview) Formula: const totalCost = 0.10 * duration * variants; Examples: 720p (1280×720), 4 seconds: $0.40 720p (1280×720), 12 seconds: $1.20 720p (720×1280), 8 seconds: $0.80 Note: Since Sora 2 currently only supports 720p in public preview, resolution doesn't affect cost, only duration matters. Cost Comparison Scenario Sora 1 (720p) Sora 2 (720p) Winner 4s video $0.20 $0.40 Sora 1 12s video $0.60 $1.20 Sora 1 12s + audio N/A (no audio) $1.20 Sora 2 (unique) Image-to-video N/A $0.40-$1.20 Sora 2 (unique) Recommendation: Use Sora 1 for cost-effective silent videos at various resolutions. Use Sora 2 when you need audio, image/video inputs, or remix capabilities. Technical Limitations & Constraints Sora 1 Limitations Resolution Options: 9 supported resolutions from 480×480 to 1920×1080 Includes square, portrait, and landscape formats Full list: 480×480, 480×854, 854×480, 720×720, 720×1280, 1280×720, 1080×1080, 1080×1920, 1920×1080 Duration: Flexible: 1 to 20 seconds Any integer value within range Variants: Depends on resolution: 1080p: Variants disabled (n_variants must be 1) 720p: Max 2 variants Other resolutions: Max 4 variants Concurrent Jobs: Maximum 2 jobs running simultaneously Job Expiration: Videos expire 24 hours after generation Audio: No audio generation (silent videos only) Sora 2 Limitations Resolution Options (Public Preview): Only 2 options: 720×1280 (portrait) or 1280×720 (landscape) No square formats No 1080p support in current preview Duration: Fixed options only: 4, 8, or 12 seconds No custom durations Defaults to 4 seconds if not specified Variants: Not prominently supported in current API documentation Focus is on single high-quality generations with audio Concurrent Jobs: Maximum 2 jobs (same as Sora 1) Job Expiration: 24 hours (same as Sora 1) Audio: Native audio generation included (dialogue, sound effects, ambience) Shared Constraints Concurrent Processing: Both models enforce a limit of 2 concurrent video jobs per Azure resource. You must wait for one job to complete before starting a third. Job Lifecycle: queued → preprocessing → processing/running → completed Download Window: Videos are available for 24 hours after completion. After expiration, you must regenerate the video. Generation Time: Typical: 1-5 minutes depending on resolution, duration, and API load Can occasionally take longer during high demand Resolution & Duration Support Matrix Sora 1 Support Matrix Resolution Aspect Ratio Max Variants Duration Range Use Case 480×480 Square 4 1-20s Social thumbnails 480×854 Portrait 4 1-20s Mobile stories 854×480 Landscape 4 1-20s Quick previews 720×720 Square 4 1-20s Instagram posts 720×1280 Portrait 2 1-20s TikTok/Reels 1280×720 Landscape 2 1-20s YouTube shorts 1080×1080 Square 1 1-20s Premium social 1080×1920 Portrait 1 1-20s Premium vertical 1920×1080 Landscape 1 1-20s Full HD content Sora 2 Support Matrix Resolution Aspect Ratio Duration Options Audio Generation Modes 720×1280 Portrait 4s, 8s, 12s ✅ Yes Text, Image, Video Remix 1280×720 Landscape 4s, 8s, 12s ✅ Yes Text, Image, Video Remix Note: Sora 2's limited resolution options in public preview are expected to expand in future releases. Implementation Best Practices 1. Job Status Polling Strategy Implement adaptive backoff to avoid overwhelming the API: const maxAttempts = 180; // 15 minutes max let attempts = 0; const baseDelayMs = 3000; // Start with 3 seconds while (attempts < maxAttempts) { const response = await fetch(statusUrl, { headers: { 'api-key': apiKey }, }); if (response.status === 404) { // Job not ready yet, wait longer const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; continue; } const job = await response.json(); // Check completion (different status values for Sora 1 vs 2) const isCompleted = isSora2 ? job.status === 'completed' : job.status === 'succeeded'; if (isCompleted) break; // Adaptive backoff const delayMs = Math.min(15000, baseDelayMs + attempts * 1000); await new Promise(r => setTimeout(r, delayMs)); attempts++; } 2. Handling Different Response Structures Sora 1 Video Download: const generations = Array.isArray(job.generations) ? job.generations : []; const genId = generations[0]?.id; const videoUrl = `${root}/${genId}/content/video`; Sora 2 Video Download: const videoUrl = `${root}/videos/${jobId}/content`; 3. Error Handling try { const response = await fetch(endpoint, fetchOptions); if (!response.ok) { const error = await response.text(); throw new Error(`Video generation failed: ${error}`); } // ... handle successful response } catch (error) { console.error('[VideoGen] Error:', error); // Implement retry logic or user notification } 4. Image Preprocessing for Image-to-Video Always resize images to match the target video resolution: async function resizeImage(file: File, targetWidth: number, targetHeight: number): Promise<File> { return new Promise((resolve, reject) => { const img = new Image(); const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); img.onload = () => { canvas.width = targetWidth; canvas.height = targetHeight; ctx.drawImage(img, 0, 0, targetWidth, targetHeight); canvas.toBlob((blob) => { if (blob) { const resizedFile = new File([blob], file.name, { type: file.type }); resolve(resizedFile); } else { reject(new Error('Failed to create resized image blob')); } }, file.type); }; img.onerror = () => reject(new Error('Failed to load image')); img.src = URL.createObjectURL(file); }); } 5. Cost Tracking Implement cost estimation before generation and tracking after: // Pre-generation estimate const estimatedCost = calculateCost(width, height, duration, variants, soraVersion); // Save generation record await saveGenerationRecord({ prompt, soraModel: soraVersion, duration: parseInt(duration), resolution: `${width}x${height}`, variants: parseInt(variants), generationMode: mode, estimatedCost, status: 'queued', jobId: job.id, }); // Update after completion await updateGenerationStatus(jobId, 'completed', { videoId: finalVideoId }); 6. Progressive User Feedback Provide detailed status updates during the generation process: const statusMessages: Record<string, string> = { 'preprocessing': 'Preprocessing your request...', 'running': 'Generating video...', 'processing': 'Processing video...', 'queued': 'Job queued...', 'in_progress': 'Generating video...', }; onProgress?.(statusMessages[job.status] || `Status: ${job.status}`); Conclusion Building with Azure OpenAI's Sora models requires understanding the nuanced differences between Sora 1 and Sora 2, both in API structure and capabilities. Key takeaways: Choose the right model: Sora 1 for resolution flexibility and cost-effectiveness; Sora 2 for audio, image inputs, and remix capabilities Handle API differences: Implement conditional logic for parameter formatting and status polling based on model version Respect limitations: Plan around concurrent job limits, resolution constraints, and 24-hour expiration windows Optimize costs: Calculate estimates upfront and track actual usage for better budget management Provide great UX: Implement adaptive polling, progressive status updates, and clear error messages The future of AI video generation is exciting, and Azure AI Foundry provides production-ready access to these powerful models. As Sora 2 matures and limitations are lifted (especially resolution options), we'll see even more creative applications emerge. Resources: Azure AI Foundry Sora Documentation OpenAI Sora API Reference Azure OpenAI Service Pricing This blog post is based on real-world implementation experience building LemonGrab, my AI video generation platform that integrates both Sora 1 and Sora 2 through Azure AI Foundry. The code examples are extracted from production usage.
My Azure login is stuck at MFA and cannot proceed
In August, I was still able to log in to Azure, and by logging in through GitHub I could bypass 2FA. But now, no matter how I try, logging in via GitHub always requires 2FA. I can’t access my Azure account anymore—nothing works. The system prompts me to use Microsoft Authenticator to confirm a two-digit code in real time. My Microsoft Authenticator on my iPhone is logged into the same Microsoft account, but I’m not receiving any verification requests for Azure login. No matter how much I refresh, nothing shows up. I’ve already updated the Microsoft Authenticator app to the latest version from the App Store. However, my personal Microsoft account works fine and can log in without any issues.How to Control Access to Entra Multi-Tenant Apps
Entra multi-tenant applications can be used by any tenant – unless you restrict sign-in audiences to permit only specific tenants to use the application. In this article, we explain the preview feature and use the Microsoft Graph PowerShell SDK to restrict sign-in audiences by defining a list of permitted tenant identifiers in the properties of multi-tenant applications. https://office365itpros.com/2026/01/28/restrict-sign-in-audience/How Do I Target the Azure VPN Client in a Conditional Access Policy?
I am using the Azure VPN Client to connect users to an Azure VPN Gateway using their Entra ID credentials to authenticate. I want to target this application with a CA policy that requires MFA every time it connects. The problem is that I don't see the applications in my Enterprise Apps and all of my searching says that it won't appear because it was "pre-certified" by Microsoft. In the Gateway setup I used the Audience GUID of c632b3df-fb67-4d84-bdcf-b95ad541b5c8. And this is working as expected. The only solution that I have found for targeting the Azure VPN Client app is to create a Service Principal using that Audience GUID. This seems like a bit of a hack, so I am posting here to see if there are any other methods that I am missing to target this app when it doesn't appear in my Enterprise Apps list.
