Recent Discussions
Sentinel SOAR migration to Unified portal: what broke? anyone evaluated the AI playbook generator?
I want to open a conversation specifically focused on the automation and SOAR side of the migration, because this is the area where problems most commonly surface after onboarding rather than during it. A quick orientation: the Unified portal introduces a specific constraint that catches teams by surprise. Alert-triggered automation for alerts created by Microsoft Defender XDR is not available in the Defender portal. The main use case for alert-triggered automation in this context is responding to alerts from analytics rules where incident creation is disabled. If you had alert-triggered playbooks firing on Defender XDR signals, those need to be re-evaluated against the incident trigger model. This is documented by Microsoft, but it is easy to miss in the volume of migration guidance. The automation failure mode I have seen most consistently: automation rules built around incident title conditions. The Defender XDR correlation engine assigns its own incident names, so any condition keyed to "if incident title contains X" stops matching without throwing an error. The rule is still active, the automation is still enabled, and everything looks fine until someone notices a class of enrichment or response has gone quiet. Microsoft's recommendation is to use Analytic rule name as the condition instead. There is also a firm near-term deadline separate from the March 2027 portal retirement: queries and automation need to be updated by July 1, 2026 for standardised account entity naming. The Name field will consistently hold only the UPN prefix from that date. Any automation comparing AccountName against a full UPN will break. A few specific questions for practitioners: When you onboarded or reviewed your automation post-onboarding, what broke silently versus what produced a visible error? Silent failures are the dangerous ones and sharing specific patterns would be genuinely useful for the community. Has anyone evaluated the new AI playbook generator in the Defender portal? It requires Security Copilot with SCUs available and generates Python-based automation coauthored with Cline in an embedded VS Code environment. Interested in real-world comparisons against existing Logic Apps workflows for the same use case. For those who have migrated alert-triggered playbooks to automation rule invocation: did you find edge cases in the migration, particularly around playbooks used by multiple analytics rules simultaneously? Writing this up as Part 4 of the migration series. Sharing the article link once it is live for anyone who wants the full detail.
XdrLogRaider Defender XDR portal telemetry
A Microsoft Sentinel custom data connector that ingests Microsoft Defender XDR portal-only telemetry — configuration, compliance, drift, exposure, governance — that public Microsoft APIs (Graph Security, Microsoft 365 Defender, MDE) don't expose. https://github.com/akefallonitis/xdrlograider— Defender XDR portal telemetry Happy Hunting 🥳 🎉Sentinel Foundry - MCP Server (Preview) (Github Community Release)
I’ve been cooking something that a lot of people in SOC have been struggling with — especially on the engineering side of Microsoft Sentinel. Thanks to the Microsoft Security team for shaping the capabilities of Sentinel even better with Sentinel Data Lake & Modern SecOps. Today’s the day I can finally share it. Note: This is not an official Microsoft product, but it is designed to make the Sentinel Build even better (complement) with much more intelligence. 🚀 Sentinel Foundry is now in public preview with 43 tools. (Sentinel Foundry - MCP Server) It’s an MCP server built to act like the brain of a strong Sentinel engineer — helping make building, improving, and operating Sentinel far more practical, faster, and honestly more enjoyable. For a lot of teams, the challenge is not understanding what Sentinel can do. The hard part is the engineering work around it: -> Deciding what data should actually be ingested -> Building a clean, scalable Sentinel foundation -> Writing useful detections instead of noisy ones -> Balancing security value with cost -> Turning ideas into deployable engineering outputs That is exactly why I built Sentinel Foundry to help communities grow stronger. It helps with the real engineering tasks behind Sentinel — from architecture thinking to detection design, deployment planning, ingestion strategy, automation ideas, and many of the workflows outlined in the GitHub project. How does it work? Here’s one of the flagship prompts I ran with it: “Give me a complete security posture report for our workspace. Score each pillar and tell me what to prioritise.” And within seconds, it produced a structured engineering blueprint that would normally take a lot longer to pull together manually. You can see the example prompts here in what it can do: https://github.com/prabhukiranveesam/Sentinel-Foundry#what-can-it-do I want building Sentinel to feel less like repetitive engineering overhead — and more like real security engineering that is fast, creative, and enjoyable. If you work with Sentinel as a SOC L2 analyst, engineer, detection engineer, consultant, or architect, I’d genuinely love for you to try it and tell me what you think. 🔗 Public Preview: https://github.com/prabhukiranveesam/Sentinel-Foundry This is just the start of an AI era — and I’m excited to keep shaping it with more powerful features over the coming days. This is very easy to set up and will be available to all of you at no cost during this month as part of the public preview, and your feedback is extremely valuable to shape this as a powerful solution.Your Sentinel AMA Logs & Queries Are Public by Default — AMPLS Architectures to Fix That
When you deploy Microsoft Sentinel, security log ingestion travels over public Azure Data Collection Endpoints by default. The connection is encrypted, and the data arrives correctly — but the endpoint is publicly reachable, and so is the workspace itself, queryable from any browser on any network. For many organisations, that trade-off is fine. For others — regulated industries, healthcare, financial services, critical infrastructure — it is the exact problem they need to solve. Azure Monitor Private Link Scope (AMPLS) is how you solve it. What AMPLS Actually Does AMPLS is a single Azure resource that wraps your monitoring pipeline and controls two settings: Where logs are allowed to go (ingestion mode: Open or PrivateOnly) Where analysts are allowed to query from (query mode: Open or PrivateOnly) Change those two settings and you fundamentally change the security posture — not as a policy recommendation, but as a hard platform enforcement. Set ingestion to PrivateOnly and the public endpoint stops working. It does not fall back gracefully. It returns an error. That is the point. It is not a firewall rule someone can bypass or a policy someone can override. Control is baked in at the infrastructure level. Three Patterns — One Spectrum There is no universally correct answer. The right architecture depends on your organisation's risk appetite, existing network infrastructure, and how much operational complexity your team can realistically manage. These three patterns cover the full range: Architecture 1 — Open / Public (Basic) No AMPLS. Logs travel to public Data Collection Endpoints over the internet. The workspace is open to queries from anywhere. This is the default — operational in minutes with zero network setup. Cloud service connectors (Microsoft 365, Defender, third-party) work immediately because they are server-side/API/Graph pulls and are unaffected by AMPLS. Azure Monitor Agents and Azure Arc agents handle ingestion from cloud or on-prem machines via public network. Simplicity: 9/10 | Security: 6/10 Good for: Dev environments, teams getting started, low-sensitivity workloads Architecture 2 — Hybrid: Private Ingestion, Open Queries (Recommended for most) AMPLS is in place. Ingestion is locked to PrivateOnly — logs from virtual machines travel through a Private Endpoint inside your own network, never touching a public route. On-premises or hybrid machines connect through Azure Arc over VPN or a dedicated circuit and feed into the same private pipeline. Query access stays open, so analysts can work from anywhere without needing a VPN/Jumpbox to reach the Sentinel portal — the investigation workflow stays flexible, but the log ingestion path is fully ring-fenced. You can also split ingestion mode per DCE if you need some sources public and some private. This is the architecture most organisations land on as their steady state. Simplicity: 6/10 | Security: 8/10 Good for: Organisations with mixed cloud and on-premises estates that need private ingestion without restricting analyst access Architecture 3 — Fully Private (Maximum Control) Infrastructure is essentially identical to Architecture 2 — AMPLS, Private Endpoints, Private DNS zones, VPN or dedicated circuit, Azure Arc for on-premises machines. The single difference: query mode is also set to PrivateOnly. Analysts can only reach Sentinel from inside the private network. VPN or Jumpbox required to access the portal. Both the pipe that carries logs in and the channel analysts use to read them are fully contained within the defined boundary. This is the right choice when your organisation needs to demonstrate — not just claim — that security data never moves outside a defined network perimeter. Simplicity: 2/10 | Security: 10/10 Good for: Organisations with strict data boundary requirements (regulated industries, audit, compliance mandates) Quick Reference — Which Pattern Fits? Scenario Architecture Getting started / low-sensitivity workloads Arch 1 — No network setup, public endpoints accepted Private log ingestion, analysts work anywhere Arch 2 — AMPLS PrivateOnly ingestion, query mode open Both ingestion and queries must be fully private Arch 3 — Same as Arch 2 + query mode set to PrivateOnly One thing all three share: Microsoft 365, Entra ID, and Defender connectors work in every pattern — they are server-side pulls by Sentinel and are not affected by your network posture. Please feel free to reach out if you have any questions regarding the information provided.
Understand New Sentinel Pricing Model with Sentinel Data Lake Tier
Introduction on Sentinel and its New Pricing Model Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platform that collects, analyzes, and correlates security data from across your environment to detect threats and automate response. Traditionally, Sentinel stored all ingested data in the Analytics tier (Log Analytics workspace), which is powerful but expensive for high-volume logs. To reduce cost and enable customers to retain all security data without compromise, Microsoft introduced a new dual-tier pricing model consisting of the Analytics tier and the Data Lake tier. The Analytics tier continues to support fast, real-time querying and analytics for core security scenarios, while the new Data Lake tier provides very low-cost storage for long-term retention and high-volume datasets. Customers can now choose where each data type lands—analytics for high-value detections and investigations, and data lake for large or archival types—allowing organizations to significantly lower cost while still retaining all their security data for analytics, compliance, and hunting. Please flow diagram depicts new sentinel pricing model: Now let's understand this new pricing model with below scenarios: Scenario 1A (PAY GO) Scenario 1B (Usage Commitment) Scenario 2 (Data Lake Tier Only) Scenario 1A (PAY GO) Requirement Suppose you need to ingest 10 GB of data per day, and you must retain that data for 2 years. However, you will only frequently use, query, and analyze the data for the first 6 months. Solution To optimize cost, you can ingest the data into the Analytics tier and retain it there for the first 6 months, where active querying and investigation happen. After that period, the remaining 18 months of retention can be shifted to the Data Lake tier, which provides low-cost storage for compliance and auditing needs. But you will be charged separately for data lake tier querying and analytics which depicted as Compute (D) in pricing flow diagram. Pricing Flow / Notes The first 10 GB/day ingested into the Analytics tier is free for 31 days under the Analytics logs plan. All data ingested into the Analytics tier is automatically mirrored to the Data Lake tier at no additional ingestion or retention cost. For the first 6 months, you pay only for Analytics tier ingestion and retention, excluding any free capacity. For the next 18 months, you pay only for Data Lake tier retention, which is significantly cheaper. Azure Pricing Calculator Equivalent Assuming no data is queried or analyzed during the 18-month Data Lake tier retention period: Although the Analytics tier retention is set to 6 months, the first 3 months of retention fall under the free retention limit, so retention charges apply only for the remaining 3 months of the analytics retention window. Azure pricing calculator will adjust accordingly. Scenario 1B (Usage Commitment) Now, suppose you are ingesting 100 GB per day. If you follow the same pay-as-you-go pricing model described above, your estimated cost would be approximately $15,204 per month. However, you can reduce this cost by choosing a Commitment Tier, where Analytics tier ingestion is billed at a discounted rate. Note that the discount applies only to Analytics tier ingestion—it does not apply to Analytics tier retention costs or to any Data Lake tier–related charges. Please refer to the pricing flow and the equivalent pricing calculator results shown below. Monthly cost savings: $15,204 – $11,184 = $4,020 per month Now the question is: What happens if your usage reaches 150 GB per day? Will the additional 50 GB be billed at the Pay-As-You-Go rate? No. The entire 150 GB/day will still be billed at the discounted rate associated with the 100 GB/day commitment tier bucket. Azure Pricing Calculator Equivalent (100 GB/ Day) Azure Pricing Calculator Equivalent (150 GB/ Day) Scenario 2 (Data Lake Tier Only) Requirement Suppose you need to store certain audit or compliance logs amounting to 10 GB per day. These logs are not used for querying, analytics, or investigations on a regular basis, but must be retained for 2 years as per your organization’s compliance or forensic policies. Solution Since these logs are not actively analyzed, you should avoid ingesting them into the Analytics tier, which is more expensive and optimized for active querying. Instead, send them directly to the Data Lake tier, where they can be retained cost-effectively for future audit, compliance, or forensic needs. Pricing Flow Because the data is ingested directly into the Data Lake tier, you pay both ingestion and retention costs there for the entire 2-year period. If, at any point in the future, you need to perform advanced analytics, querying, or search, you will incur additional compute charges, based on actual usage. Even with occasional compute charges, the cost remains significantly lower than storing the same data in the Analytics tier. Realized Savings Scenario Cost per Month Scenario 1: 10 GB/day in Analytics tier $1,520.40 Scenario 2: 10 GB/day directly into Data Lake tier $202.20 (without compute) $257.20 (with sample compute price) Savings with no compute activity: $1,520.40 – $202.20 = $1,318.20 per month Savings with some compute activity (sample value): $1,520.40 – $257.20 = $1,263.20 per month Azure calculator equivalent without compute Azure calculator equivalent with Sample Compute Conclusion The combination of the Analytics tier and the Data Lake tier in Microsoft Sentinel enables organizations to optimize cost based on how their security data is used. High-value logs that require frequent querying, real-time analytics, and investigation can be stored in the Analytics tier, which provides powerful search performance and built-in detection capabilities. At the same time, large-volume or infrequently accessed logs—such as audit, compliance, or long-term retention data—can be directed to the Data Lake tier, which offers dramatically lower storage and ingestion costs. Because all Analytics tier data is automatically mirrored to the Data Lake tier at no extra cost, customers can use the Analytics tier only for the period they actively query data, and rely on the Data Lake tier for the remaining retention. This tiered model allows different scenarios—active investigation, archival storage, compliance retention, or large-scale telemetry ingestion—to be handled at the most cost-effective layer, ultimately delivering substantial savings without sacrificing visibility, retention, or future analytical capabilities.CrowdStrike API Data Connector (via Codeless Connector Framework) (Preview)
API scopes created. Added to Connector however only streams observed are from Alerts and Hosts. Detections is not logging? Anyone experiencing this issue? Github has post about it apears to be escalated for feature request. CrowdStrikeDetections. not ingested Anyone have this setup and working?How Should a Fresher Learn Microsoft Sentinel Properly?
Hello everyone, I am a fresher interested in learning Microsoft Sentinel and preparing for SOC roles. Since Sentinel is a cloud-native enterprise tool and usually used inside organizations, I am unsure how individuals without company access are expected to gain real hands-on experience. I would like to hear from professionals who actively use Sentinel: - How do freshers typically learn and practice Sentinel? - What learning resources or environments are commonly used by beginners? - What level of hands-on experience is realistically expected at entry level? I am looking for guidance based on real industry practice. Thank you for your time.
Issue connecting Azure Sentinel GitHub app to Sentinel Instance when IP allow list is enabled
Hi everyone, I’m running into an issue connecting the Azure Sentinel GitHub app to my Sentinel workspace in order to create our CI/CD pipelines for our detection rules, and I’m hoping someone can point me in the right direction. Symptoms: When configuring the GitHub connection in Sentinel, the repository dropdown does not populate. There are no explicit errors, but the connection clearly isn’t completing. If I disable my organization’s IP allow list, everything works as expected and the repos appear immediately. I’ve seen that some GitHub Apps automatically add the IP ranges they require to an organization’s allow list. However, from what I can tell, the Azure Sentinel GitHub app does not seem to have this capability, and requires manual allow listing instead. What I’ve tried / researched: Reviewed Microsoft documentation for Sentinel ↔ GitHub integrations Looked through Azure IP range and Service Tag documentation I’ve seen recommendations to allow list the IP ranges published at //api.github.com/meta, as many GitHub apps rely on these ranges I’ve already tried allow listing multiple ranges from the GitHub meta endpoint, but the issue persists My questions: Does anyone know which IP ranges are used by the Azure Sentinel GitHub app specifically? Is there an official or recommended approach for using this integration in environments with strict IP allow lists? Has anyone successfully configured this integration without fully disabling IP restrictions? Any insight, references, or firsthand experience would be greatly appreciated. Thanks in advance!
Missing details in Azure Activity Logs – MICROSOFT.SECURITYINSIGHTS/ENTITIES/ACTION
The Azure Activity Logs are crucial for tracking access and actions within Sentinel. However, I’m encountering a significant lack of documentation and clarity regarding some specific operation types. Resources consulted: https://learn.microsoft.com/en-us/azure/sentinel/audit-sentinel-data https://learn.microsoft.com/en-us/rest/api/securityinsights/entities?view=rest-securityinsights-2024-01-01-preview https://learn.microsoft.com/en-us/rest/api/securityinsights/operations/list?view=rest-securityinsights-2024-09-01&tabs=HTTP My issue: I observed unauthorized activity on our Sentinel workspace. The Azure Activity Logs clearly indicate the user involved, the resource, and the operation type: "MICROSOFT.SECURITYINSIGHTS/ENTITIES/ACTION" But that’s it. No detail about what the action was, what entity it targeted, or how it was triggered. This makes auditing extremely difficult. It's clear the person was in Sentinel and perform an activity through it, from search, KQL, logs to find an entity from a KQL query. But, that's all... Strangely, this operation is not even listed in the official Sentinel Operations documentation linked above. My question: Has anyone encountered this and found a way to interpret this operation type properly? Any insight into how to retrieve more meaningful details (action context, target entity, etc.) from these events would be greatly appreciated.Ingest IOC from Google Threat Intelligence into Sentinel
Hi all, I'm string to ingest IOCs from Google Threat Intelligence into Sentinel. I follow the guide at gtidocs.virutotal.com/docs/gti4sentinel-guide API KEY is correct. PS: I'm using standard free public API (created in Viru Total) Managed Identitity has been configured using the correct role. When I run the Logic APP, I received an HTTP error 403 "code": "ForbiddenError", "message": "You are not authorized to perform the requested operation" What's the problem ?? Regards, HA
Pricing Calculator for Microsoft Sentinel
Hi everyone, I am using the Pricing Calculator for Microsoft Sentinel. I can see the pricing split into two parts - Azure Monitor and Microsoft Sentinel. In my understanding, Microsoft Sentinel will process the log stored in the Log Analytics Workspace. The Cost is based on the log size in the Log Analytics Workspace. It may not relate to the Azure Monitor part. The Pricing Calculator will charge the Azure Monitor part because Azure Monitor and Microsoft Sentinel share the same Log Analytics Workspace? Basically, I am not using Azure Monitor. Any method to reduce the cost of the Azure Monitor part?Security Copilot Integration with Microsoft Sentinel - Why Automation matters now
Security Operations Centers face a relentless challenge - the volume of security alerts far exceeds the capacity of human analysts. On average, a mid-sized SOC receives thousands of alerts per day, and analysts spend up to 80% of their time on initial triage. That means determining whether an alert is a true positive, understanding its scope, and deciding on next steps. With Microsoft Security Copilot now deeply integrated into Microsoft Sentinel, there is finally a practical path to automating the most time-consuming parts of this workflow. So I decided to walk you through how to combine Security Copilot with Sentinel to build an automated incident triage pipeline - complete with KQL queries, automation rule patterns, and practical scenarios drawn from common enterprise deployments. Traditional triage workflows rely on analysts manually reviewing each incident - reading alert details, correlating entities across data sources, checking threat intelligence, and making a severity assessment. This is slow, inconsistent, and does not scale. Security Copilot changes this equation by providing: Natural language incident summarization - turning complex, multi-alert incidents into analyst-readable narratives Automated entity enrichment - pulling threat intelligence, user risk scores, and device compliance state without manual lookups Guided response recommendations - suggesting containment and remediation steps based on the incident type and organizational context The key insight is that Copilot does not replace analysts - it handles the repetitive first-pass triage so analysts can focus on decision-making and complex investigations. Architecture - How the Pieces Fit Together The automated triage pipeline consists of four layers: Detection Layer - Sentinel analytics rules generate incidents from log data Enrichment Layer - Automation rules trigger Logic Apps that call Security Copilot Triage Layer - Copilot analyzes the incident, enriches entities, and produces a triage summary Routing Layer - Based on Copilot's assessment, incidents are routed, re-prioritized, or auto-closed (Forgive my AI-painted illustration here, but I find it a nice way to display dependencies.) +-----------------------------------------------------------+ | Microsoft Sentinel | | | | Analytics Rules --> Incidents --> Automation Rules | | | | | v | | Logic App / Playbook | | | | | v | | Security Copilot API | | +-----------------+ | | | Summarize | | | | Enrich Entities | | | | Assess Risk | | | | Recommend Action| | | +--------+--------+ | | | | | v | | +-----------------------------+ | | | Update Incident | | | | - Add triage summary tag | | | | - Adjust severity | | | | - Assign to analyst/team | | | | - Auto-close false positive| | | +-----------------------------+ | +-----------------------------------------------------------+ Step 1 - Identify High-Volume Triage Candidates Not every incident type benefits equally from automated triage. Start with alert types that are high in volume but often turn out to be false positives or low severity. Use this KQL query to identify your top candidates: SecurityIncident | where TimeGenerated > ago(30d) | summarize TotalIncidents = count(), AutoClosed = countif(Classification == "FalsePositive" or Classification == "BenignPositive"), AvgTimeToTriageMinutes = avg(datetime_diff('minute', FirstActivityTime, CreatedTime)) by Title | extend FalsePositiveRate = round(AutoClosed * 100.0 / TotalIncidents, 1) | where TotalIncidents > 10 | order by TotalIncidents desc | take 20 This query surfaces the incident types where automation will deliver the highest ROI. Based on publicly available data and community reports, the following categories consistently appear at the top: Impossible travel alerts (high volume, around 60% false positive rate) Suspicious sign-in activity from unfamiliar locations Mass file download and share events Mailbox forwarding rule creation Step 2 - Build the Copilot-Powered Triage Playbook Create a Logic App playbook that triggers on incident creation and leverages the Security Copilot connector. The core flow looks like this: Trigger: Microsoft Sentinel Incident - When an incident is created Action 1 - Get incident entities: let incidentEntities = SecurityIncident | where IncidentNumber == <IncidentNumber> | mv-expand AlertIds | join kind=inner (SecurityAlert | extend AlertId = SystemAlertId) on $left.AlertIds == $right.AlertId | mv-expand Entities | extend EntityData = parse_json(Entities) | project EntityType = tostring(EntityData.Type), EntityValue = coalesce( tostring(EntityData.HostName), tostring(EntityData.Address), tostring(EntityData.Name), tostring(EntityData.DnsDomain) ); incidentEntities Note: The <IncidentNumber> placeholder above is a Logic App dynamic content variable. When building your playbook, select the incident number from the trigger output rather than hardcoding a value. Action 2 - Copilot prompt session: Send a structured prompt to Security Copilot that requests: Analyze this Microsoft Sentinel incident and provide a triage assessment: Incident Title: {IncidentTitle} Severity: {Severity} Description: {Description} Entities involved: {EntityList} Alert count: {AlertCount} Please provide: 1. A concise summary of what happened (2-3 sentences) 2. Entity risk assessment for each IP, user, and host 3. Whether this appears to be a true positive, benign positive, or false positive 4. Recommended next steps 5. Suggested severity adjustment (if any) Action 3 - Parse and route: Use the Copilot response to update the incident. The Logic App parses the structured output and: Adds the triage summary as an incident comment Tags the incident with copilot-triaged Adjusts severity if Copilot recommends it Routes to the appropriate analyst tier based on the assessment Step 3 - Enrich with Contextual KQL Lookups Security Copilot's assessment improves dramatically when you feed it contextual data. Before sending the prompt, enrich the incident with organization-specific signals: // Check if the user has a history of similar alerts (repeat offender vs. first time) let userAlertHistory = SecurityAlert | where TimeGenerated > ago(90d) | mv-expand Entities | extend EntityData = parse_json(Entities) | where EntityData.Type == "account" | where tostring(EntityData.Name) == "<UserPrincipalName>" | summarize PriorAlertCount = count(), DistinctAlertTypes = dcount(AlertName), LastAlertTime = max(TimeGenerated) | extend IsRepeatOffender = PriorAlertCount > 5; userAlertHistory // Check user risk level from Entra ID Protection AADUserRiskEvents | where TimeGenerated > ago(7d) | where UserPrincipalName == "<UserPrincipalName>" | summarize arg_max(TimeGenerated, RiskLevel), RecentRiskEvents = count() | project RiskLevel, RecentRiskEvents Including this context in the Copilot prompt transforms generic assessments into organization-aware triage decisions. A "suspicious sign-in" for a user who travels internationally every week is very different from the same alert for a user who has never left their home country. Step 4 - Implement Feedback Loops Automated triage is only as good as its accuracy over time. Build a feedback mechanism by tracking Copilot's assessments against analyst final classifications: SecurityIncident | where Tags has "copilot-triaged" | where TimeGenerated > ago(30d) | where Classification != "" | mv-expand Comments | extend CopilotAssessment = extract("Assessment: (True Positive|False Positive|Benign Positive)", 1, tostring(Comments)) | where isnotempty(CopilotAssessment) | summarize Total = dcount(IncidentNumber), Correct = dcountif(IncidentNumber, (CopilotAssessment == "False Positive" and Classification == "FalsePositive") or (CopilotAssessment == "True Positive" and Classification == "TruePositive") or (CopilotAssessment == "Benign Positive" and Classification == "BenignPositive") ) by bin(TimeGenerated, 7d) | extend AccuracyPercent = round(Correct * 100.0 / Total, 1) | order by TimeGenerated asc For this query to work reliably, the automation playbook must write the assessment in a consistent format within the incident comments. Use a structured prefix such as Assessment: True Positive so the regex extraction remains stable. According to Microsoft's published benchmarks and community feedback, Copilot-assisted triage typically achieves 85-92% agreement with senior analyst classifications after prompt tuning - significantly reducing the manual triage burden. A Note on Licensing and Compute Units Security Copilot is licensed through Security Compute Units (SCUs), which are provisioned in Azure. Each prompt session consumes SCUs based on the complexity of the request. For automated triage at scale, plan your SCU capacity carefully - high-volume playbooks can accumulate significant usage. Start with a conservative allocation, monitor consumption through the Security Copilot usage dashboard, and scale up as you validate ROI. Microsoft provides detailed guidance on SCU sizing in the official Security Copilot documentation. Example Scenario - Impossible Travel at Scale Consider a typical enterprise that generates over 200 impossible travel alerts per week. The SOC team spends roughly 15 hours weekly just triaging these. Here is how automated triage addresses this: Detection - Sentinel's built-in impossible travel analytics rule flags the incidents Enrichment - The playbook pulls each user's typical travel patterns from sign-in logs over the past 90 days, VPN usage, and whether the "impossible" location matches any known corporate office or VPN egress point Copilot Analysis - Security Copilot receives the enriched context and classifies each incident Expected Result - Based on common deployment patterns, around 70-75% of impossible travel incidents are auto-closed as benign (VPN, known travel patterns), roughly 20% are downgraded to informational with a triage note, and only about 5% are escalated to analysts as genuine suspicious activity This type of automation can reclaim over 10 hours per week - time that analysts can redirect to proactive threat hunting. Getting Started - Practical Recommendations For teams ready to implement automated triage with Security Copilot and Sentinel, here is a recommended approach: Start small. Pick one high-volume, high-false-positive incident type. Do not try to automate everything at once. Run in shadow mode first. Have the playbook add triage comments but do not auto-close or re-route. Let analysts compare Copilot's assessment with their own for two to four weeks. Tune your prompts. Generic prompts produce generic results. Include organization-specific context - naming conventions, known infrastructure, typical user behavior patterns. Monitor accuracy continuously. Use the feedback loop KQL above. If accuracy drops below 80%, pause automation and investigate. Maintain human oversight. Even at 90%+ accuracy, keep a human review step for high-severity incidents. Automation handles volume - analysts handle judgment. The combination of Security Copilot and Microsoft Sentinel represents a genuine step forward for SOC efficiency. By automating the initial triage pass - summarizing incidents, enriching entities, and providing classification recommendations - analysts are freed to focus on what humans do best: making nuanced security decisions under uncertainty. Feel free to like or/and connect :)
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