Archived log data management is a critical skill for Microsoft Security Operations Analysts when investigating security threats that occurred beyond the standard retention period. Azure Sentinel and Microsoft Defender provide mechanisms to store and retrieve historical data for compliance and foren…Archived log data management is a critical skill for Microsoft Security Operations Analysts when investigating security threats that occurred beyond the standard retention period. Azure Sentinel and Microsoft Defender provide mechanisms to store and retrieve historical data for compliance and forensic analysis purposes.
When logs exceed the active retention period, they can be moved to archive storage tiers in Log Analytics workspaces. This archived data remains searchable but requires specific retrieval methods. To access archived logs, analysts use the search feature in Log Analytics, which allows querying data across both active and archived tiers.
The restoration process involves creating a search job that specifies the time range and tables containing the needed data. Search jobs run asynchronously and create a new table with the restored results. Analysts can initiate these jobs through the Azure portal, PowerShell, or REST API. The restored data becomes available in a temporary table that remains accessible for a configurable period.
Key considerations for managing archived logs include understanding cost implications, as restoration incurs charges based on data volume scanned and restored. Planning retention policies appropriately helps balance storage costs with compliance requirements. Organizations should define clear procedures for when archived data retrieval is necessary, such as during incident investigations or audit requests.
Best practices include documenting retention schedules for different log types, establishing approval workflows for archive restoration requests, and regularly testing restoration procedures to ensure data accessibility when needed. Security analysts should also understand the time delays associated with archive retrieval, as search jobs may take considerable time depending on data volume.
Using Azure Resource Graph and Azure Policy helps maintain governance over archived data across multiple workspaces. Proper tagging and organization of archived logs ensures efficient retrieval during time-sensitive security investigations, enabling analysts to reconstruct attack timelines and identify threat patterns from historical data.
Retrieve and Manage Archived Log Data
Why It Is Important
Log data is critical for security investigations, compliance requirements, and threat hunting. However, storing all log data in active storage is expensive and impractical. Organizations must archive older logs while maintaining the ability to retrieve them when needed for investigations, audits, or forensic analysis. Understanding how to manage archived log data ensures security analysts can access historical data efficiently during incident response.
What It Is
Archived log data refers to logs that have been moved from active, frequently queried storage (hot tier) to long-term, cost-effective storage (archive tier). In Microsoft Sentinel and Azure Monitor, this involves:
• Basic Logs - Lower-cost ingestion for high-volume, verbose logs with limited query capabilities • Archived Logs - Data moved to archive tier after the retention period, stored for up to 12 years • Search Jobs - Asynchronous queries used to retrieve data from archived logs • Restore - Bringing archived data back to active storage temporarily for full query capabilities
How It Works
Data Lifecycle: 1. Data is ingested into Analytics logs (interactive retention period) 2. After the retention period, data moves to archive tier 3. Archived data can be accessed via search jobs or restore operations
Search Jobs: • Run asynchronously against archived data • Results are stored in a new table with '_SRCH' suffix • Ideal for targeted queries on specific time ranges • Results remain available for the table's retention period
Restore Operations: • Brings archived data back to hot storage temporarily • Enables full KQL query capabilities • Data available for 2-14 days in restored state • Creates a table with '_RST' suffix • More expensive but provides complete query flexibility
Key Concepts for the Exam
• Retention vs Archive: Interactive retention (up to 2 years) keeps data readily queryable; archive extends total retention up to 12 years • Cost Optimization: Archive tier is significantly cheaper than analytics logs • Query Limitations: Archived data cannot be queried with standard KQL until restored or searched • Table Types: Understand the difference between Analytics, Basic, and Auxiliary log types
Exam Tips: Answering Questions on Retrieve and Manage Archived Log Data
1. Choose Search Jobs when scenarios describe needing specific data from a known time range in archived logs - this is the cost-effective approach
2. Choose Restore when questions mention needing full query capabilities, complex KQL queries, or joining archived data with other tables
3. Remember the suffixes: _SRCH for search job results, _RST for restored data
4. Retention questions: Total retention (interactive + archive) can extend up to 12 years for most tables
5. Cost scenarios: If the question emphasizes budget or cost reduction, archive tier and Basic logs are typically correct answers
6. Time-sensitive investigations: Search jobs take time to complete (asynchronous), so if the scenario requires urgent access to archived data, restore may be preferred despite higher cost