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.
Defender Entity Page w/ Sentinel Events Tab
One device is displaying the Sentinel Events Tab, while the other is not. The only difference observed is that one device is Azure AD (AAD) joined and the other is Domain Joined. Could this difference account for the missing Sentinel events data? Any insight would be appreciated!
Microsoft 365 defender alerts not capturing fields (entities) in azure sentinel
We got an alert from 365 defenders to azure sentinel ( A potentially malicious URL click was detected). To investigate this alert we have to check in the 365 defender portal. We noticed that entities are not capturing (user, host, IP). How can we resolve this issue? Note: This is not a custom rule.
Log Ingestion Delay in all Data connectors
Hi, I have integrated multiple log sources in sentinel and all the log sources are ingesting logs between 7:00 pm to 2:00 am I want the log ingestion in real time. I have integrated Azure WAF, syslog, Fortinet, Windows servers. For evidence I am attaching a screenshots. I am totally clueless if anyone can help I will be very thankful!
Playbook when incident trigger is not working
Hi I want to create a playbook to automatically revoke session user when incident with specifics title or gravity is created. But after some test the playbook is'nt run autimacally, it work when I run it manually. I did'nt find what I do wrong. See the image and the code bellow. Thanks in advance! { "definition": { "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#", "contentVersion": "1.0.0.0", "triggers": { "Microsoft_Sentinel_incident": { "type": "ApiConnectionWebhook", "inputs": { "host": { "connection": { "name": "@parameters('$connections')['azuresentinel']['connectionId']" } }, "body": { "callback_url": "@{listCallbackUrl()}" }, "path": "/incident-creation" } } }, "actions": { "Get_incident": { "type": "ApiConnection", "inputs": { "host": { "connection": { "name": "@parameters('$connections')['azuresentinel-1']['connectionId']" } }, "method": "post", "body": { "incidentArmId": "@triggerBody()?['object']?['id']" }, "path": "/Incidents" }, "runAfter": {} }, "Send_e-mail_(V2)": { "type": "ApiConnection", "inputs": { "host": { "connection": { "name": "@parameters('$connections')['office365']['connectionId']" } }, "method": "post", "body": { "To": "email address removed for privacy reasons", "Subject": "Ceci est un test", "Body": "</p> <p class="\"editor-paragraph\"">@{body('Get_incident')?['id']}</p> <p class="\"editor-paragraph\"">@{body('Get_incident')?['properties']?['description']}</p> <p class="\"editor-paragraph\"">@{body('Get_incident')?['properties']?['incidentNumber']}</p> <p>", "Importance": "Normal" }, "path": "/v2/Mail" }, "runAfter": { "Get_incident": [ "Succeeded" ] } } }, "outputs": {}, "parameters": { "$connections": { "type": "Object", "defaultValue": {} } } }, "parameters": { "$connections": { "type": "Object", "value": { "azuresentinel": { "id": "/subscriptions/xxxx/providers/Microsoft.Web/locations/xxxxx/managedApis/xxxxxxx", "connectionId": "/subscriptions/xxxxxxx/resourceGroups/xxxxxx/providers/Microsoft.Web/connections/azuresentinel-Revoke-RiskySessions1", "connectionName": "azuresentinel-Revoke-RiskySessions1", "connectionProperties": { "authentication": { "type": "ManagedServiceIdentity" } } }, "azuresentinel-1": { "id": "/subscriptions/xxxxxx/providers/Microsoft.Web/locations/xxxx/managedApis/xxx", "connectionId": "/subscriptions/xxxxxxx/resourceGroups/xxxxx/providers/Microsoft.Web/connections/xxxx", "connectionName": "xxxxxx", "connectionProperties": { "authentication": { "type": "ManagedServiceIdentity" } } }, "office365": { "id": "/subscriptions/xxxxxx/providers/Microsoft.Web/locations/xxxxx/managedApis/office365", "connectionId": "/subscriptions/xxxxx/resourceGroups/xxxxxx/providers/Microsoft.Web/connections/o365-Test_Send-email-incident-to-xxxx", "connectionName": "o365-Test_Send-email-incident-to-xxxxx" } } } } }
Optimisation For Abnormal Deny Rate for Source IP
Hi, I have recently enabled the "Abnormal Deny Rate for Source IP" alert in Microsoft Sentinel and found it to be quite noisy, generating a large number of alerts many of which do not appear to be actionable. I understand that adjusting the learning period is one way to reduce this noise. However, I am wondering if there are any other optimisation strategies available that do not involve simply changing the learning window. Has anyone had success with tuning this rule using: Threshold-based suppression (e.g. minimum deny count)? Source IP allowlists? Frequency filters (e.g. repeated anomalies over multiple intervals)? Combining with other signal types before generating alerts? Open to any suggestions, experiences, or best practices that others may have found effective in reducing false positives while still maintaining visibility into meaningful anomalies. Thanks in advance,Researching a rule template "FailedLogonToAzurePortal"
Hello, I have the template rule "FailedLogonToAzurePortal"(https://github.com/Azure/Azure-Sentinel/blob/master/Detections/SigninLogs/FailedLogonToAzurePortal.yaml and there is a column of data that I don't understand. The column is "FailedLogonCount" and it was showing inconclusive data because it was showing more data than it was... Here is an example: The issue states that 38 login failures have been detected, but if I investigate in the non-interactive login logs I only see one failure which matches the error code type "50173" but this only shows me one failure, I don't understand where the remaining 37 failures come from... Can you help me?, I am a beginner in KQL and I don't think I understand the context of the alert. Regards.
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.
Behavior Analytics, investigation Priority
Hello, Regarding the field investigation Priority in the Behavior Analytics table, what would be the value that Microsoft considers to be high/critical to look into the user's account? By analyzing the logs i would say, 7 or higher, if someone could tell me, and thank you in advance.
