How to: Ingest Splunk alert data to Microsoft Sentinel SIEM
Thanks to Javier Soriano, Principal Product Manager - OneSOC Customer Experience Engineering, for the peer review Introduction Although the recommended approach is to not have multiple SIEM solutions in place, many organizations are still running dual-SIEM setups, sometimes even introducing additional ones in the mix. Combination most often seen is running a legacy solution and pairing it with modern SIEM solutions like Microsoft Sentinel SIEM. Side-by-side architecture is recommended for just long enough to complete the migration, train people and update processes - but organizations might stay with the side-by-side configuration longer when they are not ready to move away from legacy solutions. In such situations, organizations usually opt for sending alerts from their legacy SIEM to Sentinel SIEM: Cloud data is ingested and analyzed in Sentinel SIEM On-premises data is ingested and analyzed in legacy SIEM which generates alerts Alerts are forwarded from legacy SIEM to Sentinel SIEM With this approach, SOC teams have a single interface where they are able to cross-correlate and investigate alerts from their organizations while benefiting from full value of unified security operations in Microsoft Defender. Send Splunk alerts to Sentinel SIEM Splunk side When an alert is raised in Splunk, organizations have an option to set up following alert actions: Email notification action Webhook alert action Output results to a CSV lookup Log events Monitor triggered alerts Send alerts and dashboards to Splunk Mobile Users Interestingly, it is possible to work with Webhooks to make an HTTP POST request on a URL. The data is in JSON format which makes it easily consumable by Sentinel SIEM. For this to work, Splunk needs the hook URL from the target source (in this case, Sentinel SIEM). { "result": { "sourcetype" : "mongod", "count" : "8" }, "sid" : "scheduler_admin_search_W2_at_14232356_132", "results_link" : "http://web.example.local:8000/app/search/@go?sid=scheduler_admin_search_W2_at_14232356_132", "search_name" : null, "owner" : "admin", "app" : "search" } Example: Splunk alert JSON payload Microsoft side From Microsoft perspective, organizations can take advantage of Logs Ingestion API, which allows for any application that can make a REST API call to send data to Sentinel SIEM. To configure Logs Ingestion API, a couple of supporting resources are needed: Microsoft Entra application which will authenticate against the API Custom table in Log Analytics workspace, where the data will be sent to and accessible from Sentinel SIEM Data Collection Rule (DCR) which will direct data to the target custom table Entra application from the first step needs to have RBAC assigned on the DCR resource A solution to call Logs Ingestion API so the data can be sent to the Sentinel SIEM. In order to make this process streamlined and easy to deploy, a solution has been developed which will automate creation of all of these supporting resources and allow you to have a Webhook URL ready which can be just placed in your Splunk solution: https://github.com/Azure/Azure-Sentinel/tree/master/Tools/SplunkAlertIngestion Picture: Content of the solution The script with supporting ARM templates can be run directly from the Azure Cloud Shell and configured with a couple of parameters: ./SplunkAlertIngestion.ps1 -ServicePrincipalName "" -tableName "" -workspaceResourceId "" -dataCollectionRuleName "" -location "" ServicePrincipalName - mandatory, define a name for the SP tableName - mandatory, define a name for the custom table with the suffix _CL (example: SplunkAlertInfo_CL) workspaceResourceId - mandatory, the resourceId can be fetched from the Log Analytics Workspace Properties blade (/subscriptions/xxx-xxx/resourceGroups/xxx/providers/Microsoft.OperationalInsights/workspaces/xxx) dataCollectionRuleName - mandatory, define DCR name location - mandatory, define Azure location for the resources (example: northeurope, eastus2) LogicAppName - not mandatory, define the name for the LogicApp, otherwise it will be named SplunkAlertAutomationLogicApp Result The script will create all supporting resources that are needed and will provide the Webhook URL as an output. Use this URL to configure trigger action in Splunk: Picture: Instructions for configuring webhook alert action in Splunk Once the webhook is configured on Splunk side, any time the alert is raised it will trigger the webhook, which will initiate the Logic App resource on Azure side responsible for parsing the data and sending that data through Logs Ingestion API to the destination table in Sentinel SIEM. Picture: Workflow of the Logic App Conclusion Ingesting alert data from other solutions in your organization to Sentinel SIEM allows for security teams to take advantage of unified security operations in Microsoft Defender - easier cross-correlation between various data sources, comprehensive threat intelligence and case management experience.Automating Phishing Email Triage with Microsoft Security Copilot
This blog details automating phishing email triage using Azure Logic Apps, Azure Function Apps, and Microsoft Security Copilot. Deployable in under 10 minutes, this solution primarily analyzes email intent without relying on traditional indicators of compromise, accurately classifying benign/junk, suspicious, and phishing emails. Benefits include reducing manual workload, improved threat detection, and (optional) integration seamlessly with Microsoft Sentinel – enabling analysts to see Security Copilot analysis within the incident itself. Designed for flexibility and control, this Logic App is a customizable solution that can be self-deployed from GitHub. It helps automate phishing response at scale without requiring deep coding expertise, making it ideal for teams that prefer a more configurable approach and want to tailor workflows to their environment. The solution streamlines response and significantly reduces manual effort. Access the full solution on the Security Copilot Github: GitHub - UserReportedPhishing Solution. For teams looking for a more sophisticated, fully integrated experience, the Security Copilot Phishing Triage Agent represents the next generation of phishing response. Natively embedded in Microsoft Defender, the agent autonomously triages phishing incidents with minimal setup. It uses advanced LLM-based reasoning to resolve false alarms, enabling analysts to stay focused on real threats. The agent offers step-by-step decision transparency and continuously learns from user feedback. Read the official announcement here. Introduction: Phishing Challenges Continue to Evolve Phishing continues to evolve in both scale and sophistication, but a growing challenge for defenders isn't just stopping phishing, it’s scaling response. Thanks to tools like Outlook’s "Report Phishing" button and increased user awareness, organizations are now flooded with user-reported emails, many of which are ambiguous or benign. This has created a paradox: better detection by users has overwhelmed SOC teams, turning email triage into a manual, rotational task dreaded for its repetitiveness and time cost, often taking over 25 minutes per email to review. Our solution addresses that problem, by automating the triage of user-reported phishing through AI-driven intent analysis. It's not built to replace your secure email gateways or Microsoft Defender for Office 365; those tools have already done their job. This system assumes the email: Slipped past existing filters, Was suspicious enough for a user to escalate, Lacks typical IOCs like malicious domains or attachments. As a former attacker, I spent years crafting high-quality phishing emails to penetrate the defenses of major banks. Effective phishing doesn't rely on obvious IOCs like malicious domains, URLs, or attachments… the infrastructure often appears clean. The danger lies in the intent. This is where Security Copilot’s LLM-based reasoning is critical, analyzing structure, context, tone, and seasonal pretexts to determine whether an email is phishing, suspicious, spam, or legitimate. What makes this novel is that it's the first solution built specifically for the “last mile” of phishing defense, where human suspicion meets automation, and intent is the only signal left to analyze. It transforms noisy inboxes into structured intelligence and empowers analysts to focus only on what truly matters. Solution Overview: How the Logic App Solution Works (and Why It's Different) Core Components: Azure Logic Apps: Orchestrates the entire workflow, from ingestion to analysis, and 100% customizable. Azure Function Apps: Parses and normalizes email data for efficient AI consumption. Microsoft Security Copilot: Performs sophisticated AI-based phishing analysis by understanding email intent and tactics, rather than relying exclusively on predefined malicious indicators. Key Benefits: Rapid Analysis: Processes phishing alerts and, in minutes, delivers comprehensive reports that empower analysts to make faster, more informed triage decisions – compared to manual reviews that can take up to 30 minutes. And, unlike analysts, Security Copilot requires zero sleep! AI-driven Insights: LLM-based analysis is leveraged to generate clear explanations of classifications by assessing behavioral and contextual signals like urgency, seasonal threats, Business Email Compromise (BEC), subtle language clues, and otherwise sophisticated techniques. Most importantly, it identifies benign emails, which are often the bulk of reported emails. Detailed, Actionable Reports: Generates clear, human-readable HTML reports summarizing threats and recommendations for analyst review. Robust Attachment Parsing: Automatically examines attachments like PDFs and Excel documents for malicious content or contextual inconsistencies. Integrated with Microsoft Sentinel: Optional integration with Sentinel ensures central incident tracking and comprehensive threat management. Analysis is attached directly to the incident, saving analysts more time. Customization: Add, move, or replace any element of the Logic App or prompt to fit your specific workflows. Deployment Guide: Quick, Secure, and Reliable Setup The solution provides Azure Resource Manager (ARM) templates for rapid deployment: Prerequisites: Azure Subscription with Contributor access to a resource group. Microsoft Security Copilot enabled. Dedicated Office 365 shared mailbox (e.g., phishing@yourdomain.com) with Mailbox.Read.Shared permissions. (Optional) Microsoft Sentinel workspace. Refer to the up to date deployment instructions on the Security Copilot GitHub page. Technical Architecture & Workflow: The automated workflow operates as follows: Email Ingestion: Monitors the shared mailbox via Office 365 connector. Triggers on new email arrivals every 3 minutes. Assumes that the reported email has arrived as an attachment to a "carrier" email. Determine if the Email Came from Defender/Sentinel: If the email came from Defender, it would have a prepended subject of “Phishing”, if not, it takes the “False” branch. Change as necessary. Initial Email Processing: Exports raw email content from the shared mailbox. Determines if .msg or .eml attachments are in binary format and converts if necessary. Email Parsing via Azure Function App: Extracts data from email content and attachments (URLs, sender info, email body, etc.) and returns a JSON structure. Prepares clean JSON data for AI analysis. This step is required to "prep" the data for LLM analysis due to token limits. Click on the “Parse Email” block to see the output of the Function App for any troubleshooting. You'll also notice a number of JSON keys that are not used but provided for flexibility. Security Copilot Advanced AI Reasoning: Analyzes email content using a comprehensive prompt that evaluates behavioral and seasonal patterns, BEC indicators, attachment context, and social engineering signals. Scores cumulative risk based on structured heuristics without relying solely on known malicious indicators. Returns validated JSON output (some customers are parsing this JSON and performing other action). This is where you would customize the prompt, should you need to add some of your own organizational situations if the Logic App needs to be tuned: JSON Normalization & Error Handling: A “normalization” Azure Function ensures output matches the expected JSON schema. Sometimes LLMs will stray from a strict output structure, this aims to solve that problem. If you add or remove anything from the Parse Email code that alters the structure of the JSON, this and the next block will need to be updated to match your new structure. Detailed HTML Reporting: Generates a detailed HTML report summarizing AI findings, indicators, and recommended actions. Reports are emailed directly to SOC team distribution lists or ticketing systems. Optional Sentinel Integration: Adds the reasoning & output from Security Copilot directly to the incident comments. This is the ideal location for output since the analyst is already in the security.microsoft.com portal. It waits up to 15 minutes for logs to appear, in situations where the user reports before an incident is created. The solution works pretty well out of the box but may require some tuning, give it a test. Here are some examples of the type of Security Copilot reasoning. Benign email detection: Example of phishing email detection: More sophisticated phishing with subtle clues: Enhanced Technical Details & Clarifications Attachment Processing: When multiple email attachments are detected, the Logic App processes each binary-format email sequentially. If PDF or Excel attachments are detected, they are parsed for content and are evaluated appropriately for content and intent. Security Copilot Reliability: The Security Copilot Logic App API call uses an extensive retry policy (10 retries at 10-minute intervals) to ensure reliable AI analysis despite intermittent service latency. If you run out of SCUs in an hour, it will pause until they are refreshed and continue. Sentinel Integration Reliability: Acknowledges inherent Sentinel logging delays (up to 15 minutes). Implements retry logic and explicit manual alerting for unmatched incidents, if the analysis runs before the incident is created. Security Best Practices: Compare the Function & Logic App to your company security policies to ensure compliance. Credentials, API keys, and sensitive details utilize Azure Managed Identities or secure API connections. No secrets are stored in plaintext. Azure Function Apps perform only safe parsing operations; attachments and content are never executed or opened insecurely. Be sure to check out how the Microsoft Defender for Office team is improving detection capabilities as well Microsoft Defender for Office 365's Language AI for Phish: Enhancing Email Security | Microsoft Community Hub.Case Management: Incidents, Cases, and When to Use Them
In March, Case Management went to GA status within the unified portal for customers. This introduced new functionality and experiences such as: A new case queue Custom statuses New Case task experience Linking incidents to cases This can be a little confusing for existing users who are familiar with incidents and the incident experience for either Microsoft Defender or Sentinel. Let’s break this down into more detail. What are Incidents? Incidents are artifacts that act as containers for alerts to signal that a noteworthy event took place that involves one or more malicious activities. These serve to be a single landing page for alerts, activities, entities, and more. When to use Incidents? Incidents are the default experience for analysts as they perform incident investigations and response. Incidents are where they will find any and all details available for alerts and entities while performing the basic tasks of a SOC analyst. Incidents should be used when investigating and responding to malicious activity within the environment. The current incident experience provides features such as: Alert timeline Entity mapping and tracking Entity investigation graph Copilot for Security Pre-performed investigations and responses What are Cases? Cases are artifacts that represent an actionable or trackable item, such as incident investigation, validating a threat hunting hypothesis, reviewing threat intelligence review, managing endpoint vulnerabilities, and more. They can exist without alerts or incidents. When to use Cases vs. Incidents? Cases serve as items that can be created to track important activities within the SOC, they don’t have to just be for incident response. A case can be created for any notable activity that the SOC performs, as mentioned above. Cases can be used as a collaboration tool within your SOC team. While cases may seem redundant to incident, that is not true one bit. Here are a few distinguishing points: As incidents are a container for alerts, cases can be a container for incidents, allowing multiple incidents to be worked on at once if they are related by threat actor, impacted entities, and more. Cases offer a native task experience, similar to the experience within Microsoft Sentinel in Azure. Cases offer attachment support, allowing analysts a more traditional case management experience that incidents do not have. Cases offer a stronger collaborative experience, offering rich text comments and communications within each case. Cases allow for more customization, such as custom statuses. Incidents do not offer custom statuses. Let’s look at two example scenarios: Cases with Incidents I am a SOC Analyst that is reviewing the incident queue. I find an incident that involves multiple threat types and scripts. I would like to work on this incident with my colleagues while tracking notable artifacts that we find in our investigation. For example: I visit the unified incident queue and see that I have a multi-stage incident, involving multiple alerts for multiple assets. I perform my initial triage and confirm that this is a true positive that should be addressed. I will then cut a case and attach this incident to it for collaboration. Within the case, I can add a code block to list any query that I have performed within Advanced Hunting, as well as paste results from my queries directly in the case for tracking. If using Copilot for Security, I can copy and paste the Copilot incident summary in the case so that my colleagues can get an incident summary without having to leave the case. Cases without Incidents I am a SOC Analyst that is responsible for remediating device vulnerabilities. I check our current CVE’s within Exposure Management and see that I have several devices that are currently vulnerable to CVE-2025-5419, a Microsoft Edge Chromium vulnerability. I save my list of devices to a CSV file so that I can attach it to my case. I also copy the description of the CVE to add the case notes to make it more convenient for my colleagues to join the case and not need to leave it. I then pivot to Advanced Hunting to review activities by any of these vulnerable devices. I have a match and would like to connect that result to my case, so I use Export > Copy to Clipboard so that I can paste it in the case. Back within the case, I begin uploading the CSV of exposed devices as evidence, I leave a message that is formatted to draw attention to the findings, and I paste my findings based on my query. Based on my findings, I begin generating new tasks for each device owner and pasting the instructions for remediation of the CVE. These are just some examples of the many uses for cases within the Defender Portal. Hopefully this highlights the versatility of case management today and how it can operate both with and without an incident involved. Keep an eye out for more improvements as Case Management matures. If looking to learn about case management, please check out the below resources: Public documentation: Manage security operations cases natively in the Microsoft Defender portal - Unified security operations | Microsoft Learn Video based learning: https://www.youtube.com/watch?v=G-vfMJSL11g Demo: Case Management in Microsoft DefenderUsing parameterized functions with KQL-based custom plugins in Microsoft Security Copilot
In this blog, I will walk through how you can build functions based on a Microsoft Sentinel Log Analytics workspace for use in custom KQL-based plugins for Security Copilot. The same approach can be used for Azure Data Explorer and Defender XDR, so long as you follow the specific guidance for either platform. A link to those steps is provided in the Additional Resources section at the end of this blog. But first, it’s helpful to clarify what parameterized functions are and why they are important in the context of Security Copilot KQL-based plugins. Parameterized functions accept input details (variables) such as lookback periods or entities, allowing you to dynamically alter parts of a query without rewriting the entire logic Parameterized functions are important in the context of Security Copilot plugins because of: Dynamic prompt completion: Security Copilot plugins often accept user input (e.g., usernames, time ranges, IPs). Parameterized functions allow these inputs to be consistently injected into KQL queries without rebuilding query logic. Plugin reusability: By using parameters, a single function can serve multiple investigation scenarios (e.g., checking sign-ins, data access, or alerts for any user or timeframe) instead of hardcoding different versions. Maintainability and modularity: Parameterized functions centralize query logic, making it easier to update or enhance without modifying every instance across the plugin spec. To modify the logic, just edit the function in Log Analytics, test it then save it- without needing to change the plugin at all or re-upload it into Security Copilot. It also significantly reduces the need to ensure that the query part of the YAML is perfectly indented and tabbed as is required by the Open API specification, you only need to worry about formatting a single line vs several-potentially hundreds. Validation: Separating query logic from input parameters improves query reliability by avoiding the possibility of malformed queries. No matter what the input is, it's treated as a value, not as part of the query logic. Plugin Spec mapping: OpenAPI-based Security Copilot plugins can map user-provided inputs directly to function parameters, making the interaction between user intent and query execution seamless. Practical example In this case, we have a 139-line KQL query that we will reduce to exactly one line that goes into the KQL plugin. In other cases, this number could be even higher. Without using functions, this entire query would have to form part of the plugin Note: The rest of this blog assumes you are familiar with KQL custom plugins-how they work and how to upload them into Security Copilot. CloudAppEvents | where RawEventData.TargetDomain has_any ( 'grok.com', 'x.ai', 'mistral.ai', 'cohere.ai', 'perplexity.ai', 'huggingface.co', 'adventureai.gg', 'ai.google/discover/palm2', 'ai.meta.com/llama', 'ai2006.io', 'aibuddy.chat', 'aidungeon.io', 'aigcdeep.com', 'ai-ghostwriter.com', 'aiisajoke.com', 'ailessonplan.com', 'aipoemgenerator.org', 'aissistify.com', 'ai-writer.com', 'aiwritingpal.com', 'akeeva.co', 'aleph-alpha.com/luminous', 'alphacode.deepmind.com', 'analogenie.com', 'anthropic.com/index/claude-2', 'anthropic.com/index/introducing-claude', 'anyword.com', 'app.getmerlin.in', 'app.inferkit.com', 'app.longshot.ai', 'app.neuro-flash.com', 'applaime.com', 'articlefiesta.com', 'articleforge.com', 'askbrian.ai', 'aws.amazon.com/bedrock/titan', 'azure.microsoft.com/en-us/products/ai-services/openai-service', 'bard.google.com', 'beacons.ai/linea_builds', 'bearly.ai', 'beatoven.ai', 'beautiful.ai', 'beewriter.com', 'bettersynonyms.com', 'blenderbot.ai', 'bomml.ai', 'bots.miku.gg', 'browsegpt.ai', 'bulkgpt.ai', 'buster.ai', 'censusgpt.com', 'chai-research.com', 'character.ai', 'charley.ai', 'charshift.com', 'chat.lmsys.org', 'chat.mymap.ai', 'chatbase.co', 'chatbotgen.com', 'chatgpt.com', 'chatgptdemo.net', 'chatgptduo.com', 'chatgptspanish.org', 'chatpdf.com', 'chattab.app', 'claid.ai', 'claralabs.com', 'claude.ai/login', 'clipdrop.co/stable-diffusion', 'cmdj.app', 'codesnippets.ai', 'cohere.com', 'cohesive.so', 'compose.ai', 'contentbot.ai', 'contentvillain.com', 'copy.ai', 'copymatic.ai', 'copymonkey.ai', 'copysmith.ai', 'copyter.com', 'coursebox.ai', 'coverler.com', 'craftly.ai', 'crammer.app', 'creaitor.ai', 'dante-ai.com', 'databricks.com', 'deepai.org', 'deep-image.ai', 'deepreview.eu', 'descrii.tech', 'designs.ai', 'docgpt.ai', 'dreamily.ai', 'editgpt.app', 'edwardbot.com', 'eilla.ai', 'elai.io', 'elephas.app', 'eleuther.ai', 'essayailab.com', 'essay-builder.ai', 'essaygrader.ai', 'essaypal.ai', 'falconllm.tii.ae', 'finechat.ai', 'finito.ai', 'fireflies.ai', 'firefly.adobe.com', 'firetexts.co', 'flowgpt.com', 'flowrite.com', 'forethought.ai', 'formwise.ai', 'frase.io', 'freedomgpt.com', 'gajix.com', 'gemini.google.com', 'genei.io', 'generatorxyz.com', 'getchunky.io', 'getgptapi.com', 'getliner.com', 'getsmartgpt.com', 'getvoila.ai', 'gista.co', 'github.com/features/copilot', 'giti.ai', 'gizzmo.ai', 'glasp.co', 'gliglish.com', 'godinabox.co', 'gozen.io', 'gpt.h2o.ai', 'gpt3demo.com', 'gpt4all.io', 'gpt-4chan+)', 'gpt6.ai', 'gptassistant.app', 'gptfy.co', 'gptgame.app', 'gptgo.ai', 'gptkit.ai', 'gpt-persona.com', 'gpt-ppt.neftup.app', 'gptzero.me', 'grammarly.com', 'hal9.com', 'headlime.com', 'heimdallapp.org', 'helperai.info', 'heygen.com', 'heygpt.chat', 'hippocraticai.com', 'huggingface.co/spaces/tiiuae/falcon-180b-demo', 'humanpal.io', 'hypotenuse.ai', 'ichatwithgpt.com', 'ideasai.com', 'ingestai.io', 'inkforall.com', 'inputai.com/chat/gpt-4', 'instantanswers.xyz', 'instatext.io', 'iris.ai', 'jasper.ai', 'jigso.io', 'kafkai.com', 'kibo.vercel.app', 'kloud.chat', 'koala.sh', 'krater.ai', 'lamini.ai', 'langchain.com', 'laragpt.com', 'learn.xyz', 'learnitive.com', 'learnt.ai', 'letsenhance.io', 'letsrevive.app', 'lexalytics.com', 'lgresearch.ai', 'linke.ai', 'localbot.ai', 'luis.ai', 'lumen5.com', 'machinetranslation.com', 'magicstudio.com', 'magisto.com', 'mailshake.com/ai-email-writer', 'markcopy.ai', 'meetmaya.world', 'merlin.foyer.work', 'mieux.ai', 'mightygpt.com', 'mosaicml.com', 'murf.ai', 'myaiteam.com', 'mygptwizard.com', 'narakeet.com', 'nat.dev', 'nbox.ai', 'netus.ai', 'neural.love', 'neuraltext.com', 'newswriter.ai', 'nextbrain.ai', 'noluai.com', 'notion.so', 'novelai.net', 'numind.ai', 'ocoya.com', 'ollama.ai', 'openai.com', 'ora.ai', 'otterwriter.com', 'outwrite.com', 'pagelines.com', 'parallelgpt.ai', 'peppercontent.io', 'perplexity.ai', 'personal.ai', 'phind.com', 'phrasee.co', 'play.ht', 'poe.com', 'predis.ai', 'premai.io', 'preppally.com', 'presentationgpt.com', 'privatellm.app', 'projectdecember.net', 'promptclub.ai', 'promptfolder.com', 'promptitude.io', 'qopywriter.ai', 'quickchat.ai/emerson', 'quillbot.com', 'rawshorts.com', 'read.ai', 'rebecc.ai', 'refraction.dev', 'regem.in/ai-writer', 'regie.ai', 'regisai.com', 'relevanceai.com', 'replika.com', 'replit.com', 'resemble.ai', 'resumerevival.xyz', 'riku.ai', 'rizzai.com', 'roamaround.app', 'rovioai.com', 'rytr.me', 'saga.so', 'sapling.ai', 'scribbyo.com', 'seowriting.ai', 'shakespearetoolbar.com', 'shortlyai.com', 'simpleshow.com', 'sitegpt.ai', 'smartwriter.ai', 'sonantic.io', 'soofy.io', 'soundful.com', 'speechify.com', 'splice.com', 'stability.ai', 'stableaudio.com', 'starryai.com', 'stealthgpt.ai', 'steve.ai', 'stork.ai', 'storyd.ai', 'storyscapeai.app', 'storytailor.ai', 'streamlit.io/generative-ai', 'summari.com', 'synesthesia.io', 'tabnine.com', 'talkai.info', 'talkpal.ai', 'talktowalle.com', 'team-gpt.com', 'tethered.dev', 'texta.ai', 'textcortex.com', 'textsynth.com', 'thirdai.com/pocketllm', 'threadcreator.com', 'thundercontent.com', 'tldrthis.com', 'tome.app', 'toolsaday.com/writing/text-genie', 'to-teach.ai', 'tutorai.me', 'tweetyai.com', 'twoslash.ai', 'typeright.com', 'typli.ai', 'uminal.com', 'unbounce.com/product/smart-copy', 'uniglobalcareers.com/cv-generator', 'usechat.ai', 'usemano.com', 'videomuse.app', 'vidext.app', 'virtualghostwriter.com', 'voicemod.net', 'warmer.ai', 'webllm.mlc.ai', 'wellsaidlabs.com', 'wepik.com', 'we-spots.com', 'wordplay.ai', 'wordtune.com', 'workflos.ai', 'woxo.tech', 'wpaibot.com', 'writecream.com', 'writefull.com', 'writegpt.ai', 'writeholo.com', 'writeme.ai', 'writer.com', 'writersbrew.app', 'writerx.co', 'writesonic.com', 'writesparkle.ai', 'writier.io', 'yarnit.app', 'zevbot.com', 'zomani.ai' ) | extend sit = parse_json(tostring(RawEventData.SensitiveInfoTypeData)) | mv-expand sit | summarize Event_Count = count() by tostring(sit.SensitiveInfoTypeName), CountryCode, City, UserId = tostring(RawEventData.UserId), TargetDomain = tostring(RawEventData.TargetDomain), ActionType = tostring(RawEventData.ActionType), IPAddress = tostring(RawEventData.IPAddress), DeviceType = tostring(RawEventData.DeviceType), FileName = tostring(RawEventData.FileName), TimeBin = bin(TimeGenerated, 1h) | extend SensitivityScore = case(tostring(sit_SensitiveInfoTypeName) in~ ("U.S. Social Security Number (SSN)", "Credit Card Number", "EU Tax Identification Number (TIN)","Amazon S3 Client Secret Access Key","All Credential Types"), 90, tostring(sit_SensitiveInfoTypeName) in~ ("All Full names"), 40, tostring(sit_SensitiveInfoTypeName) in~ ("Project Obsidian", "Phone Number"), 70, tostring(sit_SensitiveInfoTypeName) in~ ("IP"), 50,10 ) | join kind=leftouter ( IdentityInfo | where TimeGenerated > ago(lookback) | extend AccountUpn = tolower(AccountUPN) ) on $left.UserId == $right.AccountUpn | join kind=leftouter ( BehaviorAnalytics | where TimeGenerated > ago(lookback) | extend AccountUpn = tolower(UserPrincipalName) ) on $left.UserId == $right.AccountUpn //| where BlastRadius == "High" //| where RiskLevel == "High" | where Department == User_Dept | summarize arg_max(TimeGenerated, *) by sit_SensitiveInfoTypeName, CountryCode, City, UserId, TargetDomain, ActionType, IPAddress, DeviceType, FileName, TimeBin, Department, SensitivityScore | summarize sum(Event_Count) by sit_SensitiveInfoTypeName, CountryCode, City, UserId, Department, TargetDomain, ActionType, IPAddress, DeviceType, FileName, TimeBin, BlastRadius, RiskLevel, SourceDevice, SourceIPAddress, SensitivityScore With parameterized functions, follow these steps to simplify the plugin that will be built based on the query above Define the variable/parameters upfront in the query (BEFORE creating the parameters in the UI). This will put the query in a “temporary” unusable state because the parameters will cause syntax problems in this state. However, since the plan is to run the query as a function this is ok Create the parameters in the Log Analytics UI Give the function a name and define the parameters exactly as they show up in the query in step 1 above. In this example, we are defining two parameters: lookback – to store the lookback period to be passed to the time filter and User_Dept to the user’s department. 3. Test the query. Note the order of parameter definition in the UI. i.e. first the User_Dept THEN the lookback period. You can interchange them if you like but this will determine how you submit the query using the function. If the User_Dept parameter was defined first then it needs to come first when executing the function. See the below screenshot. Switching them will result in the wrong parameter being passed to the query and consequently 0 results will be returned. Effect of switched parameters: To edit the function, follow the steps below: Navigate to the Logs menu for your Log Analytics workspace then select the function icon Once satisfied with the query and function, build your spec file for the Security Copilot plugin. Note the parameter definition and usage in the sections highlighted in red below And that’s it, from 139 unwieldy KQL lines to one very manageable one! You are welcome 😊 Let’s now put it through its paces once uploaded into Security Copilot. We start by executing the plugin using its default settings via the direct skill invocation method. We see indeed that the prompt returns results based on the default values passed as parameters to the function: Next, we still use direct skill invocation, but this time specify our own parameters: Lastly, we test it out with a natural language prompt: tment Tip: The function does not execute successfully if the default summarize function is used without creating a variable i.e. If the summarize count() command is used in your query, it results in a system-defined output variable named count_. To bypass this issue, ensure to use a user-defined variable such as Event_Count as shown in line 77 below: Conclusion In conclusion, leveraging parameterized functions within KQL-based custom plugins in Microsoft Security Copilot can significantly streamline your data querying and analysis capabilities. By encapsulating reusable logic, improving query efficiency, and ensuring maintainability, these functions provide an efficient approach for tapping into data stored across Microsoft Sentinel, Defender XDR and Azure Data Explorer clusters. Start integrating parameterized functions into your KQL-based Security Copilot plugins today and let us have your feedback. Additional Resources Using parameterized functions in Microsoft Defender XDR Using parameterized functions with Azure Data Explorer Functions in Azure Monitor log queries - Azure Monitor | Microsoft Learn Kusto Query Language (KQL) plugins in Microsoft Security Copilot | Microsoft Learn Harnessing the power of KQL Plugins for enhanced security insights with Copilot for Security | Microsoft Community HubBusting myths on Microsoft Security Copilot
This blog aims to dispel common misconceptions surrounding Microsoft Security Copilot, a cutting-edge tool designed to enhance cybersecurity measures. By addressing these myths, we hope to provide clarity on how this innovative solution can be leveraged to strengthen your organization's security.Using Security Copilot to Proactively Identify and Prioritize Vulnerabilities
Introduction There are many different approaches when it comes to prioritizing the vulnerabilities which need addressing with urgency. Any information or guidance to help you make better informed decisions can be critical but how can you stay informed? Leveraging all the information sources available to you can be the difference and allow you to be proactive when trying to protect your organization. One useful feed is offered by CISA (Cybersecurity & Infrastructure Security Agency) who works with partners to defend against today’s threats and collaborate to build a more secure and resilient infrastructure for the future. The Known Exploited Vulnerabilities (KEV) Catalog is a curated list maintained by CISA. It identifies vulnerabilities that have been actively exploited in the wild, posing significant risks to organizations and individuals. The catalog aims to enhance cybersecurity by providing timely information on these vulnerabilities, enabling proactive mitigation efforts. Key features of the KEV Catalog include: Identification: Lists vulnerabilities that are confirmed to be exploited. Details: Provides technical details, including affected products and versions. Mitigation: Offers guidance on how to address and remediate the vulnerabilities. Updates: Regularly updated to reflect new threats and exploited vulnerabilities. The KEV Catalog serves as a critical resource for cybersecurity professionals, helping them prioritize patching and defense strategies to protect against known threats. The feed is designed to help organizations stay informed about vulnerabilities that have been exploited in the wild. It is part of CISA's efforts to defend against current threats and build a more secure and resilient infrastructure for the future Workflow overview The automated CISA feed solution addresses prioritization challenges by streamlining the process of vulnerability management. This solution checks the latest CISA feed every 24 hours and queries the CVE findings against devices within Microsoft Defender for Endpoint. Security Copilot then checks for remediation actions and enriches the description, providing a comprehensive overview of the vulnerability. Key benefits of the Logic App include: Automated Updates: The Logic App automatically retrieves the latest CISA feed, ensuring that analysts have up-to-date information without manual intervention. This eliminates the need for manual checks and reduces the risk of missing critical updates. Device Vulnerability Assessment: It queries the CVE findings against devices within the organization, identifying which devices are vulnerable to the reported CVEs. This targeted approach allows analysts to focus on the most critical vulnerabilities affecting their specific environment, enhancing the efficiency of the remediation process. Remediation Insights: Security Copilot provides detailed remediation actions, helping analysts understand the steps needed to mitigate the vulnerabilities. By enriching the description with actionable insights, it simplifies the decision-making process and accelerates the implementation of security measures. Email Notifications: An email with the findings is sent to a designated mailbox, allowing for easy review and follow-up. This ensures that all relevant stakeholders are informed promptly, facilitating coordinated responses and continuous monitoring of the organization's security posture. Click here to get started and install the Logic App today. Conclusion To prioritize effectively, gather all necessary information for informed decisions. While the Logic App CISA workflow is one approach, other methods may better suit your organization. Function Apps can enhance decision making by automating and streamlining security operations with integrated tools and processes. The Security Copilot GitHub repository offers AI-powered solutions using machine learning and natural language processing to improve security. These tools help identify vulnerabilities, predict risks, and implement protective measures. Check it out!Securely integrate On-Prem and Self-Hosted VM instances of Splunk with Microsoft Security Copilot
By leveraging Microsoft Entra ID Application Proxy and Azure Application Gateway with Web Application Firewall (WAF), you can securely connect on-premises or self-hosted Splunk instances to Microsoft Security Copilot—enabling seamless log analysis and threat investigation without exposing Splunk to the internet. This approach extends Security Copilot’s reach beyond SaaS applications, broadening the context needed for effective investigations across hybrid environments.
