Introducing Clinical Conflict Detection Safeguards in Healthcare Agent Service
Generative AI is becoming increasingly prevalent in healthcare, and its significance is continuing to grow. Given the documentation-intensive nature of healthcare, generative AI presents an excellent opportunity to help alleviate this burden. However, to truly offset the clinician workload, it is crucial that content is checked for reliability and consistency before it is validated by a human. We are pleased to announce the private preview of our clinical conflict detection safeguard, available through our healthcare agent service. This safeguard helps users identify potential clinical conflicts within documentation content, regardless of whether it was generated by a human or AI. Identifying Clinical Conflicts: Seven Detected Categories Every conflict identified by the clinical conflict detection safeguard will indicate the conflict type and reference document content that constitutes the conflict so that the healthcare provider user can validate and take appropriate actions. Opposition conflicts: Normal vs abnormal findings of the same body structure E.g. Left breast: Unremarkable <> The left breast demonstrates persistent circumscribed masses. Negative vs positive statements about the same clinical entity E.g. No cardiopulmonary disease <> Bibasilar atelectasis Lab/vital sign interpretation vs condition E.g. Low blood sugar level at admission <> Patient was admitted with hyperglycemia Opposite disorders/symptoms E.g. Hypernatremia <> Hyponatremia Sex information opposites E.g. Female patient comes in with ... <> Testis: Unremarkable Anatomical conflicts: Absent vs present body structures E.g. Cholelithiasis <> The gallbladder is absent History of removal procedure vs. present body structure E.g. Bilat Mastectomy (2010) <> Left breast: solid mass Conducted imaging study versus clinical finding of body structure E.g. Procedure: Chest XR <> Brain lesion Laterality mismatch of same clinical finding E.g. Results: Stable ductal carcinoma of left breast. <> A&P: Stage 0 stable ductal carcinoma of right breast. Value Conflicts: Condition vs. lab / vital sign / measurement E.g. Hypoglycemia <> Blood Gluc 145 Conflicting lab measurement on same timestamp E.g. 02/11/2022 WBC-8.0 <> 02/11/2022 WBC-5.5 Contraindication conflicts: Medication/substance allergy vs. prescribed medication E.g. He is allergic to acetaminophen. <> Home medication include Tylenol, ... Comparison conflicts: Increased/decreased statements vs. opposite measurements E.g. Ultrasound shows a 3 cm lesion in the bladder wall, previously 4 cm, an increase in size. Descriptive conflict: Positive vs unlikely statements of same condition E.g. Lungs: Pleural effusion is unlikely <> Assessment: Pleural effusion Conflicting characteristics of same condition E.g. Results: Stable small pleural effusion <> Impression: Small pleural effusion Multiple versus Single statement of same condition E.g. Findings: 9 mm lesion of upper pole right kidney <> Assessment: Right renal lesions Metadata conflicts: Age information in provided metadata vs documentation E.g. Date of Birth = “04-08-1990” Date of Service=”11-25-2024" <> A 42-year-old female presents for evaluation of pneumonia. Sex information in provided metadata vs documentation * E.g. Date of Service=”11-25-2024" Sex= “female” <> Finding: Prostate is enlarged A closer look Consider the following radiology report snippet: Exam: CT of the abdomen and pelvis Clinical history: LLQ pain x 10 days, cholecystectomy 6 weeks ago Findings: - New calcified densities are seen in the nondistended gallbladder. - Heterogeneous enhancement of the liver with periportal edema. No suspicious hepatic masses are identified. Portal veins are patent. - Gastrointestinal Tract: No abnormal dilation or wall thickening. Diverticulosis. - Kidneys are normal in size. The patient comes in post cholecystectomy for a CT of abdomen/pelvis. We can create a simple request to the clinical conflict detection safeguards like this: { "input_document":{ "document_id": "1", "document_text": "Exam: CT of the abdomen and pelvis\nClinical history: LLQ pain x 10 days, cholecystectomy 6 weeks ago\nFindings:\n- New calcified densities are seen in the nondistended gallbladder.\n- Heterogeneous enhancement of the liver with periportal edema. No suspicious hepatic masses are identified. Portal veins are patent.\n- Gastrointestinal Tract: No abnormal dilation or wall thickening. Diverticulosis.\n- Kidneys are normal in size.", "document_metadata":{ "document_type":"CLINICAL_REPORT", "date_of_service": "2024-10-10", "locale": "en-us" } }, "patient_metadata":{ "date_of_birth": "1944-01-01", "date_of_admission": "2024-10-10", "biological_sex": "FEMALE", "patient_id": "3" }, "request_id": "1" } The request provides the metadata for document text to allow for potential metadata conflict detections. The clinical conflict detection safeguard considers the document text together with the metadata and returns the following response: { "inferences": [ { "type": "ANATOMICAL_CONFLICT", "confidence_score": 1, "output_token": { "offsets": [ { "document_id": "1", "begin": 73, "end": 88 } ] }, "reference_token": { "offsets": [ { "document_id": "1", "begin": 153, "end": 165 }, { "document_id": "1", "begin": 166, "end": 177 } ] } } ], "status": "SUCCESS", "model_version": "1" } The safeguard picks up an anatomical conflict in the document text and provides text references using the offsets that make up the clinical conflict. In this case, it picks up an anatomical conflict between “cholecystectomy” (which means a gallbladder removal) and the finding of “New calcified densities are seen in the nondistended gallbladder”. The new densities in the gallbladder conflict with the statement that the gallbladder was removed 6 weeks prior. In practice The clinical conflicts detected by the safeguard can be leveraged in various stages of any report generation solution to build trust in its clinical consistency. Imagine a report generation application calling the clinical conflict detection safeguards to highlight potential inconsistencies to the HCP end user — as illustrated below — for review before signing off on the report. There are multiple conflicts in the example above, but the highlight shows inconsistently generated documentation. The normal statement about the lungs contradicts “small nodules in the left lung” findings, so the “Lungs are unremarkable” statement should have been removed. How to use Apply for private preview by filling in the form here. Once approved, users must provision a healthcare agent service resource in their Azure subscription to use the clinical safeguards API. When creating the healthcare agent service, make sure to set the plan to “Agent (C1)”. * This clinical safeguard does not define criteria for determining or identifying biological sex. Sex mismatch is based on the information in the metadata and the medical note. Please remember that neither clinical conflict detection nor healthcare agent service are made available, designed, intended or licensed to be used (1) as a medical device, (2) in the diagnosis, cure, mitigation, monitoring, treatment or prevention of a disease, condition or illness or as a substitute for professional medical advice. The use of these products are subject to the Microsoft Product Terms and other licensing agreements and to the Medical Device Disclaimer and documentation available here.Seamless and Secure Access to Digital Healthcare Records with Microsoft Entra Suite
Healthcare professionals who dedicate their skills to saving lives must also manage operational and safety challenges inherent to their roles. If you’re in charge of cybersecurity for a healthcare organization, you’re intimately familiar with the need to comply with government healthcare regulations that, for example, require securing access to systems that house patient health information (PHI), are used for overseeing controlled substances, or are necessary to enable the secure consumption of AI. Every year, hundreds of U.S. healthcare institutions fall victim to ransomware attacks, resulting in network closures and critical systems going offline, not to mention delayed medical operations and appointments.[i] Sensitive healthcare systems are very attractive targets for cyberattacks and internal misuse. Many cybercriminals gain initial access by compromising identities. Thus, the first line of defense against bad actors, whether internal or external, is to protect identities and to closely govern access permissions based on Zero Trust principles: Verify explicitly. Confirm that the individual signing into a system used to electronically prescribe controlled substances is actually the care provider they say they are. Use least privilege access. Limit a care giver’s access to systems they need to use for their job Assume breach. Discover unauthorized access and block it before an adversary can deploy ransomware. This blog is the first in a series of how Microsoft Entra Suite and the power of cloud-based security tools can protect access to sensitive healthcare assets while improving the user experience for care teams and staff. On-premises healthcare applications and cloud-based security Some of the most widely adopted healthcare applications, such as electronic health records (EHRs), began decades ago as on-premises solutions that used LDAP (Lightweight Directory Access Protocol) and Active Directory to authenticate users. As enterprises shifted from on-premises networks protected by firewalls at the network perimeter to hybrid environments that enabled “anytime, anywhere access,” these solutions became vulnerable to attackers who gained unauthorized access to hospital networks via the Internet. Cloud-based security tools introduced advantages such as centralized visibility and control, continuous monitoring, automated threat detection and response, and advanced threat intelligence based on trillions of security signals. Many existing healthcare applications, however, didn’t support the new protocols necessary to take advantage of all these benefits. Over the past several years, Microsoft has worked closely with software vendors to integrate their applications with our comprehensive identity security platform, Entra ID—which is built on modern open security standards. As a result, many healthcare applications, including the most widely deployed EHR systems, can now benefit from the advanced security capabilities available through Microsoft Entra Suite, including single sign-on (SSO), multifactor authentication (MFA), Conditional Access, Identity Protection, and Network Protection. Securing access to healthcare applications with Microsoft Entra Suite Healthcare organizations can standardize on Microsoft Entra to enable single sign-on (SSO) to their most commonly used Healthcare applications and resources, including the most widely used EHR vendors, whether they’re on-premises or in clouds from Microsoft, Amazon, Google, or Oracle. Care teams, who may use dozens of different applications during their workday, benefit from seamless and secure access to all their resources with Microsoft’s built-in advanced identity and network security controls. Not only does Microsoft Entra offer a holistic view of all users and their access permissions, but it also employs a centralized access policy engine, called Conditional Access, that combines trillions of signals from multiple sources, including identities and devices, to detect anomalous user behavior, assess risk, and make real-time access and data protection decisions that adhere to regulatory mandates and Zero Trust principles. In simple terms, this enables controls that verify who a user is and what device they are using – including when using kiosks, remote, or many-to-one workstations - to decide if it is safe to enable access. This ability to support modern authentication successfully maps the clinicians to their cloud identity and in turn, unlocks powerful user-based models for data protection with Microsoft Purview. With Microsoft Entra, healthcare organizations can enforce MFA at the application level for more granular control. They can strengthen security by requiring phishing-resistant authentication for staff, contractors, and partners, and by evaluating device health before authorizing access to resources. They can even require additional verification steps for IT admins performing sensitive actions. Moreover, Microsoft Entra ID Protection processes a vast array of signals to identify suspicious behaviors that may indicate an identity compromise. It can raise risk levels to trigger risk-based Conditional Access policies that protect users and resources from unauthorized access. For more details about risk detections in Entra ID Protection, visit our documentation. Seamless and secure access for healthcare professionals Integrating applications with Microsoft Entra ID makes it possible for healthcare professionals to work more securely with fewer disruptions when they access medical records and treat patients, even when they’re working offsite, such as at a patient’s home or as part of a mobile medical unit. Microsoft Entra supports the strict protocols for electronic prescribing of controlled substances (EPCS). The EPCS mandate requires that healthcare providers authenticate their identities before they can prescribe controlled substances electronically. This means that each provider must have a unique user identity that can be verified through secure methods such as Multi-Factor Authentication (MFA). This helps prevent unauthorized access and ensures that only authorized individuals can issue prescriptions. The Health Insurance Portability and Accountability Act (HIPAA) also has specific obligations for access and identity to ensure the security and privacy of protected health information (PHI). Microsoft Entra Suite has a variety of controls to help meet these obligations that we will explore in additional blogs. Phishing-resistant authentication methods, which rely on biometrics and hardware tokens, significantly reduce the risk of unauthorized access to sensitive systems and data. These methods, which include passkeys, are practically impossible for cybercriminals to compromise, unlike passwords or SMS-based MFA. By eliminating passwords altogether, healthcare providers can better protect patient data, reduce the risk of violating HIPAA regulations, and prevent cyber and ransomware attacks that could disrupt healthcare operations. You can experience the benefits of Microsoft Entra ID, MFA, Conditional Access, and Entra ID Protection as part of the Microsoft Entra Suite, the industry’s most comprehensive Zero Trust access solution for the workforce. The Microsoft Entra Suite provides everything needed to verify users, prevent overprivileged permissions, improve detections, and enforce granular access controls for all users and resources. Get started with the Microsoft Entra Suite with a free 90-day trial. For additional details, please reach out to your Microsoft Representative. Read more on this topic Electronic Prescriptions for Controlled Substances (EPCS) - Azure Compliance | Microsoft Learn Conditional Access adaptive session lifetime policies - Microsoft Entra ID | Microsoft Learn Overview of Microsoft Entra authentication strength - Microsoft Entra ID | Microsoft Learn Microsoft Entra ID Epic Connector – Edgile Use data connectors to import and archive third-party data in Microsoft 365 | Microsoft Learn Learn more about Microsoft Entra Prevent identity attacks, ensure least privilege access, unify access controls, and improve the experience for users with comprehensive identity and network access solutions across on-premises and clouds. Microsoft Entra News and Insights | Microsoft Security Blog Microsoft Entra blog | Tech Community Microsoft Entra documentation | Microsoft Learn Microsoft Entra discussions | Microsoft Community [i] Microsoft Corporation. Microsoft Digital Defense Report 2024: The foundations and new frontiers of cybersecurity. p.3. Microsoft, October 2024.Expanding Healthcare AI Models to New Modalities and Capabilities
At HIMSS 2025 Microsoft introduced several new breakthrough developments in multimodal healthcare AI to ensure that healthcare developers and researchers have the tools and expertise necessary to innovate the use of AI in building the healthcare AI applications of the future. Fine-tuning capability for MedImageInsight To further help health AI model developers fine-tune and customize our most popular state of the art multimodal foundation model, we’ve introduced a fine-tuning training capability for MedImageInsight in Azure Machine Learning. This enhancement lowers barriers to building and testing robust imaging classifiers and embedding models tailored to specific settings. This will enable building on the performance for 14 medical imaging modalities. Even with smaller, highly specialized datasets, developers can now adapt our pre-existing models to explore the capability to detect nuanced patterns in clinical imaging—such as subtle tumor markers or rare pathologies. By streamlining the process from model selection to testing and validation, Azure AI Foundry equips innovators with a nimble framework that accelerates discovery and shortens development cycles. Use Case: MedImageInsight in Action MedImageInsight is already showing the potential for impact in healthcare research. As Alan McMillan, Professor of Radiology at the University of Wisconsin School of Medicine and Public Health, shared: “We optimized the sensitivity and specificity of Microsoft’s MedImageInsight foundation model to accurately identify a significant portion of normal chest X-rays. This could allow radiologists to focus more on analyzing suspicious cases. In our evaluation, we found that at a threshold achieving 99% sensitivity for detecting abnormal chest X-rays, the model could reduce radiologists’ workload on normal cases by 42%.” This kind of workflow optimization would be a crucial step toward reducing radiologist burnout and ensuring that specialists can dedicate more time to the most clinically significant cases. Expanding the Health and Life Science AI Model Catalog Within our model catalog in Azure AI Foundry, we’re introducing several new and updated multimodal medical foundation models to our industry-leading ecosystem: Rad-Dino – A radiology-focused Chest X-Ray foundation model with over 1M downloads on Hugging Face and published in Nature Machine Intelligence. TamGen – A protein design model that employs a chemical language model to generate target-specific molecules, accelerating drug discovery and published in Nature. BioEmu-1 – A protein design model that generates diverse structural conformations of proteins, enhancing understanding of protein dynamics and function. Hist-ai – Cutting-edge pathology models ECG-FM – A foundation model for electrocardiogram (ECG) analysis. MedImageParse 3D – An adaptation of Microsoft’s MedImageParse model (now called MedImageParse 2D) for 3D medical imaging. Continuing our commitment to open-source AI, many of these models will also be available on Hugging Face, broadening accessibility and fostering a global community of researchers and innovators. The Microsoft healthcare AI models are intended for research and model development exploration. The models are not designed or intended to be deployed in clinical settings as-is nor for use in the diagnosis or treatment of any health or medical condition, and the individual models’ performances for such purposes have not been established. You bear sole responsibility and liability for any use of the healthcare AI models, including verification of outputs and incorporation into any product or service intended for a medical purpose or to inform clinical decision-making, compliance with applicable healthcare laws and regulations, and obtaining any necessary clearances or approvals. Partnering with Stanford: The First Public Healthcare AI Model Leaderboard Alongside these core technology offerings, we are partnering with Stanford University to support their launch of the first public Healthcare AI Model Leaderboard building upon real-world clinical data and use cases. This initiative shines a spotlight on these innovative models, comparing performance across a diverse range of benchmarks that go beyond multiple-choice clinical testing data and bring much-needed transparency to the healthcare AI space, allowing researchers to rapidly identify which models align best with their particular use cases. It also fosters a spirit of open, data-driven competition that we believe will push the industry toward increasingly accurate and reliable solutions. HIMMS 2025 We're thrilled by the enthusiastic response we received when showcasing these groundbreaking advancements at HIMSS 2025. It was inspiring to share detailed insights and see firsthand the excitement around innovations like the fine-tuning capabilities for MedImageInsight, the expanded catalog featuring new models such as Rad-Dino, TamGen, BioEmu-1, Hist-ai, ECG-FM, and MedImageParse 3D, as well as our pioneering partnership with Stanford University on the first public Healthcare AI Model Leaderboard. Our unified, secure, and high-performing platform, designed to power the next generation of healthcare, generated significant excitement and valuable conversations, reinforcing our commitment to accelerating innovation, improving patient outcomes, and driving healthcare forward. None of this would be possible without our incredible network of collaborators—research institutions, clinical partners, technology innovators, and dedicated healthcare practitioners. Your ongoing partnership continually fuels and challenges our progress. Thank you to everyone who joined us at HIMSS 2025—here's to a future shaped together, powered by AI-driven solutions that meaningfully reduce administrative burdens, enhance diagnostic precision, and ultimately save more lives.
Empowering radiologists with clinical guidance, quality standards, and scoring and assessments
In March 2024, we announced general availability of Radiology Insights, a new model built in Azure AI Health Insights service. This model uses radiology reports to surface relevant insights that can help radiologists enhance the quality of their reports. Today, we are announcing three new capabilities added to the Radiology Insights model: clinical guidance, quality measures, and scoring and assessment. Each plays a crucial role in enhancing clinicians’ decision-making, improving healthcare quality, and standardizing evaluations in medical imaging. Clinical Guidance Clinical guidance uses an evidence-based approach based on industry guidelines (ACR Guidelines [1,2,3,4] and Fleischner Society Guidelines [5] ) to help radiologists make the most appropriate recommendations and timing for future actions, such as specific follow-up studies. Clinical guidance extracts clinical finding information from the documentation, retrieving the necessary evidence to support proposed recommendations. If no follow-up action can be proposed, clinical guidance will identify what information is missing from the documentation. Figure 1 – Clinical Guidance: In this example, only two findings are considered and highlighted, each serving as a trigger for the pulmonary nodule clinical guideline. Radiology Insights model response: The first finding in the report proposes two candidate recommendations, including their modality and anatomy. The details of the first finding, LOBE and SIZE, are present in the report and extracted by the model. The second finding does not lead to a recommendation proposal due to missing information (Size). The first finding is ranked higher because of its greater information depth. Quality Measures Quality measures guidelines are an essential tool in healthcare to monitor the quality of care by providing frameworks (MIPS QCDR measures [6]) for measuring, reporting, and continuously improving healthcare practices. These measures are now supported by the Radiology Insights model to ensure healthcare providers meet established quality standards. The model captures quality measures criteria explicitly documented in the report and checks if all criteria necessary to meet quality standards are included. For each quality measure there are three possible outcomes: The report meets the required criteria: the performance is 'met'. The report does not meet all the criteria: the performance is 'not met'. The report does not meet all the required criteria: the model states an ‘exception’. For example, if a patient is allergic to Chlorhexidine, a substance used to meet quality measures for Central Venous Catheter (CVC) insertion, the standard procedure cannot be followed. The model will recognize this as an exception. When quality measure criteria are missing from the report, the documentation could be updated to include this information, or a review by a healthcare professional could be conducted to understand why these important criteria were not documented. Quality measures play a vital role in ensuring healthcare providers adhere to high standards, ultimately improving patient care. By following these measures, healthcare providers can avoid complications and deliver better outcomes for their patients. Figure 2 – Quality Measures: In this example, findings about a new nodule are considered and highlighted by the model. The model triggers the quality measure for incidental pulmonary nodule. Radiology Insights model response: For the quality measure ‘Incidental Pulmonary Nodule’, the performance is 'met'. The model surfaces the criteria for having a follow-up recommendation in the report, which is the only criterion required for compliance. Scoring and Assessment Scoring and assessment systems [7,8] are used in medical imaging and diagnostics to help standardize the evaluation and reporting of findings and provide a structured approach to interpreting imaging studies, assessing disease risk, and guiding clinical management. The Radiology Insights model surfaces and highlights scoring and assessments with their classifications or values that were explicitly documented by radiologists in their reports. In the sample below, the model identifies two assessments with its values: the ASCVD (Atherosclerotic Cardiovascular Disease) risk and the Agatston Score, which measures the amount of calcium in the coronary arteries. In this example, there is a 17.6% chance of experiencing a cardiovascular event in the next 10 years (ASCVD) and Agatston Score of 0 suggests low short-term risk of heart attack. Figure 3 – Scoring and Assessment: In this example, two assessments, ASCVD and Agatston score, are reported with their values. Radiology Insights model response: The model surfaces two scoring and assessment instances, one of category ASCVD Risk with a value of 17.6% and one of category Calcium Score with value 0. Do more with your data with Microsoft Cloud for Healthcare With Azure AI Health Insights, health organizations can transform their patient experience, discover new insights with the power of machine learning and AI, and manage protected health information (PHI) data with confidence. Enable your data for the future of healthcare innovation with Microsoft Cloud for Healthcare. We look forward to working with you as you build the future of health. Learn more about Azure AI Health Insights and how to start working with this Azure resource in the Azure AI Health Insights documentation Learn more about Radiology Insights Important Radiology Insights is a capability provided “AS IS” and “WITH ALL FAULTS.” Radiology Insights isn’t intended or made available for use as a medical device, clinical support, diagnostic tool, or other technology intended to be used in diagnosis, cure, mitigation, treatment, or prevention of disease or other conditions, and no license or right is granted by Microsoft to use this capability for such purposes. This capability isn’t designed or intended to be implemented or deployed as a substitute for professional medical advice or healthcare opinion, diagnosis, treatment, or the clinical judgment of a healthcare professional, and should not be used as such. The customer is solely responsible for any use of the Radiology Insights model.Implementing Disaster Recovery for Azure App Service Web Applications
Starting March 31, 2025, Microsoft will no longer automatically place Azure App Service web applications in disaster recovery mode in the event of a regional disaster. This change emphasizes the importance of implementing robust disaster recovery (DR) strategies to ensure the continuity and resilience of your web applications. Here’s what you need to know and how you can prepare. Understanding the Change Azure App Service has been a reliable platform for hosting web applications, REST APIs, and mobile backends, offering features like load balancing, autoscaling, and automated management. However, beginning March 31, 2025, in the event of a regional disaster, Azure will not automatically place your web applications in disaster recovery mode. This means that you, as a developer or IT professional, need to proactively implement disaster recovery techniques to safeguard your applications and data. Why This Matters Disasters, whether natural or technical, can strike without warning, potentially causing significant downtime and data loss. By taking control of your disaster recovery strategy, you can minimize the impact of such events on your business operations. Implementing a robust DR plan ensures that your applications remain available and your data remains intact, even in the face of regional outages. Common Disaster Recovery Techniques To prepare for this change, consider the following commonly used disaster recovery techniques: Multi-Region Deployment: Deploy your web applications across multiple Azure regions. This approach ensures that if one region goes down, your application can continue to run in another region. You can use Azure Traffic Manager or Azure Front Door to route traffic to the healthy region. Multi-region load balancing with Traffic Manager and Application Gateway Highly available multi-region web app Regular Backups: Implement regular backups of your application data and configurations. Azure App Service provides built-in backup and restore capabilities that you can schedule to run automatically. Back up an app in App Service How to automatically backup App Service & Function App configurations Active-Active or Active-Passive Configuration: Set up your applications in an active-active or active-passive configuration. In an active-active setup, both regions handle traffic simultaneously, providing high availability. In an active-passive setup, the secondary region remains on standby and takes over only if the primary region fails. About active-active VPN gateways Design highly available gateway connectivity Automated Failover: Use automated failover mechanisms to switch traffic to a secondary region seamlessly. This can be achieved using Azure Site Recovery or custom scripts that detect failures and initiate failover processes. Add Azure Automation runbooks to Site Recovery recovery plans Create and customize recovery plans in Azure Site Recovery Monitoring and Alerts: Implement comprehensive monitoring and alerting to detect issues early and respond promptly. Azure Monitor and Application Insights can help you track the health and performance of your applications. Overview of Azure Monitor alerts Application Insights OpenTelemetry overview Steps to Implement a Disaster Recovery Plan Assess Your Current Setup: Identify all the resources your application depends on, including databases, storage accounts, and networking components. Choose a DR Strategy: Based on your business requirements, choose a suitable disaster recovery strategy (e.g., multi-region deployment, active-active configuration). Configure Backups: Set up regular backups for your application data and configurations. Test Your DR Plan: Regularly test your disaster recovery plan to ensure it works as expected. Simulate failover scenarios to validate that your applications can recover quickly. Document and Train: Document your disaster recovery procedures and train your team to execute them effectively. Conclusion While the upcoming change in Azure App Service’s disaster recovery policy may seem daunting, it also presents an opportunity to enhance the resilience of your web applications. By implementing robust disaster recovery techniques, you can ensure that your applications remain available and your data remains secure, no matter what challenges come your way. Start planning today to stay ahead of the curve and keep your applications running smoothly. Recover from region-wide failure - Azure App Service Reliability in Azure App Service Multi-Region App Service App Approaches for Disaster Recovery Feel free to share your thoughts or ask questions in the comments below. Let's build a resilient future together! 🚀Empowering Employees with Purpose-Built Agents Using Agent Builder in Microsoft 365 Copilot Chat
In today's fast-paced work environment, empowering employees with the right tools can make all the difference in productivity and job satisfaction. One innovative solution is the use of purpose-built agents through Microsoft 365 Copilot Chat and Microsoft Teams. These agents are designed to streamline workflows, provide instant assistance, and enhance overall efficiency.
