Introducing the new PowerShell Module for Microsoft Defender for Identity
Today, I am excited to introduce a new PowerShell module designed to help further simplify the deployment and configuration of Microsoft Defender for Identity. This tool will make it easier than ever to protect your organization from identity-based cyber-threats.
Microsoft Defender for Office 365 investigation improvements coming soon
For those of you using Microsoft Defender for Office 365 automated investigations, we have several new investigation improvements rolling out this month to improve your experience in the security center. These new features improve the clarity of Office 365 investigations, as well as improve Defender for Office 365 integration with SecOps tools in the security center.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.Microsoft Sentinel MCP Entity Analyzer: Explainable risk analysis for URLs and identities
What makes this release important is not just that it adds another AI feature to Sentinel. It changes the implementation model for enrichment and triage. Instead of building and maintaining a chain of custom playbooks, KQL lookups, threat intel checks, and entity correlation logic, SOC teams can call a single analyzer that returns a reasoned verdict and supporting evidence. Microsoft positions the analyzer as available through Sentinel MCP server connections for agent platforms and through Logic Apps for SOAR workflows, which makes it useful both for interactive investigations and for automated response pipelines. Why this matters First, it formalizes Entity Analyzer as a production feature rather than a preview experiment. Second, it introduces a real cost model, which means organizations now need to govern usage instead of treating it as a free enrichment helper. Third, Microsoft’s documentation is now detailed enough to support repeatable implementation patterns, including prerequisites, limits, required tables, Logic Apps deployment, and cost behavior. From a SOC engineering perspective, Entity Analyzer is interesting because it focuses on explainability. Microsoft describes the feature as generating clear, explainable verdicts for URLs and user identities by analyzing multiple modalities, including threat intelligence, prevalence, and organizational context. That is a much stronger operational model than simple point-enrichment because it aims to return an assessment that analysts can act on, not just more raw evidence What Entity Analyzer actually does The Entity Analyzer tools are described as AI-powered tools that analyze data in the Microsoft Sentinel data lake and provide a verdict plus detailed insights on URLs, domains, and user entities. Microsoft explicitly says these tools help eliminate the need for manual data collection and complex integrations usually required for investigation and enrichment hat positioning is important. In practice, many SOC teams have built enrichment playbooks that fetch sign-in history, query TI feeds, inspect click data, read watchlists, and collect relevant alerts. Those workflows work, but they create maintenance overhead and produce inconsistent analyst experiences. Entity Analyzer centralizes that reasoning layer. For user entities, Microsoft’s preview architecture explains that the analyzer retrieves sign-in logs, security alerts, behavior analytics, cloud app events, identity information, and Microsoft Threat Intelligence, then correlates those signals and applies AI-based reasoning to produce a verdict. Microsoft lists verdict examples such as Compromised, Suspicious activity found, and No evidence of compromise, and also warns that AI-generated content may be incorrect and should be checked for accuracy. That warning matters. The right way to think about Entity Analyzer is not “automatic truth,” but “high-value, explainable triage acceleration.” It should reduce analyst effort and improve consistency, while still fitting into human review and response policy. Under the hood: the implementation model Technically, Entity Analyzer is delivered through the Microsoft Sentinel MCP data exploration tool collection. Microsoft documents that entity analysis is asynchronous: you start analysis, receive an identifier, and then poll for results. The docs note that analysis may take a few minutes and that the retrieval step may need to be run more than once if the internal timeout is not enough for long operations. That design has two immediate implications for implementers. First, this is not a lightweight synchronous enrichment call you should drop carelessly into every automation branch. Second, any production workflow should include retry logic, timeouts, and concurrency controls. If you ignore that, you will create fragile playbooks and unnecessary SCU burn. The supported access path for the data exploration collection requires Microsoft Sentinel data lake and one of the supported MCP-capable platforms. Microsoft also states that access to the tools is supported for identities with at least Security Administrator, Security Operator, or Security Reader. The data exploration collection is hosted at the Sentinel MCP endpoint, and the same documentation notes additional Entity Analyzer roles related to Security Copilot usage. The prerequisite many teams will miss The most important prerequisite is easy to overlook: Microsoft Sentinel data lake is required. This is more than a licensing footnote. It directly affects data quality, analyzer usefulness, and rollout success. If your organization has not onboarded the right tables into the data lake, Entity Analyzer will either fail or return reduced-confidence output. For user analysis, the following tables are required to ensure accuracy: AlertEvidence, SigninLogs, CloudAppEvents, and IdentityInfo. also notes that IdentityInfo depends on Defender for Identity, Defender for Cloud Apps, or Defender for Endpoint P2 licensing. The analyzer works best with AADNonInteractiveUserSignInLogs and BehaviorAnalytics as well. For URL analysis, the analyzer works best with EmailUrlInfo, UrlClickEvents, ThreatIntelIndicators, Watchlist, and DeviceNetworkEvents. If those tables are missing, the analyzer returns a disclaimer identifying the missing sources A practical architecture view An incident, hunting workflow, or analyst identifies a high-interest URL or user. A Sentinel MCP client or Logic App calls Entity Analyzer. Entity Analyzer queries relevant Sentinel data lake sources and correlates the findings. AI reasoning produces a verdict, evidence narrative, and recommendations. The result is returned to the analyst, incident record, or automation workflow for next-step action. This model is especially valuable because it collapses a multi-query, multi-tool investigation pattern into a single explainable decisioning step. Where it fits in real Sentinel operations Entity Analyzer is not a replacement for analytics rules, UEBA, or threat intelligence. It is a force multiplier for them. For identity triage, it fits naturally after incidents triggered by sign-in anomaly detections, UEBA signals, or Defender alerts because it already consumes sign-in logs, cloud app events, and behavior analytics as core evidence sources. For URL triage, it complements phishing and click-investigation workflows because it uses TI, URL activity, watchlists, and device/network context. Implementation path 1: MCP clients and security agents Microsoft states that Entity Analyzer integrates with agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms. In practice, this makes it attractive for analyst copilots, engineering-side investigation agents, and guided triage experiences The benefit of this model is speed. A security engineer or analyst can invoke the analyzer directly from an MCP-capable client without building a custom orchestration layer. The tradeoff is governance: once you make the tool widely accessible, you need a clear policy for who can run it, when it should be used, and how results are validated before action is taken. Implementation path 2: Logic Apps and SOAR playbooks For SOC teams, Logic Apps is likely the most immediately useful deployment model. Microsoft documents an entity analyzer action inside the Microsoft Sentinel MCP tools connector and provides the required parameters for adding it to an existing logic app. These include: Workspace ID Look Back Days Properties payload for either URL or User The documented payloads are straightforward: { "entityType": "Url", "url": "[URL]" } And { "entityType": "User", "userId": "[Microsoft Entra object ID or User Principal Name]" } Also states that the connector supports Microsoft Entra ID, service principals, and managed identities, and that the Logic App identity requires Security Reader to operate. This makes playbook integration a strong pattern for incident enrichment. A high-severity incident can trigger a playbook, extract entities, invoke Entity Analyzer, and post the verdict back to the incident as a comment or decision artifact. The concurrency lesson most people will learn the hard way Unusually direct guidance on concurrency: to avoid timeouts and threshold issues, turn on Concurrency control in Logic Apps loops and start with a degree of parallelism of . The data exploration doc repeats the same guidance, stating that running multiple instances at once can increase latency and recommending starting with a maximum of five concurrent analyses. This is a strong indicator that the correct implementation pattern is selective analysis, not blanket analysis. Do not analyze every entity in every incident. Analyze the entities that matter most: external URLs in phishing or delivery chains accounts tied to high-confidence alerts entities associated with high-severity or high-impact incidents suspicious users with multiple correlated signals That keeps latency, quota pressure, and SCU consumption under control. KQL still matters Entity Analyzer does not eliminate KQL. It changes where KQL adds value. Before running the analyzer, KQL is still useful for scoping and selecting the right entities. After the analyzer returns, KQL is useful for validation, deeper hunting, and building custom evidence views around the analyzer’s verdict. For example, a simple sign-in baseline for a target user: let TargetUpn = "email address removed for privacy reasons"; SigninLogs | where TimeGenerated between (ago(7d) .. now()) | where UserPrincipalName == TargetUpn | summarize Total=count(), Failures=countif(ResultType != "0"), Successes=countif(ResultType == "0"), DistinctIPs=dcount(IPAddress), Apps=make_set(AppDisplayName, 20) by bin(TimeGenerated, 1d) | order by TimeGenerated desc And a lightweight URL prevalence check: let TargetUrl = "omicron-obl.com"; UrlClickEvents | where TimeGenerated between (ago(7d) .. now()) | search TargetUrl | take 50 Cost, billing, and governance GA is where technical excitement meets budget reality. Microsoft’s Sentinel billing documentation says there is no extra cost for the MCP server interface itself. However, for Entity Analyzer, customers are charged for the SCUs used for AI reasoning and also for the KQL queries executed against the Microsoft Sentinel data lake. Microsoft further states that existing Security Copilot entitlements apply The April 2026 “What’s new” entry also explicitly says that starting April 1, 2026, customers are charged for the SCUs required when using Entity Analyzer. That means every rollout should include a governance plan: define who can invoke the analyzer decide when playbooks are allowed to call it monitor SCU consumption limit unnecessary repeat runs preserve results in incident records so you do not rerun the same analysis within a short period Microsoft’s MCP billing documentation also defines service limits: 200 total runs per hour, 500 total runs per day, and around 15 concurrent runs every five minutes, with analysis results available for one hour. Those are not just product limits. They are design requirements. Limitations you should state clearly The analyze_user_entity supports a maximum time window of seven days and only works for users with a Microsoft Entra object ID. On-premises Active Directory-only users are not supported for user analysis. Microsoft also says Entity Analyzer results expire after one hour and that the tool collection currently supports English prompts only. Recommended rollout pattern If I were implementing this in a production SOC, I would phase it like this: Start with a narrow set of high-value use cases, such as suspicious user identities and phishing-related URLs. Confirm that the required tables are present in the data lake. Deploy a Logic App enrichment pattern for incident-triggered analysis. Add concurrency control and retry logic. Persist returned verdicts into incident comments or case notes. Then review SCU usage and analyst value before expanding coverage.Automating Microsoft Sentinel: A blog series on enabling Smart Security
Welcome to the first entry of our blog series on automating Microsoft Sentinel. We're excited to share insights and practical guidance on leveraging automation to enhance your security posture. In this series, we'll explore the various facets of automation within Microsoft Sentinel. Whether you're a seasoned security professional or just starting, our goal is to empower you with the knowledge and tools to streamline your security operations and stay ahead of threats. Join us on this journey as we uncover the power of automation in Microsoft Sentinel and learn how to transform your security strategy from reactive to proactive. Stay tuned for our upcoming posts where we'll dive deeper into specific automation techniques and share success stories from the field. Let's make your security smarter, faster, and more resilient together. In this series, we will show you how to automate various aspects of Microsoft Sentinel, from simple automation of Microsoft Sentinel Alerts and Incidents to more complicated response scenarios with multiple moving parts. We’re doing this as a series so that we can build up our knowledge step-by-step and finishing off with a “capstone project” that takes SOAR into areas that most people aren’t aware of or even thought was possible. Here is a preview of what you can expect in the upcoming posts [we’ll be updating this post with links to new posts as they happen]: Part 1: [You are here] – Introduction to Automating Microsoft Sentinel Part 2: Automation Rules – Automate the mundane away Part 3: Playbooks 1 – Playbooks Part I – Fundamentals o Triggers o Entities o In-App Content / GitHub o Consumption plan vs. dedicated – which to choose and why? Part 4: Playbooks 2 – Playbooks Part II – Diving Deeper o Built-In 1 st and 3 rd Party Connections (ServiceNow, etc.) o REST APIs (everything else) Part 5: Azure Functions / Custom Code o Why Azure Functions? o Consumption vs. Dedicated – which to choose and why? Part 6: Capstone Project (Art of the Possible) – Putting it all together Part 1: Introduction to Automating Microsoft Sentinel Microsoft Sentinel is a cloud-native security information and event management (SIEM) platform that helps you collect, analyze, and respond to security threats across your enterprise. But did you know that it also has a native, integrated Security Orchestration, Automation, and Response (SOAR) platform? A SOAR platform that can do just about anything you can think of? It’s true! What is SOAR and why would I want to use it? A Security Orchestration, Automation, and Response (SOAR) platform helps your team take action in response to alerts or events in your SIEM. For example, let’s say Contoso Corp has a policy where if a user has a medium sign-in risk in Entra ID and fails their login three times in a row within a ten-minute timeframe that we force them to re-confirm their identity with MFA. While an analyst could certainly take the actions required, wouldn’t it be better if we could do that automatically? Using the Sentinel SOAR capabilities, you could have an analytic rule that automatically takes the action without the analyst being involved at all. Why Automate Microsoft Sentinel? Automation is a key component of any modern security operations center (SOC). Automation can help you: Reduce manual tasks and human errors Improve the speed and accuracy of threat detection and response Optimize the use of your resources and skills Enhance your visibility and insights into your security environment Align your security processes with your business objectives and compliance requirements Reduce manual tasks and human errors Alexander Pope famously wrote “To err is human; to forgive, divine”. Busy and distracted humans make mistakes. If we can reduce their workload and errors, then it makes sense to do so. Using automation, we can make sure that all of the proper steps in our response playbook are followed and we can make our analysts lives easier by giving them a simpler “point and click” response capability for those scenarios that a human is “in the loop” or by having the system run the automation in response to events and not have to wait for the analyst to respond. Improve the speed and accuracy of threat detection and response Letting machines do machine-like things (such as working twenty-four hours a day) is a good practice. Leveraging automation, we can let our security operations center (SOC) run around the clock by having automation tied to analytics. Rather than waiting for an analyst to come online, triage an alert and then take action, Microsoft Sentinel can stand guard and respond when needed. Optimize the use of your resources and skills Having our team members repeat the same mundane tasks is not optimal for the speed of response and their work satisfaction. By automating the mundane away, we can give our teams more time to learn new things or work on other tasks. Enhance your visibility and insights into your security environment Automation can be leveraged for more than just responding to an alert or incident. We can augment the information we have about entities involved in an alert or incident by using automation to call REST based APIs to do point-in-time lookups of the latest threat information, vulnerability data, patching statuses, etc. Align your security processes with your business objectives and compliance requirements If you have to meet particular regulatory requirements or internal KPIs, automation can help your team to achieve their goals quickly and consistently. What Tools and Frameworks Can You Use to Automate Microsoft Sentinel? Microsoft Sentinel provides several tools that enable you to automate your security workflows, such as: Automation Rules o Automation rules can be used to automate Microsoft Sentinel itself. For example, let’s say there is a group of machines that have been classified as business critical and if there is an alert related to those machines, then the incident needs to be assigned to a Tier 3 response team, and the severity of the alert needs to be raised to at least “high”. Using an automation rule, you can take one analytic rule, apply it to the entire enterprise, but then have an automation rule that only applies to those business-critical systems. That way only the items that need that immediate escalation receive it, quickly and efficiently. o Another great use of Automation Rules is to create Incident Tasks for analysts to follow. If you have a process and workflow, by using Incident Tasks, you can have those appear inside of an Incident right there for the analysts to follow. No need to go “look it up” in a PDF or other document. Playbooks: You can use playbooks to automatically execute actions based on triggers, such as alerts, incidents, or custom events. Playbooks are based on Azure Logic Apps, which allow you to create workflows using various connectors, such as Microsoft Teams, Azure Functions, Azure Automation, and third-party services. Azure Functions can be leveraged to run custom code like PowerShell or Python and can be called from Sentinel via Playbooks. This way if you have a process or code that’s beyond a Playbook , you can still call it from the normal Sentinel workflow. Conclusion In this blog post, we introduced the automation capabilities and benefits of SOAR in Microsoft Sentinel, and some of the tools and frameworks that you can use to automate your security workflows. In the next blog posts, we will dive deeper into each of these topics and provide some practical examples and scenarios of how to automate Microsoft Sentinel. Stay tuned for more updates and tips on automating Microsoft Sentinel! Additional Resources What are Automation Rules? Automate Threat Response with playbooks in Microsoft Sentinel
