Introducing Secret Finder: Finding Real Credentials Where Traditional Tools Fail
Secret Finder is an AI-powered capability in Microsoft Security Copilot that detects leaked credentials in unstructured content, such as emails, chat logs, documents, and screenshots, where traditional pattern-matching tools struggle. It relies on a multi‑step, multi‑agent reasoning workflow rather than a single pass detector. Detection, verification, and contextual analysis are handled by distinct reasoning stages, allowing Secret Finder to find real credentials without flooding users with false positives. Unlike regex-based scanners, Secret Finder uses reasoning to identify not just credentials, but the systems they unlock, helping security teams understand exposure and respond faster. In benchmark testing on synthetic datasets, Secret Finder achieved 98.33% true credential detection with zero false alarms on realistic emails, chats, notes, and documents—while traditional regex scanners detected only about 40% of the same credentials. Secret Finder is now generally available in Security Copilot, supporting 20+ credential types with high precision and actionable context. The Problem: Credentials Hide Where Traditional Tools Can't See When security incidents happen, leaked credentials don't always appear in clean, predictable formats. They show up buried in email threads, pasted into Teams messages, embedded in Word documents, or captured in screenshots of logs and terminals. These are exactly the places where security teams spend the most time and where traditional secret scanning tools fail. Most existing tools rely on regular expressions or simple pattern matching. This works reasonably well for structured environments like source code repositories, where credentials follow predictable formats. But in real-world incidents, secrets look different. A storage key might be split across multiple messages in an email thread. A credential could be reformatted, partially redacted, or embedded alongside explanatory text. In these situations, pattern matching produces two painful outcomes: it misses real credentials because the format doesn’t match a known rule, or it floods analysts with false positives that waste time. Security teams are left manually reviewing content, guessing which findings are real, and piecing together what systems might actually be at risk. In practice, this failure mode has a real human cost that security analysts end up reviewing thousands of alerts, manually inspecting email threads and chat logs, and trying to determine whether a suspicious string actually unlocks a storage account, API, or production service. Teams can spend days reconstructing context across messages and documents just to understand what a credential grants access to, slowing containment and increasing risk during active incidents. This is the gap Secret Finder was built to close. The Solution: Secret Finder Brings Reasoning to Secret Detection Secret Finder approaches secret detection as a reasoning problem, not a string-matching exercise. Instead of asking "does this text match a pattern?" It asks human-like questions: Is this text describing a credential or access mechanism? Does the value look real and usable? What system or resource could this access? This shift is subtle but powerful. Secret Finder doesn't just detect credentials, it connects them to doors: the specific targets those credentials unlock, such as API endpoints, storage accounts, applications, or services. This is critical for triage. Instead of stopping at “this looks like a credential,” Secret Finder tells analysts what that credential actually opens. Without context, a credential triggers manual follow‑up. When it’s linked to a specific target, analysts can immediately assess impact and act. By understanding messy, real-world content the way a human investigator would, Secret Finder delivers findings that security teams can trust and act on immediately. It's designed specifically for the unstructured, noisy environments where incidents actually unfold. Why Secret Finder Outperforms Traditional Pattern Matching Traditional secret scanners are built for clean data. Secret Finder is built for reality. Traditional tools struggle when: Credentials appear in natural language descriptions rather than code Context determines whether a string is sensitive or benign Credentials are incomplete, malformed, or partially redacted Secret Finder excels because it: Reasons through context, understanding surrounding text to identify what's truly sensitive Detects credentials and their associated resources together, providing the "what" and the "where" in a single pass Handles noisy, unstructured inputs like emails, chat logs, documents Assigns confidence scores to help teams prioritize findings and reduce alert fatigue What Secret Finder Can Do Today Secret Finder is now generally available in Microsoft Security Copilot, with capabilities shaped directly by real security workflows across incident response, red teaming, and SOC operations. It detects over 20 major credential categories, spanning cloud provider credentials like Azure Storage Keys and AWS Access Keys, authentication credentials including Microsoft Entra passwords and OAuth tokens, database connection strings, SSH private keys, API keys, and generic secrets that don't fit predefined patterns. This broad coverage means analysts can scan investigation artifacts without worrying whether the secret type is supported. What makes Secret Finder particularly effective is where it works. Email threads where credentials are discussed across multiple messages. Teams chats where credentials are pasted quickly during troubleshooting. Word documents and internal wikis where credentials are documented for operational handoffs. Incident reports and post-mortem notes written under pressure. These are the environments where traditional pattern-matching tools fail, and where Secret Finder delivers the most value. In benchmark evaluations, Secret Finder achieved 100% recall with 0% false positives on synthetic datasets containing embedded Azure Storage credentials, compared to 40% recall from traditional regex‑based tools such as CredScan. In more complex scenarios involving multiple credential types and noisy email content, Secret Finder maintained 98.33% recall with 0% false positives. These results were observed on synthetically generated evaluation datasets spanning emails, chats, notes, and documents, designed to reflect how engineers communicate and how credentials may be inadvertently shared in real‑world workflows. Scenario Precision Recall Single credential type 100% 100% Complex, multiple credential types 100% 98.33% Secret Finder is currently integrated into Security Copilot, actively supporting incident response workflows, and working toward deeper integrations with developer platforms such as GitHub to bring contextual secret detection to source code analysis at scale. Using Secret Finder in Security Copilot Secret Finder is available as a skill in Microsoft Security Copilot, making credential detection a seamless part of analyst workflows. How to use Secret Finder: Enable the Secret Finder skill in Security Copilot via "Manage Sources" → "Manage Plugins" (Figure 1) Select "FindSecretInText" from Promptbook (Figure 2) Submit unstructured content directly in the Copilot prompt: paste the text blob that might contain credentials Secret Finder analyzes the content using its multi-agent workflow, detecting credentials and associated doors Review actionable findings with contextual details Figure 1. Enabling the Secret Finder skill in Microsoft Security Copilot (Due to recent naming changes, users might see "Agentic secret finder" in Security copilot. Naming changes will reflect in a few weeks) Figure 2. Selecting the FindSecretInText prompt, which invokes Secret Finder’s multi‑step credential detection and verification workflow Figure 3. Submitting a text blob containing embedded credentials for analysis (example is synthetic) Figure 4. Secret Finder output with detected credentials and associated doors (example credentials and associated doors are synthetic) What's Next for Secret Finder Secret Finder is a living capability. Over the next six months, we are working towards coverage and deepening integrations: Exploring integrations with GitHub to reduce false positives in secret scanning for code repositories Optimizing for large-scale analysis to handle enterprise-wide scans efficiently with reduced latency Exploring graph-based risk modeling to map relationships between credentials, services, and attack paths Our long-term vision goes beyond detection: we want to help security teams understand how credentials are used, what risks exist if they're exposed, and what the impact of rotation or revocation would be. By moving from "what's leaked" to "what does it mean," Secret Finder will enable smarter prioritization, faster response, and more confident decision-making. Acknowledgments Secret Finder has been a cross-team effort over the past year, evolving from early research and prototyping through private preview, public preview, and now general availability. This milestone reflects contributions across many phases from initial system design and technical direction, to evaluation, product integration, and deployment at scale. Contributors include Mariko Wakabayashi leading the early research through production and to the team including Zixiao Chen and Avy Challa for GA improvements and bringing Secret Finder to production readiness. We also appreciate Tony Twum-Barimah, Malachi Jones, and the Security Copilot team, including Austin Trapp and Vinod Jagannathan for their technical and product support throughout the process, as well as Christian Rudnick and Helen Chang for guiding us through the responsible AI reviews before launch. Finally, a huge thanks to the incident responders and security researchers who shared valuable insights along the way. Secret Finder wouldn’t have been possible without their work and feedback.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.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 hereCase 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!Extending Microsoft Copilot for Security Capabilities with Azure Function Apps
Azure Function Apps offer a convenient way to execute functions in a server-less environment. They allow users to write functions in C#, Java, JavaScript, PowerShell, Python and Typescript which can then be called using several trigger options. One of the most common triggers is the HTTP trigger allowing functions to be called like a REST API. This article shows how to build a Copilot for Security API plugin that calls an Azure Function App.Microsoft Copilot for Security Defender Threat Intelligence and Threat Analytics Plugin Overview
Copilot for Security delivers information about threat actors, indicators of compromise (IOCs), tools, and vulnerabilities, as well as contextual threat intelligence from Microsoft Defender Threat Intelligence (MDTI) and Threat Analytics (TA).
