Model Mondays S2E12: Models & Observability
1. Weekly Highlights This week’s top news in the Azure AI ecosystem included: GPT Real Time (GA): Azure AI Foundry now offers GPT Real Time (GA)—lifelike voices, improved instruction following, audio fidelity, and function calling, with support for image context and lower pricing. Read the announcement and check out the model card for more details. Azure AI Translator API (Public Preview): Choose between fast Neural Machine Translation (NMT) or nuanced LLM-powered translations, with real-time flexibility for multilingual workflows. Read the announcement then check out the Azure AI Translator documentation for more details. Azure AI Foundry Agents Learning Plan: Build agents with autonomous goal pursuit, memory, collaboration, and deep fine-tuning (SFT, RFT, DPO) - on Azure AI Foundry. Read the announcement what Agentic AI involves - then follow this comprehensive learning plan with step-by-step guidance. CalcLM Agent Grid (Azure AI Foundry Labs): Project CalcLM: Agent Grid is a prototype and open-source experiment that illustrates how agents might live in a grid-like surface (like Excel). It's formula-first and lightweight - defining agentic workflows like calculations. Try the prototype and visit Foundry Labs to learn more. Agent Factory Blog: Observability in Agentic AI: Agentic AI tools and workflows are gaining rapid adoption in the enterprise. But delivering safe, reliable and performant agents requires foundation support for Observability. Read the 6-part Agent Factory series and check out the Top 5 agent observability best practices for reliable AI blog post for more details. 2. Spotlight On: Observability in Azure AI Foundry This week’s spotlight featured a deep dive and demo by Han Che (Senior PM, Core AI/ Microsoft ), showing observability end-to-end for agent workflows. Why Observability? Ensures AI quality, performance, and safety throughout the development lifecycle. Enables monitoring, root cause analysis, optimization, and governance for agents and models. Key Features & Demos: Development Lifecycle: Leaderboard: Pick the best model for your agent with real-time evaluation. Playground: Chat and prototype agents, view instant quality and safety metrics. Evaluators: Assess quality, risk, safety, intent resolution, tool accuracy, code vulnerability, and custom metrics. Governance: Integrate with partners like Cradle AI and SideDot for policy mapping and evidence archiving. Red Teaming Agent: Automatically test for vulnerabilities and unsafe behavior. CI/CD Integration: Automate evaluation in GitHub Actions and Azure DevOps pipelines. Azure DevOps GitHub Actions Monitoring Dashboard: Resource usage, application analytics, input/output tokens, request latency, cost breakdown, and evaluation scores. Azure Cost Management SDKs & Local Evaluation: Run evaluations locally or in the cloud with the Azure AI Evaluation SDK. Demo Highlights: Chat with a travel planning agent, view run metrics and tool usage. Drill into run details, debugging, and real-time safety/quality scores. Configure and run large-scale agent evaluations in CI/CD pipelines. Compare agents, review statistical analysis, and monitor in production dashboards 3. Customer Story: Saifr Saifr is a RegTech company that uses artificial intelligence to streamline compliance for marketing, communications, and creative teams in regulated industries. Incubated at Fidelity Labs (Fidelity Investments’ innovation arm), Saifr helps enterprises create, review, and approve content that meets regulatory standards—faster and with less manual effort. What Saifr Offers AI-Powered Compliance: Saifr’s platform leverages proprietary AI models trained on decades of regulatory expertise to automatically detect potential compliance risks in text, images, audio, and video. Automated Guardrails: The solution flags risky or non-compliant language, suggests compliant alternatives, and provides explanations—all in real time. Workflow Integration: Saifr seamlessly integrates with enterprise content creation and approval workflows, including cloud platforms and agentic AI systems like Azure AI Foundry. Multimodal Support: Goes beyond text to check images, videos, and audio for compliance risks, supporting modern marketing and communications teams. 4. Key Takeaways Observability is Essential: Azure AI Foundry offers complete monitoring, evaluation, tracing, and governance for agentic AI—making production safe, reliable, and compliant. Built-In Evaluation and Red Teaming: Use leaderboards, evaluators, and red teaming agents to assess and continuously improve model safety and quality. CI/CD and Dashboard Integration: Automate evaluations in GitHub Actions or Azure DevOps, then monitor and optimize agents in production with detailed dashboards. Compliance Made Easy: Safer’s agents and models help financial services and regulated industries proactively meet compliance standards for content and communications. Sharda's Tips: How I Wrote This Blog I focus on organizing highlights, summarizing customer stories, and linking to official Microsoft docs and real working resources. For this recap, I explored the Azure AI Foundry Observability docs, tested CI/CD pipeline integration, and watched the customer demo to share best practices for regulated industries. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E12 based on the transcript and episode details. Focus on observability, agent dashboards, CI/CD, compliance, and customer stories. Add correct, working Microsoft links!" Coming Up Next Week Next week: Open Source Models! Join us for the final episode with Hugging Face VP of Product, live demos, and open model workflows. Register For The Livestream – Sep 15, 2025 About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.A New Platform Management Group & Subscription for Security in Azure landing zone (ALZ)
At the start of 2025, during the January 2025 ALZ Community Call, we asked everyone for their feedback, via these discussions on our GitHub repo: 1898 & 1978 , on the future of Microsoft Sentinel in the Azure landing zone (ALZ) architecture as we were receiving feedback that it needed some changes and additional clarity from what ALZ was deploying and advising then. We have now worked with customers, partners, and internal teams to figure out what we should update in ALZ around Microsoft Sentinel and Security tooling and have updated the ALZ conceptual architecture to show this. What did ALZ advise and deploy before, by default? Prior to these updates ALZ advised the following: The central Log Analytics Workspace (LAW) in the Management Subscription should Be used to capture all logs, including security/SIEM logs The Microsoft Sentinel solution (called Security) should be installed upon this LAW also And in the accelerators and tooling it deployed, by default: The central Log Analytics Workspace (LAW) in the Management Subscription with the Microsoft Sentinel solution installed Microsoft Sentinel had no additional configuration apart from being installed as a solution on the central LAW What are the changes being made to ALZ from today? Based on the feedback from the GitHub discussions and working with customers, partners and internal teams we are making the following changes: A new dedicated Security Management Group beneath the Platform Management Group A new dedicated Security Subscription placed in the new Security Management Group Nothing will be deployed into this subscription by ALZ by default. This allows: Customers & partners to deploy and manage the Microsoft Sentinel deployment how they wish to The 31-day 10GB/day free trial can be started when the customer or partner is ready to utilise it No longer deploy the Microsoft Sentinel solution (called Security) on the central LAW in the Management Subscription This allows for separating of operational/platform logs from security logs, as per considerations documented in Design a Log Analytics workspace architecture The changes have only been made to our ALZ CAF/MS Learn guidance as of now, and the changes to the accelerators and implementation tools will be made over the coming months 👍 These changes can be seen in the latest ALZ conceptual architecture snippet below The full ALZ conceptual architecture can be seen here on MS Learn. You can also download a Visio or PDF copy of all the ALZ diagrams. What if we have already deployed ALZ? If you have already deployed ALZ and haven't tailored the ALZ default Management Group hierarchy to create a Security Management Group then you can now review and decide whether this is something you'd like to create and align with. While not mandatory, this enhancement to the ALZ architecture is recommended for new customers. The previous approach remains valid; however, feedback from customers, partners, and internal teams indicates that using a dedicated Microsoft Sentinel and Log Analytics Workspace within a separate security-focused Subscription and Management Group is a common real-world practice. To reflect these real-world implementations and feedback, we’re evolving the ALZ conceptual architecture accordingly 👍 Closing We hope you benefit from this update to the ALZ conceptual architecture. As always we welcome any feedback via our GitHub Issues
GA: Enhanced Audit in Azure Security Baseline for Linux
We’re thrilled to announce the General Availability (GA) of the Enhanced Azure Security Baseline for Linux—a major milestone in cloud-native security and compliance. This release brings powerful, audit-only capabilities to over 1.6 million Linux devices across all Azure regions, helping enterprise customers and IT administrators monitor and maintain secure configurations at scale. What Is the Azure Security Baseline for Linux? The Azure Security Baseline for Linux is a set of pre-configured security recommendations delivered through Azure Policy and Azure Machine Configuration. It enables organizations to continuously audit Linux virtual machines and Arc-enabled servers against industry-standard benchmarks—without enforcing changes or triggering auto-remediation. This GA release focuses on enhanced audit capabilities, giving teams deep visibility into configuration drift and compliance gaps across their Linux estate. For our remediation experience, there is a limited public preview available here: What is the Azure security baseline for Linux? | Microsoft Learn Why Enhanced Audit Matters In today’s hybrid environments, maintaining compliance across diverse Linux distributions is a challenge. The enhanced audit mode provides: Granular insights into each configuration check Industry aligned benchmark for standardized security posture Detailed rule-level reporting with evidence and context Scalable deployment across Azure and Arc-enabled machines Whether you're preparing for an audit, hardening your infrastructure, or simply tracking configuration drift, enhanced audit gives you the clarity and control you need—without enforcing changes. Key Features at GA ✅ Broad Linux Distribution Support 📘 Full distro list: Supported Client Types 🔍 Industry-Aligned Audit Checks The baseline audits over 200+ security controls per machine, aligned to industry benchmarks such as CIS. These checks cover: OS hardening Network and firewall configuration SSH and remote access settings Logging and auditing Kernel parameters and system services Each finding includes a description and the actual configuration state—making it easy to understand and act on. 🌐 Hybrid Cloud Coverage The baseline works across: Azure virtual machines Arc-enabled servers (on-premises or other clouds) This means you can apply a consistent compliance standard across your entire Linux estate—whether it’s in Azure, on-prem, or multi-cloud. 🧠 Powered by Azure OSConfig The audit engine is built on the open-source Azure OSConfig framework, which performs Linux-native checks with minimal performance impact. OSConfig is modular, transparent, and optimized for scale—giving you confidence in the accuracy of audit results. 📊 Enterprise-Scale Reporting Audit results are surfaced in: Azure Policy compliance dashboard Azure Resource Graph Explorer Microsoft Defender for Cloud (Recommendations view) You can query, export, and visualize compliance data across thousands of machines—making it easy to track progress and share insights with stakeholders. 💰 Cost There’s no premium SKU or license required to use the audit capabilities with charges only applying to the Azure Arc managed workloads hosted on-premises or other CSP environments—making it easy to adopt across your environment. How to Get Started Review the Quickstart Guide 📘 Quickstart: Audit Azure Security Baseline for Linux Assign the Built-In Policy Search for “Linux machines should meet requirements for the Azure compute security baseline” in Azure Policy and assign it to your desired scope. Monitor Compliance Use Azure Policy and Resource Graph to track audit results and identify non-compliant machines. Plan Remediation While this release does not include auto-remediation, the detailed audit findings make it easy to plan manual or scripted fixes. Final Thoughts This GA release marks a major step forward in securing Linux workloads at scale. With enhanced audit now available, enterprise teams can: Improve visibility into Linux security posture Align with industry benchmarks Streamline compliance reporting Reduce risk across cloud and hybrid environmentsPrevent Accidental Deletion of an Instance in Azure Postgres
Did you know that accidental deletion of database servers is a leading source of support tickets? Read this blog post to learn how you can safeguard your Azure Database for PostgreSQL Flexible Server instances using ARM’s CanNotDelete lock — an easy best-practice that helps prevent accidental deletions while keeping regular operations seamless. 🌐 Prevent Accidental Deletion of an Instance in Azure PostgresDesigning for Certainty: How Azure Capacity Reservations Safeguard Mission‑Critical Workloads
Why capacity reservations matter now Cloud isn’t running out of metal, but demand is compounding and often spikes. Resource strain shows up in specific regions, zones, and VM SKUs, especially for popular CPU families, memory-optimized sizes, and anything involving GPUs. Seasonal events (retail peaks), regulatory cutovers, emergency response, and bursty AI pipelines can trigger sudden surges. Even with healthy regional capacity, a single zone or a specific SKU can be tight. Capacity reservations acknowledge this reality and make it designable instead of probabilistic. Root reality: Capacity is finite at the SKU-in-zone granularity, and demand arrives in waves. Risk profile: The risk is not “no capacity in the cloud,” but “no capacity for this exact size in this exact place at this exact moment.” Strategic move: Reserve what matters, where it matters, before you need it. What capacity means in practice Think of three dimensions: region, zone, and SKU. Your workload’s SLO ties to all three. Region: The biggest pool of resources. It gives you flexibility but doesn’t guarantee availability in a specific zone. Zone: This is where fault isolation happens and where you’ll often feel the pinch first when demand spikes. SKU: The specific type of machine you’re asking for. This is usually the tightest constraint, especially for popular sizes like Dv5, Ev5, or anything with GPUs. Azure Capacity Reservations let you lock capacity for a specific VM size at the regional or zonal scope and then place VMs/scale sets into that reservation. Pay‑as‑you‑go vs capacity reservations vs reserved instances Attribute Pay‑as‑you‑go Capacity Reservations Reserved Instances Primary purpose Flexibility, no commitment Guarantee availability for a VM size Reduce price for steady usage What it guarantees Nothing beyond current availability Capacity in region/zone for N of a SKU Discount on matching usage (1‑ or 3‑year term) Scope Region/zone at runtime, best‑effort Bound to region or specific zone Billing benefit across scope rules Commitment None Active while you keep it (on‑demand) Term commitment (1 or 3 years) Key clarifications Capacity reservations ≠ discount tool: They exist to secure availability. You pay while the reservation is active (even if idle) because Azure is holding that capacity for you. Reserved Instances ≠ capacity guarantee: They reduce the rate you pay when you run matching VMs, but they don’t hold hardware for you. Together: Use Capacity Reservations to ensure the VMs can run; use Reserved Instances to lower the cost of the runtime those VMs consume. This is universal, not just Azure Every major cloud faces the same physics: finite hardware, localized spikes, SKU-specific constraints, and growth in high-demand families (especially GPUs). AWS offers On‑Demand Capacity Reservations; Google Cloud offers zonal reservations. The names differ; the pattern and the need are the same. If your architecture depends on “must run here, as this size, and right now,” you either design for capacity or accept availability risk. When mission‑critical means “reserve it” If failure to acquire capacity breaks your SLO, treat capacity as a dependency to engineer, not a variable to assume. High-stakes cutovers and events: Examples: Black Friday, tax deadlines, trading close, clinical batch windows. Action: Pre‑reserve the exact SKU in the exact zones for the surge window. HA across zones: Goal: Survive a zone failure by scaling in active zones. Action: Consider keeping extra capacity in each zone based on your failover plan, whether that’s N+1 or matching peak load, depending on active/active vs. active/passive. Change windows that deallocate/recreate: Risk: If a VM is deallocated during maintenance, it might not get the same placement when restarted. Action: Associate VMs/VMSS with a capacity reservation group before deallocation. Fixed‑SKU dependencies: Signal: Performance needs, licensing rules, or hardware accelerators that lock you into a specific VM family. Action: Reserve by SKU. If possible, define fallback SKUs and split reservations across them. Regulated or latency‑sensitive workloads: Constraint: Must run in a specific zone or region due to compliance or latency. Action: Prefer zonal reservations to control both locality and availability. How reserved instances complement capacity reservations Two-layer strategy: Layer 1: Availability: Capacity reservations ensure your compute can be placed when needed. Layer 2: Economics: Reserved Instances (or Savings Plans) apply a pricing benefit to the steady‑state hours you actually run. Practical pairing: Steady base load: Cover with 1/3‑year Reserved Instances for maximum savings. Critical surge headroom: Hold with Capacity Reservations; if the surge is predictable, you can still layer partial RI coverage aligned to expected utilization. Dynamic burst: Leave as pay‑as‑you‑go or use short‑lived reservations during known windows. FinOps hygiene: Coverage ratios: Track RI coverage and capacity reservation utilization separately. Rightsizing: Align reservations to the SKU mix you truly run; shift or cancel idle capacity reservations quickly. Chargeback: Attribute the cost of “insurance” (capacity) to the workloads that require the SLO, separate from the cost of “fuel” (compute hours). Conclusion In today’s cloud landscape, resilience isn’t just redundancy; it’s about assured access to the exact resources your workload demands. Capacity Reservations remove uncertainty by guaranteeing placement, while Reserved Instances drive cost efficiency for predictable use. Together, they form a strategic duo that keeps mission‑critical services running smoothly under any demand surge. Build with both in mind, and you turn capacity from a risk into a controlled asset.
Mine your Azure backup data, it could save you 💰💡
Your data has a story to tell. Mine it, decipher it, and turn it into actionable outcomes. 📊🔍 Azure backups can become orphaned in several ways (I'll dive into that in a future post). But here’s a key point: orphaned doesn’t always mean useless, hence the word “Potential” in the title of my Power BI report. Each workload needs to be assessed individually. If a backup is no longer needed, you might be paying for it - unnecessarily and unknowingly. 🕵️♂️💸 To uncover these hidden costs, I combined data from the Azure Business Continuity Center with a PowerShell script I wrote to extract LastBackupTime and other metadata. This forms the foundation of my report, helping visualize and track backup usage over time. This approach helped me identify forgotten one-time backups, VMs deleted without stopping the backup, workloads excluded due to policy changes, and backups left behind after resource migrations. If you delete unneeded backups and have soft-delete enabled, the backup size drops to zero and Azure stops charging for it. ✅🧹 💡 Do your Azure backups have their own untold story to tell? 📸 Here's a snapshot of my report that helped me uncover these insights 👇Sensitivity Auto-labelling via Document Property
Why is this needed? Sensitivity labels are generally relevant within an organisation only. If a file is labelled within one environment and then moved to another environment, sensitivity label content markings may be visible, but by default, the applied sensitivity label will not be understood. This can lead to scenarios where information that has been generated externally is not adequately protected. My favourite analogy for these scenarios is to consider the parallels between receiving sensitive information and unpacking groceries. When unpacking groceries, you might sit your grocery bag on a counter or on the floor next to the pantry. You’ll likely then unpack each item, take a look at it and then decide where to place it. Without looking at an item to determine its correct location, you might place it in the wrong location. Porridge might be safe from the kids on the bottom shelf. If you place items that need to be protected, such as chocolate, on the bottom shelf, it’s not likely to last very long. So, I affectionately refer to information that hasn’t been evaluated as ‘porridge’, as until it has been checked, it will end up on the bottom shelf of the pantry where it is quite accessible. Label-based security controls, such as Data Loss Prevention (DLP) policies using conditions of ‘content contains sensitivity label’ will not apply to these items. To ensure the security of any contained sensitive information, we should look for potential clues to its sensitivity and then utilize these clues to ensure that the contained information is adequately protected - We take a closer look at the ‘porridge’, determine whether it’s an item that needs protection and if so, move it to a higher shelf in the pantry so that it’s out of reach for the kids. Effective use of Purview revolves around the use of ‘know your data’ strategies. We should be using as many methods as possible to try to determine the sensitivity of items. This can include the use of Sensitive Information Types (SITs) containing keyword or pattern-based classifiers, trainable classifiers, Exact Data Match, Document fingerprinting, etc. Matching items via SITs present in the items content can be problematic due to false positives. Keywords like ‘Sensitive’ or ‘Protected’ may be mentioned out of context, such as when referring to a classification or an environment. When classifications have been stamped via a property, it allows us to match via context rather than content. We don’t need to guess at an item’s sensitivity if another system has already established what the item’s classification is. These methods are much less prone to false positives. Why isn’t everyone doing this? Document properties are often not considered in Purview deployments. SharePoint metadata management seems to be a dying artform and most compliance or security resources completing Purview configurations don’t have this skill set. There’s also a lack of understanding of the relevance of checking for item properties. Microsoft haven’t helped as the documentation in this space is somewhat lacking and needs to be unpicked via some aligning DLP guidance (Create a DLP policy to protect documents with FCI or other properties). Many of these configurations will also be tied to regional requirements. Document properties being used by systems where I’m from, in Australia, will likely be very different to those used in other parts of the world. In the following sections, we’ll take a look at applicable use cases and walk through how to enable these configurations. Scenarios for use Labelling via document property isn’t for everyone. If your organisation is new to classification or you don’t have external partners that you collaborate with at higher sensitivity levels, then this likely isn’t for you. For those that collaborate heavily and have a shared classification framework, as is often seen across government, this is a must! This approach will also be highly relevant to multi-tenant organisations or conglomerates where information is regularly shared between environments. The following scenarios are examples of where this configuration will be relevant: 1. Migrating from 3 rd party classification tools If an item has been previously stamped by a 3 rd party classification tool, then evaluating its applied document properties will provide a clear picture of its security classification. These properties can then be used in service-based auto-labelling policies to effectively transition items from 3 rd party tools to Microsoft Purview sensitivity labels. As labels are applied to items, they will be brought into scope of label-based controls. 2. Detecting data spill Data spill is a term that is used to define situations where information that is of a higher than permitted security classification land in an environment. Consider a Microsoft 365 tenant that is approved for the storage of Official information but Top Secret files are uploaded to it. Document properties that align with higher than permitted classifications provide us with an almost guaranteed method of identifying spilled items. Pairing this document property with an auto-labelling policy allows for the application of encryption to lock unauthorized users out of the items. Tools like Content Explorer and eDiscovery can then be used to easily perform cleanup activities. If using document properties and auto-labelling for this purpose, keep in mind that you’ll need to create sensitivity labels for higher than permitted classifications in order to catch spilled items. These labels won’t impact usability as you won’t publish them to users. You will, however, need to publish them to a single user or break glass account so that they’re not ignored by auto-labelling. 3. Blocking access by AI tools If your organization was concerned about items with certain properties applied being accessed by generative AI tools, such as Copilot, you could use Auto-labelling to apply a sensitivity label that restricts EXTRACT permissions. You can find some information on this at Microsoft 365 Copilot data protection architecture | Microsoft Learn. This should be relevant for spilled data, but might also be useful in situations where there are certain records that have been marked via properties and which should not be Copilot accessible. 4. External Microsoft Purview Configurations Sensitivity labels are relevant internally only. A label, in its raw form, is essentially a piece of metadata with an ID (or GUID) that we stamp on pieces of information. These GUIDs are understood by your tenant only. If an item marked with a GUID shows up in another Microsoft 365 tenant, the GUID won’t correspond with any of that tenant’s labels or label-based controls. The art in Microsoft Purview lies in interpreting the sensitivity of items based on content markings and other identifiers, so that data security can be maintained. Document properties applied by Purview, such as ClassificationContentMarkingHeaderText are not relevant to a specific tenant, which makes them portable. We can use these properties to help maintain classifications as items move between environments. 5. Utilizing metadata applied by Records Management solutions Some EDRMS, Records or Content Management solutions will apply properties to items. If an item has been previously managed and then stamped with properties, potentially including a security classification, via one of these systems, we could use this information to inform sensitivity label application. 6. 3 rd party classification tools used externally Even if your organisation hasn’t been using 3rd party classification tools, you should consider that partner organisations, such as other Government departments, might be. Evaluating the properties applied by external organisations to items that you receive will allow you to extend protections to these items. If classification tools like Janus or Titus are used in your geography/industry, then you may want to consider checking for their properties. Regarding the use of auto-classification tools Some organisations, particularly those in Government, will have organisational policies that prevent the use of automatic classification capabilities. These policies are intended to ensure that each item is assessed by an actual person for risk of disclosure rather than via an automated service that could be prone to error. However, when auto-labelling is used to interpret and honour existing classifications, we are lowering rather than raising the risk profile. If the item’s existing classification (applied via property) is ignored, the item will be treated as porridge and is likely to be at risk. If auto-labelling is able to identify a high-risk item and apply the relevant label, it will then be within scope of Purview’s data security controls, including label-based DLP, groups and sites data out of place alerting, and potentially even item encryption. The outcome is that, through the use of auto-labelling, we are able to significantly reduce risk of inappropriate or unintended disclosure. Configuration Process Setting up document property-based auto-labelling is fairly straightforward. We need to setup a managed property and then utilize it an auto-labelling policy. Below, I've split this process into 6 steps: Step 1 – Prepare your files In order to make use of document properties, an item with the properties applied will first need to be indexed by SharePoint. SharePoint will record the properties as ‘crawled properties’, which we’ll then need to convert into ‘managed properties’ to make them useful. If you already have items with the relevant properties stored in SharePoint, then they are likely already indexed. If not, you’ll need to upload or create an item or items with the properties applied. For testing, you’ll want to create a file with each property/value combination so that you can confirm that your auto-labelling policies are all working correctly. This could require quite a few files depending on the number of properties you’re looking for. To kick off your crawled property generation though, you could create or upload a single file with the correct properties applied. For example: In the above, I’ve created properties for ClassificationContentMarkingHeaderText and ClassificationContentMarkingFooterText, which you’ll often see applied by Purview when an item has a sensitivity label content marking applied to it. I’ve also included properties to help identify items classified via JanusSeal, Titus and Objective. Step 2 – Index the files After creating or uploading your file, we then need SharePoint to index it. This should happen fairly quickly depending on the size of your environment. I'd expect to wait sometime between 10 minutes and 24 hrs. If you're not in a hurry, then I'd recommend just checking back the next day. You'll know when this has been completed when you head into SharePoint Admin > Search > Managed Search Schema > Crawled Properties and can find your newly indexed properties: Step 3 – Configure managed properties Next, the properties need to be configured as managed properties. To do this, go to SharePoint Admin > More features > Search > Managed Search Schema > Managed Properties. Create a new managed property and give it a name. Note that there are some character restrictions in naming, but you should be able to get it close to your document property name. Set the property’s type to text, select queryable and retrievable. Under ‘mappings to crawled properties’, choose add mapping, search for and select the property indexed from the file property. Note that the crawled property will have the same name as your document property, so there’s no need to browse through all of them: Repeat this so that you have a managed property for each document property that you want to look for. Step 4 – Configure Auto-labelling policies Next up, create some auto-labelling policies. You’ll need one for each label that you want to apply, not one per property as you can check multiple properties within the one auto-labelling policy. - From within Purview, head to Information Protection > Policies > Auto-labelling policies. - Create a new policy using the custom policy template. - Give your policy an appropriate name (e.g. Label PROTECTED via property). - Select the label that you want to apply (e.g. PROTECTED). - Select SharePoint based services (SharePoint and OneDrive). - Name your auto-labelling rules appropriately (e.g. SPO – Contains PROTECTED property) - Enter your conditions as a long string with property and value separated via a colon and multiple entries separated with a comma. For example: ClassificationContentMarkingHeaderText:PROTECTED,ClassificationContentMarkingFooterText:PROTECTED,Objective-Classification:PROTECTED,PMDisplay:PROTECTED,TitusSEC:PROTECTED Note that the properties that you are referencing are the Managed Property rather than the document property. This will be relevant if your managed property ended up having a different name due to character restrictions. After pasting in your string into the UI, the resultant rule should look something like this: When done, you can either leave your policy in simulation mode or save it and then turn it on from the auto-labelling policies screen. Just be aware of any potential impacts, such as accidently locking users out by automatically deploying a label with encryption configuration. You can reduce any potential impact by targeting your auto-labelling policy at a site or set of sites initially and then expanding its scope after testing. Step 5 - Test Testing your configuration will be as easy as uploading or creating a set of files with the relevant document properties in place. Once uploaded, you’ll need to give SharePoint some time to index the items and then the auto-labelling policy some time to apply sensitivity labels to them. To confirm label application, you can head to the document library where your test files are located and enable the sensitivity column. Files that have been auto-labelled will have their label listed: You could also check for auto-labelling activity in Purview via Activity explorer: Step 6 – Expand into DLP If you’ve spent the time setting up managed properties, then you really should consider capitalizing on them in your DLP configurations. DLP policy conditions can be configured in the same manner that we configured Auto-labelling in Step 3 above. The document property also gives us an anchor for DLP conditions that is independent of an item’s sensitivity label. You may wish to consider the following: DLP policies blocking external sharing of items with certain properties applied. This might be handy for situations where auto-labelling hasn’t yet labelled an item. DLP policies blocking the external sharing of items where the applied sensitivity label doesn’t match the applied document property. This could provide an indication of risky label downgrade. You could extend such policies into Insider Risk Management (IRM) by creating IRM policies that are aligned with the above DLP policies. This will allow for document properties to be considered in user risk calculation, which can inform controls like Adaptive Protection. Here's an example of a policy from the DLP rule summary screen that shows conditions of item contains a label or one of our configured document properties: Thanks for reading and I hope this article has been of use. If you have any questions or feedback, please feel free to reach out.
