Correcting links

Author: Anil Godavarthy

articles/sentinel/sentinel-solution-quality-guidance.md

@@ -57,13 +57,13 @@ Hunting queries are KQL queries that are used to proactively search for potentia
 When building Hunting queries, consider the following best practices:
 - Use MITRE framework to identify potential threats: The MITRE framework provides a comprehensive set of tactics, techniques, and procedures (TTPs) that can be used to identify potential threats in your data. By using the MITRE framework, you can ensure that your hunting queries are aligned with industry best practices and can help you identify potential threats more effectively.
 - Create queries that cover all important data columns that are being pulled by the data connector. This ensures that your hunting queries are comprehensive and also provide guidance on whether new data points have to be added to the data connector or if any of the existing data points have to remove.
-- Incorporating Threat intelligence (TI) can provide valuable context for your hunting queries. Incorporating threat intelligence available in Microsoft Sentinel into your hunting queries ensure that the SOC analysts have valuable context to identify potential threats. For more information on threat intelligence in Microsoft Sentinel, see [Threat intelligence in Microsoft Sentinel | Microsoft Learn](/azure/sentinel/threat-intelligence). 
+- Incorporating Threat intelligence (TI) can provide valuable context for your hunting queries. Incorporating threat intelligence available in Microsoft Sentinel into your hunting queries ensure that the SOC analysts have valuable context to identify potential threats. For more information on threat intelligence in Microsoft Sentinel, see [Threat intelligence in Microsoft Sentinel | Microsoft Learn](/azure/sentinel/understand-threat-intelligence). 
 
 > [!NOTE]
 > Although we don't mandate the availability of hunting queries as part of the solution, we strongly recommend that you include hunting queries as part of your solution. Creating hunting queries enable SOC analysts to understand the underlying schema better and inspire them to think of new scenarios.
 
 ## Parsers
-Parsers are KQL functions that transform custom data from third-party products into a normalized ASIM schema. Normalization ensures that SOC analysts don’t have to learn details about new schemas and instead build analytic rules and hunting queries on the normalized schema that they're already familiar with. Review the available ASIM schemas provided by Microsoft Sentinel to identify relevant ASIM schemas (one or more) for your data to ensure easier onboarding for SOC analysts and to ensure that the existing security content written for the ASIM schema is applicable out-of-the-box for your product data. For more information on the available ASIM schemas, see [Advanced Security Information Model (ASIM) schemas | Microsoft Learn](/azure/sentinel/asim-schemas).
+Parsers are KQL functions that transform custom data from third-party products into a normalized ASIM schema. Normalization ensures that SOC analysts don’t have to learn details about new schemas and instead build analytic rules and hunting queries on the normalized schema that they're already familiar with. Review the available ASIM schemas provided by Microsoft Sentinel to identify relevant ASIM schemas (one or more) for your data to ensure easier onboarding for SOC analysts and to ensure that the existing security content written for the ASIM schema is applicable out-of-the-box for your product data. For more information on the available ASIM schemas, see [Advanced Security Information Model (ASIM) schemas | Microsoft Learn](/azure/sentinel/normalization-about-schemas).
 
 Microsoft Sentinel provides several built-in, source-specific parsers for many of the data sources. You may want to modify, or develop new parsers in the following situations:
 - When your device provides events that fit an ASIM schema, but a source-specific parser for your device and the relevant schema isn't available in Microsoft Sentinel.