How To

Microsoft Defender Experts for XDR recognized in the latest MITRE Engenuity ATT&CK® Evaluation for Managed Services

Microsoft Defender Experts for XDR demonstrated excellent managed extended detection and response (MXDR) by unifying our human-driven services and Microsoft Defender XDR in the MITRE Engenuity ATT&CK® Evaluations: Managed Services menuPass + ALPHV BlackCat.    Defender Experts for XDR offers a range of capabilities:  Managed detection and response: Let our expert analysts manage your Microsoft Defender […]

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Microsoft Incident Response tips for managing a mass password reset

Explore how effective incident response helps organizations detect, address, and stop cyberattacks Learn more As part of any robust incident response plan, organizations often work through potential security weaknesses by responding to hypothetical cyberthreats. In this blog post, we’ll imagine a scenario in which a threat actor uses malware to infect the network, moving laterally

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How to achieve cloud-native endpoint management with Microsoft Intune

This is the final blog post in our series highlighting the increasing benefits of becoming fully cloud-native in endpoint management with Microsoft Intune. In our first post, we talked about why more of our customers are migrating to cloud-native endpoint management. Our second post presented a three-phase model for how customers can go cloud-native with

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Debugging Playbooks

This article assumes knowledge of how to create Microsoft Sentinel Analytic Rules, Automation Rules, and playbooks. Create the playbook You will want to create the playbook first, since one will need to be created before it can be used in the Automation rule.   In the example shown below, the playbook will iterate through all the

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How to identify the most impactful GenAI use case

Generative AI solutions are very transformational for businesses nowadays. Organisations have access to tools and AI capabilities that will enable them to generate immense value for the business. At the same time though, some businesses are struggling to identify the areas they should invest in AI. Most of the AI engagements fail because there isn’t

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Maximizing Performance: Leveraging PTUs with Client Retry Mechanisms in LLM Applications

Introduction Achieving maximum performance in PTU environments requires sophisticated handling of API interactions, especially when dealing with rate limits (429 errors). This blog post introduces a technique that exemplifies how to maintain optimal performance using Azure OpenAI’s API by intelligently managing rate limits. This method strategically switches between PTU and Standard deployments, enhancing throughput and

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Cloud security posture and contextualization across cloud boundaries from a single dashboard

Introduction:   Have you ever found yourself in a situation where you wanted to prioritize the riskiest misconfigurations on cloud workloads across Azure, AWS, and GCP? Have you ever wondered how to implement a unified dashboard for cloud security posture across a multicloud environment?   This article covers how you can achieve these scenarios by using

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AI-as-a-Service: Architecting GenAI Application Governance with Azure API Management and Fabric

The past year has seen explosive growth for Azure OpenAI and large language models in general. With models reliant on a token-based approach for processing requests, ensuring prompt engineering is being done correctly, tracking what models and api‘s are being used, load balancing across multiple instances, and creating chargeback models has become increasingly important. The

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Build Your Dream With Autogen

  The motivation – Is it possible to solve multi step tasks?   100 years ago, a guy called Francis Galton, ran an experiment, he asked 787 villagers to guess the weight of an Ox. Surprisingly none of them answered correctly, but when he averaged the different answers, the average was really close, how close? 10

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Build your dream team with Autogen

  The motivation – Is it possible to solve multi step tasks?   100 years ago, a guy called Francis Galton, ran an experiment, he asked 787 villagers to guess the weight of an Ox. Surprisingly none of them answered correctly, but when he averaged the different answers, the average was really close, how close? 10

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