Take Flight with Microsoft Security Copilot Flight School
Greetings pilots, and welcome to another pioneering year of AI innovation with Security Copilot. Find out how your organization can reach new heights with Security Copilot through the many exciting announcements on the way at both Microsoft Secure and RSA 2025. This is why now is the time to familiarize yourself and get airborne with Security Copilot. Go to School Microsoft Security Copilot Flight School is a comprehensive series charted to take students through fundamental concepts of AI definitions and architectures, take flight with prompting and automation, and hit supersonic speeds with Logic Apps and custom plugins. By the end of the course, students should be equipped with the requisite knowledge for how to successfully operate Security Copilot to best meet their organizational needs. The series contains 11 episodes with each having a flight time of around 10 minutes. Security Copilot is something I really, really enjoy, whether I’m actively contributing to its improvement or advocating for the platform’s use across security and IT workflows. Ever since I was granted access two years ago – which feels like a millennium in the age of AI – it’s been a passion of mine, and it’s why just recently I officially joined the Security Copilot product team. This series in many ways reflects not only my passion but similar passion found in my marketing colleagues Kathleen Lavallee (Senior Product Marketing Manager, Security Copilot) Shirleyse Haley (Senior Security Skilling Manager), and Shateva Long (Product Manager, Security Copilot). I hope that you enjoy it just as much as we did making it. Go ahead, and put on your favorite noise-cancelling headphones, it’s time, pilots, to take flight. Log Flight Hours There are two options for watching Security Copilot Flight School: either on Microsoft Learn or via the Youtube Playlist found on the Microsoft Security Youtube Channel. The first two episodes focus on establishing core fundamentals of Security Copilot platform design and architecture – or perhaps attaining your instrument rating. The episodes thereafter are plotted differently, around a standard operating procedure. To follow the ideal flight path Security Copilot should be configured and ready to go – head over to MS Learn and the Adoption Hub to get airborne. It’s also recommended that pilots watch the series sequentially, and be prepared to follow along with resources found on Github, to maximize learning and best align with the material. This will mean that you’ll need to coordinate with a pilot with owner permissions for your instance to create and manipulate the necessary resources. Episode 1 - What is Microsoft Security Copilot? Security is complex and requires highly specialized skills to face the challenges of today. Because of this, many of the people working to protect an organization work in silos that can be isolated from other business functions. Further, enterprises are highly fragmented environments with esoteric systems, data, and processes. All of which takes a tremendous amount of time, energy, and effort just to do the day-to-day. Security Copilot is a cloud-based, AI-powered security platform that is designed to address the challenges presented by complex and fragmented enterprise environments by redefining what security is and how security gets done. What is AI, and why exactly should it be used in a cybersecurity context? Episode 2 - AI Orchestration with Microsoft Security Copilot Why is The Paper Clip Pantry a 5-star restaurant renowned the world over for its Wisconsin Butter Burgers? Perhaps it’s how a chef uses a staff with unique skills and orchestrates the sourcing of resources in real time, against specific contexts to complete an order. After watching this episode you’ll understand how AI Orchestration works, why nobody eats a burger with only ketchup, and how the Paper Clip Pantry operates just like the Security Copilot Orchestrator. Episode 3 – Standalone and Embedded Experiences Do you have a friend who eats pizza in an inconceivable way? Maybe they eat a slice crust-first, or dip it into a sauce you never thought compatible with pizza? They work with pizza differently, just like any one security workflow could be different from one task team, or individual to the next. This philosophy is why Security Copilot has two experiences – solutions embedded within products, and a standalone portal – to augment workflows no matter their current state. This episode will begin covering those experiences. Episode 4 – Other Embedded Experiences Turns out you can also insist upon putting cheese inside of pizza crust, or bake it thick enough as to require a fork and knife. I imagine, it’s probably something Windows 95 Man would do. In this episode, the Microsoft Entra, Purview, Intune, and Microsoft Threat Intelligence products showcase how Security Copilot advances their workflows within their portals. Beyond baking in the concepts of many workflows, many operators, the takeaway from this episode is that Security Copilot works with security adjacent workflows – IT, Identity, and DLP. Episode 5 – Manage Your Plugins Like our chef in The Paper Clip Pantry, we should probably define what we want to cook, what chefs to use, and set permissions for those that can interact within any input or output from the kitchen. Find out what plugins add to Security Copilot and how you can set plugin controls for your team and organization. Episode 6 – Prompting Is this an improv lesson, or a baking show? Or maybe if you watch this episode, you’ll learn how Security Copilot handles natural language inputs to provide you meaningful answers know as responses. Episode 7 – Prompt Engineering With the fundamentals of prompting in your flight log, it’s time to soar a bit higher with prompt engineering. In this episode you will learn how to structure prompts in a way to maximize the benefits of Security Copilot and begin building workflows. Congrats, pilot, your burgers will no longer come with just ketchup. Episode 8 – Using Promptbooks What would it look like to find a series of prompts and run them, in the same sequence with the same output every time? You guessed it, a promptbook, a repeatable workflow in the age of AI. See where to access promptbooks within the platform, and claw back some of your day to perfect your next butter burger. Episode 9 – Custom Promptbooks You’ve been tweaking your butter burger recipe for months now. You’ve finally landed at the perfect version by incorporating a secret nacho cheese recipe. The steps are defined, the recipe perfect. How do you repeat it? Just like your butter burger creation, you might discover or design workflows with Security Copilot. With custom promptbooks you can repeat and share them across your organization. In this episode you’ll learn about the different ways Security Copilot helps you develop your own custom AI workflows. Episode 10 – Logic Apps System automation, robot chefs? Actions? What if customers could order butter burgers with the click of a button, and the kitchen staff would automatically make one? Or perhaps every Friday at 2pm a butter burger was just delivered to you? Chances are there are different conditions across your organization that when present requires a workflow to being. With Logic Apps, Security Copilot can be used to automatically aid workflows across any system a Logic App can connect to. More automation, less mouse clicking, that’s a flight plan everyone can agree on. Episode 11 – Extending to Your Ecosystem A famed restaurant critic stopped into the The Paper Clip Pantry butter burger, and it’s now the burger everyone is talking about. Business is booming and it's time to expand the menu – maybe a butter burger pizza, perhaps a doughnut butter burger? But you’ll need some new recipes and sources of knowledge to achieve this. Like a food menu the possibilities of expanding Security Copilot’s capabilities are endless. In this episode learn how this can be achieved with custom plugins and knowledgebases. Once you have that in your log, you will be a certified Ace, and ready to take flight with Security Copilot. Take Flight I really hope that you not only learn something new but have fun taking flight with the Security Copilot Flight School. As with any new and innovative technology, the learning never stops, and there will be opportunities to log more flight hours from our expert flight crews. Stay tuned at the Microsoft Security Copilot video hub, Microsoft Secure, and RSA 2025 for more content in the next few months. If you think it’s time to get the rest of your team and/or organization airborne there’s check out the Security Copilot adoption hub to get started: aka.ms/SecurityCopilotAdoptionHub Other Resources Our teams have been hard at work building solutions to extend Security Copilot, you can find them on our community Github page found at: aka.ms/SecurityCopilotGitHubRepo To stay close to the latest in product news, development, and to interact with our engineering teams, please join the Security Copilot CCP to get the latest information: aka.ms/JoinCCPEmpowering Security Copilot with NL2KQL: Transforming Natural Language into Insightful KQL queries
By leveraging NL2KQL, a powerful framework that translates natural language into KQL queries, Security Copilot makes querying in KQL as intuitive as a conversation. In this article, we’ll explore the story behind NL2KQL, its potential to transform security operations, and why it matters for the future of cybersecurity.Next-Gen Device Incident Investigation & Threat Hunting with Custom Plugins
The Security Copilot custom plugin empowers you to extend Security Copilot functionalities beyond the preinstalled and third-party plugins. This blog introduces two custom plugins that you can install and use in your environment. An incident investigation case study will be used to demonstrate the features of these two custom plugins. Additionally, a step-by-step guide will walk you through the setup process, which only takes a few clicks. The first custom plugin, “Custom Plugin Defender Device Investigation”, provides the following skills: Title: File - Files Downloaded Description: Lists files downloaded to this device in specific timeframe within past 30 days. Title: File - Last 15 Days Files Downloaded Description: Lists files downloaded to this device in the last 15 days. Title: File - Any Device Events Related To This File Description: Display device events that include the filename, in specific timeframe. Title: File - Sensitive Files Events Description: Lists sensitive files events on this device in the last 10 days. Title: File - File Origin Description: Display the origin or source of the file, in past 30 days. Title: Process - Process Executions Summary Description: Summary of process executions on this device in specific timeframe. Title: Process - Detailed Process Executions Description: Detailed all process execution events on device within a brief period, e.g. an hour. Title: Process - Detailed Process Events Description: Detailed specific process execution events on device within a defined time frame. Title: Lateral Movement - RDP To Device Description: Inbound RDP connection to this device in a specific timeframe. Title: Lateral Movement - Logon To Device Description: Logon events from other devices to this device in a specific timeframe. Title: Lateral Movement - Logons To Device In Last 10 Days Description: Logon events from other devices to this device in the last 10 days. Title: Network - Outbound Network Events Description: Device outbound network events, including attempts and failed connections. Title: Network - Inbound Network Events Description: Device inbound network events and attempts in a specific timeframe. Title: Network - Device Listening Ports Description: Displays device listening ports in specific timeframe. Title: Device Events - Scheduled Task Events Description: Scheduled task events seen on a device in a specific timeframe. Title: Device Events - User Account Events Description: User account events seen on a device in a specific timeframe. Title: Device Events - User Account Added Or Removed From Local Group Description: User account added or removed from local group in a specific timeframe. Title: Suspicious Activities - ASR Rules Triggered Description: ASR rules that were triggered on this device in the past 7 days. Title: Suspicious Activities - ASMSI Script Detection Description: Script detection from Windows Antimalware Scan Interface (AMSI) in past 7 days. Title: Suspicious Activities - Exploit Guard Events Description: Exploit Guard events detected on this device in past 7 days. Title: Suspicious Activities - Network Protection Events Description: Network Protection events triggered on this device in the past 7 days. Title: Suspicious Activities - Device Tampering Attempts Description: Possible tampering attempts on this device in the past 7 days. The second custom plugin, “Custom Plugin Defender Device Info”, offers specific device information often needed during an investigation. Its skills include: Title: Device OS Information Description: Latest device OS information with the device name as the input. Title: Device Current and Past IPs Description: The current and past IPs assigned to this device in the last 10 days. Title: Device Users and Login Counts Description: List users logged onto this device and the number of times, within the last 10 days. Title: Device Alert Information Description: Alerts observed on this device in the last 30 days. Title: Device Installed Applications Description: Currently installed applications on this device. Title: Device Vulnerability Information Description: Vulnerabilities identified on this device. Title: Device Critical Vulnerabilities Description: Vulnerability with CVSS score 7 or higher, or exploit is publicly available. Both custom plugins are available for download from the Security Copilot GitHub repository at this link. Step-by-step guides on how to install the custom plugin will be covered later in this blog. Let's start by demonstrating some of the capabilities of the two custom plugins through a case study of a Microsoft Defender XDR incident. For this incident, the Security Copilot incident summary reveals that the threat actor used a credential phishing attack to gain initial access. Over the course of the incident, several instances of lateral movement, credential access, and privilege escalation were detected, impacting users and devices across the network. Key activities included the use of tools like Mimikatz and Rubeus, suspicious remote sessions, and evidence of system manipulation. From the Security Copilot incident summary, you learn that the attack started when user “jonaw” clicked on a malicious URL in an email. Following that, a suspicious remote session was detected on device “vnevado-win10v”. To investigate the suspicious remote session on the device, one way is to leverage the “Lateral Movement – Logon To Device” skill from the “Custom Plugin Defender Device Investigation” plugin in Security Copilot's standalone mode. This skill presents the logon events that occurred on the device within the specified timeframe. The logon events include console logons, Remote Desktop logons, remote registry logons, scheduled task logons, and more. You can invoke this skill by navigating to the System Capabilities menu option from the prompt bar. To get to the System Capabilities menu option, select the Prompts option from the prompt bar, as shown next. Then the System Capabilities menu option appears. This skill is located under the plugin named “CUSTOM PLUGIN DEFENDER DEVICE INVESTIGATION”, as shown next. Once this skill is selected, you will need to fill in three input fields: the device name, start time, and end time. For this case study, the alert for the suspicious remote session was triggered for device vnevado-win10v, occurring at approximately 9:42 UTC on November 22 nd 2024. For the investigation, let's set the start time to 2024-11-22 9:30 UTC and the end time to 9:50 UTC, as shown in the next screenshot. The next screenshot demonstrates that Security Copilot executes this skill. Using the “Export to Excel” option in the Copilot response, you can download then manually review the logon events. Upon inspection, it is discovered that for device vnevado-win10v, there is a long list of logon events involving different user accounts within the 20-minute time frame. A screenshot showing a portion of the logon events is displayed next. You can then ask Security Copilot with this prompt: “Can you review the previous output of the logon events for the device vnevado-win10v between 2024-11-22 09:30 and 2024-11-22 09:50, summarize the logon events and also point out anything suspicious”. The next screenshot displays the Security Copilot prompt along with the beginning of its response. The logon event summary provided by Security Copilot is thorough but a bit long. At the end, it includes the identified suspicious logon activities: There are several instances where logon attempts are followed by successful logons within milliseconds, which could indicate automated or scripted logon attempts. There are 10 logon events with an "Unknown" logon type, which is unusual and may warrant further investigation. The account debrab has one logon event where it is marked as a local admin, which should be verified for legitimacy. For your reference, the last section of the Security Copilot’s logon event summary is shown in the next screen capture. After reviewing the logon event summary for device vnevado-win10v, let’s find out who might be the owner of this device. The “Device Users and Login Counts” skill from the “Custom Plugin Defender Device Info” plugin provides a summary of how many times each user has logged into the device over the past 30 days. Typically, the user with the most logins is likely the device owner. Once the skill is executed for device vnevado-win10v, Security Copilot reports that “user jonaw has logged onto the device vnevado-win10v a total of 189 times in the last 30 days”, as shown in the next screen capture. This helps to identify user “jonaw” as the likely device owner, which in turn makes user “debrab” appear even more suspicious. Let’s go back to the detailed logon events provided by Security Copilot earlier and take another look at user account “debrab”. The next screenshot shows the logon events for device vnevado-win10v, filtered to display only those associated with the user “debrab”. One notable observation is that the logon type for user “debrab” is either batch or unknown, which appears suspicious as well, especially with one batch logon with local admin privilege. What is a batch logon type? You can ask Security Copilot for more insights. The next screenshot displays Copilot’s responses, which explains that a batch logon type is typically used for scheduled tasks. The batch logon seems odd in this case. One of Security Copilot's key features is its ability to distinguish between normal and anomalous behavior in IT operations. In this case, let’s ask Security Copilot whether it’s common for someone with local admin privilege to log on to a device through a batch logon. As seen in the previous screenshot, Security Copilot points out that the batch logon is unusual, as it is typically used for scheduled tasks or automated processes, not for interactive sessions by administrators. Security Copilot’s response further confirms that the batch logon events with user account “debrab” are suspicious. This information and the other Security Copilot observations can assist you in identifying the suspicious remote session detected on device “vnevado-win10v”. The incident summary generated by Security Copilot not only mentions the detection of a suspicious remote session on device vnevado-win10v, but also reports the presence of suspicious files, including mimikatz.exe, rubeus.exe, xcopy.exe, and powershell.exe. The incident summary snippet is displayed next for reference. Let’s now examine what occurred on the device involving these suspicious files. A quick and easy way to start the investigation is to check for files downloaded to the device and reviewing the device's process execution events around the time of the incident to identify anything suspicious. Manually checking for downloaded files and examining process execution events can be time-consuming and labor-intensive. However, with the help of Security Copilot, these tasks can be performed more quickly and efficiently. The “File - Files Downloaded” skill from the “Custom Plugin Defender Device Investigation” plugin can be used to quickly identify files that were downloaded onto a device within a specific time period. Then, the “Process - Process Executions Summary” skill from the same Security Copilot plugin can be used to list the processes that executed on the device during the same timeframe. You can then ask Security Copilot to analyze these processes to identify anything suspicious. After the “File - Files Downloaded” skill executes, Security Copilot identifies a file named DomainDominance198.zip was downloaded to device vnevado-win10v. Another thing to keep in mind is that not all the information from the Copilot findings is directly visible in the Security Copilot console. You can expand the output result within the console or export the findings to Excel for a clearer view of the additional details. For this investigation, you can then more thoroughly review the URL from which the file was downloaded, verify the file location through its folder path, and locate the user account associated with the download. The next screenshot displays these additional details seen in the Excel spreadsheet. Then, the “Process - Process Executions Summary” skill provides a list of processes executed on the same device, vnevado-win10v, during the same period. Instead of manually reviewing all 128 processes, you can ask Security Copilot to analyze the processes and flag any suspicious ones. In addition, it's worth mentioning earlier in the investigation, leveraging the Microsoft Entra plugin, Security Copilot reports that user account “jonaw” belongs to Jonathan Wolcott, an account executive in the Sales department. With this information, let’s ask Security Copilot to identify any process execution that should typically not be carried out by someone outside of the IT department. Here is the Security Copilot prompt you can use: User “jonaw” is an account executive in the sales department, with this information, can you identify any processes that typically should not be carried out by someone outside of the IT department? Security Copilot then identifies six suspicious processes and provides its reasoning along the way. Once again, you can export the Security Copilot findings to Excel for a more thorough review. The next screenshot displays the results in Excel, with a more readable format. Now that a few more suspicious processes have been identified, let's revisit the downloaded file, DomainDominance198.zip, to see if more details can be uncovered. The skill, “File - Any Device Events Related To This File”, is part of the “Custom Plugin Defender Device Investigation” plugin in Security Copilot. It is designed to identify any device events or activities related to a specific file. It uses the filename as a keyword to filter and display only the device events containing this keyword within a defined time period. For this security incident, let's use this skill to search for device events containing the name of the downloaded file, DomainDominance198. Upon reviewing the Security Copilot response exported to Excel, you can see that a new file, DomainDominance198.ps1, has been created in the same directory as DomainDominance198.zip. In addition, the “File - File Origin” skill in the “Custom Plugin Defender Device Investigation” plugin provides details about a file's origin or source. It shows where the file came from, and any associated file or connection linked to it. In this case, as shown in the next screenshot, Security Copilot reveals that the file DomainDominance198.zip was downloaded from a specific URL. And that the file DomainDominance198.ps1 is associated with file DomainDominance198.zip, as shown next. The additional details in Security Copilot’s responses highlight the exact association, indicating that the File Origin Referrer URL for DomainDominance198.ps1 is DomainDominance198.zip, as shown in the next screen capture. With these insights, let's use another Security Copilot skill to conduct a more in-depth examination of PowerShell execution events on device vnevado-win10v. The skill, “Process - Detailed Process Events”, is also part of the “Custom Plugin Defender Device Investigation” plugin. It retrieves detailed process execution events, including process command line information and the parent process execution details, for the specified process on a given device within a defined time frame. When this skill is invoked, it requires four mandatory fields to be filled, as shown next. Security Copilot then displays the PowerShell execution events identified on device vnevado-win10v within the specified timeframe of 2024-11-22 09:30 to 2024-11-22 10:30, as shown next. From a more condensed text view of the responses from Security Copilot, a range of unusual or potentially harmful behaviors can be observed in the next screenshot. Some of these suspicious events are highlighted in yellow or displayed in bold in the next screenshot. The process execution events retrieved include command line details and parent process, therefore you are able to see both the PowerShell execution and processes launched with PowerShell as the parent process. The suspicious processes, such as mimikatz.exe, Rubeus.exe, xcopy.exe, PxExec.exe, and others mentioned in the Security Copilot incident summary, are identified here, allowing you to quickly recognize the correlation. Additionally, you can ask Security Copilot to assist you in reviewing the suspicious events. For instance, immediately after the xcopy command was used to copy the file “Rubeus.exe” to the remote device vnevado-win10b, a subsequent command involving “PsExec.exe” is observed in the detailed PowerShell execution events presented earlier by Security Copilot. The two command lines are shown in the next screen capture. Consulting with Security Copilot reveals that “PsExec.exe” executed a command remotely on the device vnevado-win10b. This command launched “Rubeus.exe” to dump Kerberos tickets for the user “nestorw” and saved the output to C:\Temp\AdminTicket.txt. Security Copilot notes that this action indicates credential dumping and potential lateral movement within the network. The next screenshot shows the prompt along with part of the responses from Security Copilot. As there are many other potentially harmful behaviors also observed in the detailed PowerShell execution events presented by Security Copilot earlier, you can submit each of these suspicious events to Security Copilot and ask for insights. Downloading and Installing the Custom Plugins The configuration files for the custom plugins can be downloaded from this link. Once you have the configuration file (in YAML format), here are the steps to upload and install it to your Security Copilot instance. Step 1: Select the Sources icon in the Prompt bar. Step 2: Scroll to the bottom of the Manage Sources page, within the Custom section, you'll find the "Add a plugin" option. Step 3: Click on “Add plugin” and then choose “Copilot for Security plugin”, as illustrated in the next screenshot. Step 4: Click on “Upload file” to install configuration file, which is in YAML format. Step 5: Click on Add. And voilà, the new custom plugin appears along with other plugins in the Manage sources section, as seen in the screen capture next. Now you can start using the custom plugins and they will appear in the “System Capabilities” section.Accelerating the Anomalous Sign-Ins detection with Microsoft Entra ID and Security Copilot
Overview In today’s complex threat landscape, identity protection is critical for securing organizational assets. A common sign of compromise is user activity indicating connections from multiple locations separated by over X- kilometers within a short period. Such events might represent risky sign-ins, requiring Security Analysts to determine whether they are true positives (indicating malicious activity) or false positives (such as misconfigured settings or benign anomalies). To enhance efficiency and accelerate the investigation process, organizations can leverage AI tools like Microsoft Security Copilot. By integrating Security Copilot with Microsoft Entra ID mainly AADUserRiskEvent and developing custom Promptbooks, organizations can investigate risky sign-ins, reduce manual workloads, and enable proactive decision-making to boost SOC efficiency in such scenarios. Use Case: Challenge and Solution Challenge Organizations face significant challenges in investigating and triaging identity protection alerts for sign-in anomalies, especially when users appear to log in from geographically disparate locations within hours. These challenges include: Volume of Alerts: Large organizations generate numerous risky sign-in events daily. False Positives: Legitimate activities, such as VPN connections or device relocations, may be flagged. Resource Constraints: Security teams must efficiently prioritize true positives for investigation. Solution Using Microsoft Security Copilot with a tailored Promptbook, Security Analysts can automate the initial triage process and focus on meaningful insights. This approach combines data querying, AI-driven analysis, and actionable recommendations to improve investigation workflows. Promptbook Structure The custom Promptbook comprises two key prompts: 1. First Prompt: Data Retrieval from Defender XDR via KQL Query This query retrieves users flagged for risky sign-ins within a 1-day window, focusing on events where the distance between locations exceeds 500 kilometers within 3 hours as example. Retrieve Defender XDR information using this KQL query: let riskyusers = AADUserRiskEvents | where TimeGenerated is greater than or equal ago(<TimeIntervalByDays>) | project UserPrincipalName, TimeGenerated, Location, IpAddress, RiskState, Id, RiskEventType; riskyusers | join kind=inner ( riskyusers | extend TimeGenerated1 = TimeGenerated, LocationDetails1 = Location ) on UserPrincipalName | where TimeGenerated is less than TimeGenerated1 and datetime_diff('hour', TimeGenerated1, TimeGenerated) is less than or equal <ConnectionsInterbalByHrs> | extend latyy = Location.geoCoordinates.latitude | extend longy= Location.geoCoordinates.longitude | extend latyy1 = LocationDetails1.geoCoordinates.latitude | extend longy1 = LocationDetails1.geoCoordinates.longitude | extend distance = geo_distance_2points(todouble(Location.geoCoordinates.latitude), todouble(Location.geoCoordinates.longitude), todouble(LocationDetails1.geoCoordinates.latitude), todouble(LocationDetails1.geoCoordinates.longitude)) | where distance is greater than or equal <SepratedDistanceByKM> | summarize arg_max(TimeGenerated, *) by Id | where RiskState is not equal @"dismissed" | project UserPrincipalName, TimeGenerated, IpAddress, Location, TimeGenerated1, IpAddress1, LocationDetails1, RiskEventType, distance Please make sure to set value for the following input parameters: <TimeIntervalByDays> example: 7d <ConnectionsInterbalByHrs> example: 3 <SepratedDistanceByKM> example: 5000 2. Second Prompt: AI Analysis for Patterns and Recommendations This prompt enables Security Copilot to analyze the retrieved data, identify patterns (e.g., recurring IP addresses or anomalous locations), and suggest further investigative steps and mitigative actions. /AnalyzeSecurityData Provide your insights as Security Analyst about what anomalies or similarity patterns can you identify. Provide a list of prompts for Security Copilot to investigate further and a list of recommendations. Use as input security data the information in the table from the previous prompt in this session. Automating the Process with Azure Logic Apps Organizations can further streamline the process by automating risky sign-in investigations using Azure Logic Apps. Here’s how: Create a Logic App: Set up a Logic App in the Azure portal. Trigger Configuration: Use a recurring schedule trigger to run the investigation daily. Integration with Security Copilot: Configure the Logic App to execute the Security Copilot’s Promptbook. Automate prompts for insights and recommendations. Notification Mechanism: Send results via email to the SOC team or log them in a ticketing system for further action. Note: to send only the result of the last prompt in the promptbook, use: last(body('Run_a_Security_Copilot_promptbook')?['evaluationResults'])['evaluationResultContent'] Benefits of the Approach Efficiency: Reduces manual efforts by automating repetitive tasks. Accuracy: AI analysis helps filter out false positives and prioritize true positives. Scalability: Easily extendable for other security use cases. Fast triage: Enables SOC teams to act quickly and decisively. Conclusion Incorporating Microsoft Security Copilot with a custom Promptbook into daily operations empowers Security Analysts to efficiently investigate and triage risky sign-in events. By automating processes through Azure Logic Apps, organizations can maintain a proactive security posture and better protect their identities and assets. Try it out: If your organization is looking to enhance its SOC capabilities, consider implementing this solution to harness the power of AI for identity protection. The Promptbook added to the github Security Copilot repo : Click hereHunt for identity-based threats with Security Copilot and Microsoft Sentinel
Enter Microsoft Sentinel and Security Copilot, a powerful duo that brings great value to your security operations. Microsoft Sentinel's User and Entity Behavior Analytics (UEBA) capabilities are designed to distill anomalies from vast amounts of raw data, providing clear and actionable insights. By leveraging advanced machine learning algorithms, Sentinel UEBA can identify unusual patterns and behaviors that may indicate potential security threats, allowing for proactive threat detection and response. On its part, Security Copilot further enhances this capability by offering investigators and threat hunters a head start by analyzing the inputs from Sentinel UEBA and helping the analyst prioritize their investigation or hunting efforts. This streamlines the investigation process, enabling security teams to proactively and quickly identify potential threats. In this blog we will showcase how Microsoft Sentinel UEBA can narrow down a set of anomalies associated with high blast radius users and how Security Copilot speeds up the investigation process, offering AI-enriched insights and recommendations through a Security Copilot Promptbook. This Promptbook brings together insights from 1 st party (built-in Microsoft plugins), 3 rd party plugins from ISVs and custom plugins leveraging the extensibility of the Security Copilot platform. The Microsoft Sentinel UEBA data is brought into the Security Copilot session via custom KQL plugin. Summary of requirements # Artefact Link 1 High Blast Radius User investigation custom plugin Security-Copilot/Plugins/Community Based Plugins/Microsoft Sentinel Custom Plugin Scenarios/High Blast Radius User investigation at plugins-blastradius · inwafula/Security-Copilot 2 AbuseIPDB plugin AbuseIPDB and Microsoft Security Copilot | Microsoft Learn 3 Microsoft Intune Microsoft Copilot in Intune features overview | Microsoft Learn 4 Cybersixgill Threat Intelligence Cybersixgill and Microsoft Security Copilot | Microsoft Learn 5 Rare process running as a service detection Security-Copilot/Plugins/Community Based Plugins/Microsoft Defender XDR Custom Plugin Scenarios/RareProcess at plugins-blastradius · inwafula/Security-Copilot 6 Promptbook Security-Copilot/Promptbook samples/High Blast Radius User investigation.md at main · Azure/Security-Copilot Below is a snapshot of the promptbook we shall step through and call out the highlights: The first prompt serves to make the connection between Security Copilot and Sentinel by executing a custom KQL plugin that will identify high blast radius users as computed by Sentinel UEBA on the user's relative position within Entra ID as well as the Azure roles assigned to the user. The higher the user is in the organization and the more impactful their Azure permissions are, the higher the blast radius. In this run we see that Copilot has come back with 17 users who fit the description of being “high blast radius” users, however, we want to narrow down to which particular user we should prioritize. Once this information is brought into the Security Copilot session, we can begin to apply the power of AI over it and glean some insights that will help us quickly prioritize which identity to focus on and get useful guidance along the way. The output below is in response to the second prompt which tasks Security Copilot with analyzing all the users that came back from the first prompt as being high blast radius users but go a step further and identify which specific one we should prioritize: The third prompt gathers reputation information about the IP used for anomalous activities, the fourth examines the user's device, while the fifth extends the investigation beyond the Entra ID tenant into the deep and dark web using Cybersixgill, a Bitsight company plugin for a comprehensive view of the threat exposure landscape. *Note: This is a simulated breach for demo purposes, not an actual one The 6 th prompt reaches into Intune to obtain information about the compliance state of the device while the 7 th uses data from Microsoft Defender XDR to check for any unusual processes that may have been executed on the device, additional context needed to paint a holistic picture of the gravity of the incident. The final prompt brings it all together by summarizing the investigation, suggesting a verdict, recommending follow-up actions and providing an explanation of Copilot’s reasoning for both. Below is the final output from this Promptbook’s run: Jan 6, 3:39 PM Investigation Summary User: Ismat Bekarevich (OPS) User Principal Name: isbe54@woodgrove.ms Blast Radius: High Recent Anomalous Activity: Logon from IP address 2601:xxxxx:7273:958b:e655:xxxx:b98c (City, United States) Group Memberships: AWS Admins, On-prem Admins, Azure ATP contosodemos2 Viewers, AAD DC Administrators Assigned Roles: Global Administrator Risk State: Remediated Credentials Compromised: Yes (Breach Date: 2024-11-26, Source: telegram) IP Address: 2601:xxxxx:c182:7273:958b:e655:xxxx:b98c Abuse Confidence Score: 0 (No known malicious activity) ISP: Comcast Cable Communications, LLC Country: United States Devices Assigned CPC-isbe5-L1BOV o Compliance Status: Compliant o Compliance Policies: Default Device Compliance Policy, Defender for Endpoint - Device Risk o Rare Processes Executed: None over the past 7 days DESKTOP-VSA7FBL o Compliance Status: Noncompliant o Compliance Issues: Default Device Compliance Policy, Defender for Endpoint - Device Risk Demo’s MacBook Pro (2) o Compliance Status: Compliant WoodgroveVM02 o Compliance Status: Compliant Verdict Threat Hunting Required: Yes Reasons: High Blast Radius: The user has significant access and potential impact within the organization. Compromised Credentials: The user's credentials have been compromised, posing a potential security risk. Noncompliant Device: One of the user's devices (DESKTOP-VSA7FBL) is noncompliant with critical security policies. Suggested Follow-Up Actions Monitor User Activity: Continuously monitor the user's activities for any further anomalies or suspicious behavior. Review and Remediate Noncompliant Device: Investigate and address the compliance issues on the device DESKTOP-VSA7FBL. Credential Reset: Perform a credential reset for the user to mitigate the risk of compromised credentials. Conditional Access Policies: Ensure that appropriate conditional access policies are in place to limit access from potentially risky locations or devices. Security Awareness Training: Provide the user with security awareness training to prevent future credential compromises. Conclusion Together, Security Copilot and Microsoft Sentinel provide a comprehensive security solution that empowers organizations to stay ahead of identity-based attacks, among others. By combining the strengths of both platforms, you can achieve greater efficiencies in your security operations. Try it out and give us your feedback and recommendations for further improvement. Additional resources Microsoft Security Copilot | Microsoft Security Microsoft Sentinel - Cloud-native SIEM Solution | Microsoft Azure Microsoft Sentinel UEBA reference | Microsoft Learn AbuseIPDB - IP address abuse reports - Making the Internet safer, one IP at a time Advanced Dark Web Threat Intelligence Platform | Cybersixgill Microsoft Digital Defense Report: 600 million cyberattacks per day around the globeKQL Migrator powered by Microsoft Security Copilot
Overview A couple of weeks ago, Hesham and Hiten attended an internal Global Blackbelt summit in Redmond. Unfortunately, we encountered bad weather due to a “Bomb Cyclone”. Consequently, many people within the Washington State area were left without essential services such as electricity and running water. The Microsoft Campus was converted into a temporary relief center, leading to the rescheduling of our sessions for the day. Fortunately, we were staying at a hotel equipped with a backup generator, running water, and heating. We borrowed a whiteboard and spent the next few hours discussing the migration from 3 rd party solutions and mainly different query-based languages to Microsoft Sentinel – Defender XDR and KQL (Kusto Query Language) SIEM migrations are always a challenging process that requires meticulous planning and a thorough understanding of the existing setup in the legacy SIEM and what needs to be migrated to the modern SIEM. Our primary discussion point was how we can efficiently assist organizations in translating detection rules from query-based languages like AQL (Ariel Query Languages) to KQL (Kusto Query Language) and to help organizations mainly SOC teams to convert their YARA rules, STIX II and OpenIOC intel to KQL format We identified three options: Convert AQL to KQL Convert YARA to KQL Convert STIX II and OpenIOC intel to KQL All three options require specialized skills and can be cumbersome to complete. For example, the user would need to understand precisely how AQL maps to KQL and second option require through knowledge of the syntax used in YARA generation. Both of these are niche skills, the same also applies to STIX II. One thing to point out here that we are not experts in query language like AQL and YARA Rules, we know how to export the detection rules in CVE format, our combined knowledge of AQL is also limited and would be considered basic at best. We quickly realized that AQL is similar to SQL and consequently KQL. We had the following AQL Code: Select sourceip, destinationip, "Process Name" FROM events WHERE "Process Name" IMATCHES '.*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ie4uinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*|' GROUP BY "Process Name",sourceip LAST 3 DAYS This query aims to track the use of specific executables for administrative or potentially malicious activities. By grouping results by process name and source IP, it helps detect patterns or anomalies that may indicate security incidents or policy violations. Using Microsoft Security Copilot What if we could get Security Copilot to firstly explain the code and then convert it to Kusto? We used the following prompt: /askGPT I am planning a SIEM migration from AQL query based to Microsoft Sentinel KQL. As AQL expert can you create a detailed summary that explains the following AQL query in square brackets [ Select sourceip, destinationip, "Process Name" FROM events WHERE "Process Name" IMATCHES '.*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ie4uinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*|' GROUP BY "Process Name",sourceip LAST 3 DAYS] Below is the OUTPUT Detailed Summary of AQL Query Breakdown Query Overview The provided AQL query is designed to search for specific process names within event logs and return the source and destination IP addresses associated with those processes. Here's a detailed breakdown of the query: AQL Query Breakdown Select Clause: sourceip: The source IP address from which the event originated. destinationip: The destination IP address to which the event was directed. "Process Name": The name of the process involved in the event. From Clause: events: The dataset or table from which the query is retrieving data. Where Clause: "Process Name" IMATCHES '.*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ie4uinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*': This condition filters events to include only those where the process name matches any of the specified patterns. The IMATCHES operator is used for case-insensitive regular expression matching. Group By Clause: "Process Name", sourceip: The results are grouped by the process name and source IP address. Time Range: LAST 3 DAYS: The query is limited to events that occurred within the last three days. Summary This AQL query is designed to identify and group events based on specific process names that are often associated with suspicious or malicious activity. By focusing on these processes, the query helps in monitoring and investigating potential security incidents. The results provide insights into the source and destination IP addresses involved, which can be crucial for further analysis and response. The query returns the process name, source IP, and destination IP for events matching the specified process names, grouped by process name and source IP, within the last three days. This next step to see if Security Copilot can help convert the AQL to something that is mapped to Microsoft Sentinel Kusto Query Language: Can you analyze the AQL query above and map it to KQL, it is important to ensure that the mapping directly correlates to the schema in KQL. Can you also show the output of the query in a table. Present the mappings in a table: KQL Query and Output KQL Query Mapping DeviceProcessEvents | where TimeGenerated >= ago(3d) | where InitiatingProcessFileName matches regex @'(?i).*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ie4uinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*' | summarize by InitiatingProcessFileName Query Output InitiatingProcessFileName csc.exe gpupdate.exe microsoftedgeupdate.exe msiexec.exe schtasks.exe sc.exe cscript.exe rundll32.exe makecab.exe route.exe Row Counts Total Rows: 10 Displayed Rows: 10 Product Name Product: Sentinel The extended team highlighted the importance of considering YARA rules for uniformity. Given that expertise in YARA rule generation was lacking, Security Copilot as again consulted: /askGPT Can you explain what a YAYA rule is with an example A YARA rule is a type of rule used in cybersecurity to identify and classify malware samples. YARA, which stands for "Yet Another Recursive Acronym," is a tool aimed at helping malware researchers identify and classify malware. YARA rules are written in a specific syntax and are used to describe patterns that can be found in files or processes. Example of a YARA Rule Here is a simple example of a YARA rule that detects a file containing the string "malicious": rule ExampleRule { meta: description = "This rule detects files containing the string 'malicious'" author = "Security Researcher" date = "2024-12-09" strings: $malicious_string = "malicious" condition: $malicious_string } So we have a base understand of what a YARA rule is and its typical format. The step is can we use Security Copilot to convert the YARA into a KQL Rule: We used the following prompt: As a YARA rule expert, can you take the above KQL query generated and create a KQL rule? rule Suspicious_Process_Execution { meta: description = "Detects execution of potentially suspicious processes" author = "Security Analyst" date = "2023-10-01" strings: $atbroker = /(?i).*atbroker\.exe.*/ $bash = /(?i).*bash\.exe.*/ $bitsadmin = /(?i).*bitsadmin\.exe.*/ $certutil = /(?i).*certutil\.exe.*/ $cmdkey = /(?i).*cmdkey\.exe.*/ $cmstp = /(?i).*cmstp\.exe.*/ $control = /(?i).*control\.exe.*/ $csc = /(?i).*csc\.exe.*/ $cscript = /(?i).*cscript\.exe.*/ $dfsvc = /(?i).*dfsvc\.exe.*/ $diskshadow = /(?i).*diskshadow\.exe.*/ $dnscmd = /(?i).*dnscmd\.exe.*/ $esentutl = /(?i).*esentutl\.exe.*/ $eventvwr = /(?i).*eventvwr\.exe.*/ $expand = /(?i).*expand\.exe.*/ $extexport = /(?i).*extexport\.exe.*/ $extrac32 = /(?i).*extrac32\.exe.*/ $findstr = /(?i).*findstr\.exe.*/ $forfiles = /(?i).*forfiles\.exe.*/ $ftp = /(?i).*ftp\.exe.*/ $gpscript = /(?i).*gpscript\.exe.*/ $hh = /(?i).*hh\.exe.*/ $ie4uinit = /(?i).*ie4uinit\.exe.*/ $ieexec = /(?i).*ieexec\.exe.*/ $infdefaultinstall = /(?i).*infdefaultinstall\.exe.*/ $installutil = /(?i).*installutil\.exe.*/ $makecab = /(?i).*makecab\.exe.*/ $reg = /(?i).*reg\.exe.*/ $print = /(?i).*print\.exe.*/ $presentationhost = /(?i).*presentationhost\.exe.*/ $pcwrun = /(?i).*pcwrun\.exe.*/ $pcalua = /(?i).*pcalua\.exe.*/ $odbcconf = /(?i).*odbcconf\.exe.*/ $msiexec = /(?i).*msiexec\.exe.*/ $mshta = /(?i).*mshta\.exe.*/ $msdt = /(?i).*msdt\.exe.*/ $msconfig = /(?i).*msconfig\.exe.*/ $msbuild = /(?i).*msbuild\.exe.*/ $mmc = /(?i).*mmc\.exe.*/ $microsoft_workflow_compiler = /(?i).*microsoft\.workflow\.compiler\.exe.*/ $mavinject = /(?i).*mavinject\.exe.*/ $vsjitdebugger = /(?i).*vsjitdebugger\.exe.*/ $tracker = /(?i).*tracker\.exe.*/ $te = /(?i).*te\.exe.*/ $sqltoolsps = /(?i).*sqltoolsps\.exe.*/ $sqlps = /(?i).*sqlps\.exe.*/ $sqldumper = /(?i).*sqldumper\.exe.*/ $rcsi = /(?i).*rcsi\.exe.*/ $msxsl = /(?i).*msxsl\.exe.*/ $msdeploy = /(?i).*msdeploy\.exe.*/ $mftrace = /(?i).*mftrace\.exe.*/ $dxcap = /(?i).*dxcap\.exe.*/ $dnx = /(?i).*dnx\.exe.*/ $csi = /(?i).*csi\.exe.*/ $cdb = /(?i).*cdb\.exe.*/ $bginfo = /(?i).*bginfo\.exe.*/ $appvlp = /(?i).*appvlp\.exe.*/ $xwizard = /(?i).*xwizard\.exe.*/ $wsreset = /(?i).*wsreset\.exe.*/ $wscript = /(?i).*wscript\.exe.*/ $wmic = /(?i).*wmic\.exe.*/ $wab = /(?i).*wab\.exe.*/ $verclsid = /(?i).*verclsid\.exe.*/ $syncappvpublishingserver = /(?i).*syncappvpublishingserver\.exe.*/ $scriptrunner = /(?i).*scriptrunner\.exe.*/ $schtasks = /(?i).*schtasks\.exe.*/ $sc = /(?i).*sc\.exe.*/ $runscripthelper = /(?i).*runscripthelper\.exe.*/ $runonce = /(?i).*runonce\.exe.*/ $rundll32 = /(?i).*rundll32\.exe.*/ $rpcping = /(?i).*rpcping\.exe.*/ $replace = /(?i).*replace\.exe.*/ $regsvr32 = /(?i).*regsvr32\.exe.*/ $regsvcs = /(?i).*regsvcs\.exe.*/ $register_cimprovider = /(?i).*register-cimprovider\.exe.*/ $regedit = /(?i).*regedit\.exe.*/ $regasm = /(?i).*regasm\.exe.*/ condition: any of ($atbroker, $bash, $bitsadmin, $certutil, $cmdkey, $cmstp, $control, $csc, $cscript, $dfsvc, $diskshadow, $dnscmd, $esentutl, $eventvwr, $expand, $extexport, $extrac32, $findstr, $forfiles, $ftp, $gpscript, $hh, $ie4uinit, $ieexec, $infdefaultinstall, $installutil, $makecab, $reg, $print, $presentationhost, $pcwrun, $pcalua, $odbcconf, $msiexec, $mshta, $msdt, $msconfig, $msbuild, $mmc, $microsoft_workflow_compiler, $mavinject, $vsjitdebugger, $tracker, $te, $sqltoolsps, $sqlps, $sqldumper, $rcsi, $msxsl, $msdeploy, $mftrace, $dxcap, $dnx, $csi, $cdb, $bginfo, $appvlp, $xwizard, $wsreset, $wscript, $wmic, $wab, $verclsid, $syncappvpublishingserver, $scriptrunner, $schtasks, $sc, $runscripthelper, $runonce, $rundll32, $rpcping, $replace, $regsvr32, $regsvcs, $register_cimprovider, $regedit, $regasm) } KQL Rule Based on YARA Rule DeviceProcessEvents | where Timestamp >= ago(3d) | where InitiatingProcessFileName matches regex @'(?i).*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ieuinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*' | summarize by InitiatingProcessFileName We now have a method to efficiently convert a detection rule from a legacy SIEM to one that is compatible with Microsoft Sentinel . The next step is understanding how a user can implement this in Sentinel. It is important to consider that the user may need to acquaint themselves with the new SIEM or hire a contractor. Since a contractor wasn’t available, given the circumstances, we decided to ask Security Copilot: Can you create a change control document that shows the steps required to create the detection rule for the KQL query generated above in sentinel. Also include the required level of access to make such a change. Ensure rollback is included. Change Control Document: Creating a Detection Rule in Microsoft Sentinel Change Request Details Change Request ID: [To be assigned] Change Title: Create Detection Rule for Suspicious Process Execution Change Description: Implement a detection rule in Microsoft Sentinel based on the provided KQL query to monitor for suspicious process executions. Steps to Implement the Change Log in to Azure Portal: Navigate to the Azure portal (https://portal.azure.com). Ensure you have the necessary permissions to access Microsoft Sentinel. Access Microsoft Sentinel: In the Azure portal, search for "Microsoft Sentinel" and select the appropriate workspace. Create a New Analytics Rule: Go to the "Analytics" section in the Microsoft Sentinel workspace. Click on "Create" and select "Scheduled query rule." Define Rule Details: Name: Suspicious Process Execution Detection Description: Detects execution of potentially suspicious processes based on the provided KQL query. Severity: High Tactics: Execution Set Rule Logic: Query: Copy and paste the following KQL query: DeviceProcessEvents | where TimeGenerated >= ago(3d) | where InitiatingProcessFileName matches regex @'(?i).*atbroker\.exe.*|.*bash\.exe.*|.*bitsadmin\.exe.*|.*certutil\.exe.*|.*cmdkey\.exe.*|.*cmstp\.exe.*|.*control\.exe.*|.*csc\.exe.*|.*cscript\.exe.*|.*dfsvc\.exe.*|.*diskshadow\.exe.*|.*dnscmd\.exe.*|.*esentutl\.exe.*|.*eventvwr\.exe.*|.*expand\.exe.*|.*extexport\.exe.*|.*extrac32\.exe.*|.*findstr\.exe.*|.*forfiles\.exe.*|.*ftp\.exe.*|.*gpscript\.exe.*|.*hh\.exe.*|.*ie4uinit\.exe.*|.*ieexec\.exe.*|.*infdefaultinstall\.exe.*|.*installutil\.exe.*|.*makecab\.exe.*|.*reg\.exe.*|.*print\.exe.*|.*presentationhost\.exe.*|.*pcwrun\.exe.*|.*pcalua\.exe.*|.*odbcconf\.exe.*|.*msiexec\.exe.*|.*mshta\.exe.*|.*msdt\.exe.*|.*msconfig\.exe.*|.*msbuild\.exe.*|.*mmc\.exe.*|.*microsoft.workflow.compiler\.exe.*|.*mavinject\.exe.*|.*vsjitdebugger\.exe.*|.*tracker\.exe.*|.*te\.exe.*|.*sqltoolsps\.exe.*|.*sqlps\.exe.*|.*sqldumper\.exe.*|.*rcsi\.exe.*|.*msxsl\.exe.*|.*msdeploy\.exe.*|.*mftrace\.exe.*|.*dxcap\.exe.*|.*dnx\.exe.*|.*csi\.exe.*|.*cdb\.exe.*|.*bginfo\.exe.*|.*appvlp\.exe.*|.*xwizard\.exe.*|.*wsreset\.exe.*|.*wscript\.exe.*|.*wmic\.exe.*|.*wab\.exe.*|.*verclsid\.exe.*|.*syncappvpublishingserver\.exe.*|.*scriptrunner\.exe.*|.*schtasks\.exe.*|.*sc\.exe.*|.*runscripthelper\.exe.*|.*runonce\.exe.*|.*rundll32\.exe.*|.*rpcping\.exe.*|.*replace\.exe.*|.*regsvr32\.exe.*|.*regsvcs\.exe.*|.*register-cimprovider\.exe.*|.*regedit\.exe.*|.*regasm\.exe.*' | summarize by InitiatingProcessFileName Query Frequency: 5 minutes Query Period: 3 days Set Alert Details: Alert Rule Name: Suspicious Process Execution Detected Description: Alerts when a suspicious process execution is detected. Severity: High Tactics: Execution Configure Automated Response (Optional): Add any playbooks or automated responses if required. Review and Create: Review the rule configuration. Click "Create" to save and activate the rule. Required Access Level Role: Security Administrator or Sentinel Contributor Permissions: Read and write access to Microsoft Sentinel Ability to create and manage analytics rules Rollback Plan Log in to Azure Portal: Navigate to the Azure portal (https://portal.azure.com). Access Microsoft Sentinel: In the Azure portal, search for "Microsoft Sentinel" and select the appropriate workspace. Delete the Analytics Rule: Go to the "Analytics" section in the Microsoft Sentinel workspace. Locate the "Suspicious Process Execution Detection" rule. Select the rule and click "Delete." Verify Deletion: Ensure the rule is no longer listed in the "Analytics" section. Document the Rollback: Update the change control document to reflect the rollback action. Approval Change Approver: [To be assigned] Approval Date: [To be assigned] Implementation Date Scheduled Date: [To be assigned] Implementation Time: [To be assigned] Rollback Date Scheduled Date: [To be assigned] Rollback Time: [To be assigned] As you can see with the above methods, we have the option now to create a prompt book which can be manually run to efficiently convert AQL and YARA to KQL. But since we had more time to play with, we decide to see if we could do this task in an automated fashion using logic apps. The first step in this process was to create a simplified promptbooks that can be later invoked by a logic app: We exported the AQL queries and imported them directly into Watchlists within Sentinel: Clicking on view logs shows AQLPayLoad stored in the _GetWatchlist('AQL2KQL') table: Since we have this watch list now imported, it simply will not work as the syntax of AQL is not understood by Sentinel: We now need to design a logic app that can help do the conversation directly from the uploaded watchlist, it has to also invoke the relevant prompt book(s) created above: We now have the option of getting Copilot to carry out the conversion via a Logic App so in theory we just have to upload the watch list and let the logic app do the conversation. You can download the following from Microsoft Security Copilot official Github repo, thanks for supporting the automated deployment package. KQL Migrator Azure Logic App Code file : Click here o Make sure to replace the following with your tenant's correct information: <subscription-ID> <ResourceGroup-Name> Sample AQL queries / YARA rules: Click here Conclusion and Consideration So, we've explored how Security Copilot Azure Logic App connector and Promptbooks revolutionizes the integration of AI-driven security solutions into current workflows. This tool not only provides greater customization and smoother system integration but also access to a broader range of ready-made Azure logic security tools integration. We encourage you to experience the efficiency of the Security Copilot Azure Logic App connector in enhancing your security operations. Whilst investigating and understanding AQL/YARA we did not leave the copilot platform, we also found that it can convert to STIX II to as well You can customize the Logic App by automatically creating the Microsoft Sentinel analytics rules via the API If Logic Apps are not suitable, consider adding a step to create control documentation within the promptbook as shown above. Authors Hesham Saad - Sr Cybersecurity Global Blackbelt Hiten Sharma – Sr Cybersecurity Global BlackbeltIdentity forensics with Copilot for Security Identity Analyst Plugin
Microsoft Copilot for Securityis a platform that brings together the power of AI and human expertise to help administrators and security teams respond to attacks faster and more effectively. Copilot for Security is embedded in Microsoft Entra so you can investigate and resolve identity risks, assess identities and access with AI-driven intelligence, and complete complex tasks quickly. Microsoft Copilot in Microsoft Entra gets insights from your Microsoft Entra users, groups, sign-in logs, audit logs, and more. You can explore sign-ins and risky users and get contextualized insights on how to resolve incidents and what to do to protect the accounts in natural language. Built on top of real-time machine learning, Copilot in Microsoft Entra can help you find gaps in access policies, generate identity workflows, and troubleshoot faster. You can also unlock new skills that allow admins at all levels to complete complex tasks such as incident investigation, sign-in log analysis, and more, to gain savings in time and resources.Case Study: Harnessing Copilot for Security in Defending Against Cyberthreats
Get ready to dive into a real-life security incident within Microsoft Defender XDR! In this case study, you’ll take on the role of a security analyst and uncover how Copilot for Security can empower you throughout the investigation. Let’s see how you can tackle cyber threats head-on!
