Raw Logs to Detection-Ready Intelligence
Microsoft Sentinel Connector Engineering | RAHSI Framework™
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Raw Logs to Detection-Ready Intelligence | Microsoft Sentinel Connector Engineering | RAHSI Framework™
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A Microsoft Sentinel deployment becomes powerful only when logs are onboarded with engineering discipline: right source, right connector, right schema, right normalization, right detection outcome.
Raw telemetry is not intelligence by default.
It becomes intelligence when the SOC can answer five engineering questions:
- Where should this telemetry come from?
- Which connector path should acquire it?
- Which schema should receive it?
- Which parser should normalize it?
- Which detection, hunting, incident, or automation outcome should use it?
That is the operating line behind this article:
From raw telemetry to detection-ready intelligence.
Microsoft Sentinel connector engineering is the discipline of turning source logs into governed, searchable, normalized, and action-ready security data.
Executive Frame
Microsoft Sentinel is not only a place where logs arrive.
It is a security operations platform where telemetry must be routed, acquired, normalized, protected, and intelligence-enabled before it can support reliable analytics.
The Microsoft-aligned design philosophy is clear:
- Use data connectors to connect Microsoft, Azure, cloud, SaaS, identity, endpoint, network, firewall, DNS, Syslog, CEF, and custom sources.
- Use Content Hub to discover and deploy solution content, connectors, workbooks, analytics, and hunting assets.
- Use Azure Monitor Agent and Data Collection Rules to shape Linux Syslog and CEF ingestion paths.
- Use ASIM normalization to make source-specific logs queryable through consistent schemas.
- Use KQL parsers to translate raw source fields into detection-ready fields.
- Use analytics rules, hunting queries, incidents, workbooks, watchlists, and automation to convert telemetry into SOC execution.
The goal is not to collect every log.
The goal is to collect the telemetry that improves detection, investigation, response, and evidence.
RAHSI Framework™ Operating Model
| RAHSI Layer | Sentinel Engineering Action | Detection-Ready Output |
|---|---|---|
| R — Route | Prioritize telemetry sources by security value | Source-to-use-case map |
| A — Acquire | Select native, API, AMA, Syslog, CEF, or custom ingestion | Connector execution context |
| H — Harmonize | Normalize with ASIM and KQL parsers | Common detection schema |
| S — Secure | Protect credentials, collectors, agents, DCRs, and workspace access | Trusted ingestion path |
| I — Intelligence-enable | Connect data to analytics, hunting, incidents, automation, and CVE evidence | SOC-ready intelligence |
R — Route the Right Telemetry Sources
A strong Sentinel onboarding plan starts with source prioritization.
Not every source has the same value.
High-signal sources usually include:
- Microsoft Entra ID sign-in and audit activity
- Microsoft Defender XDR alerts and incidents
- Azure Activity and Azure resource logs
- Microsoft Defender for Cloud
- Endpoint telemetry
- Firewall logs
- DNS activity
- Proxy logs
- VPN logs
- Identity provider activity
- Syslog and CEF security devices
- Cloud control-plane events
- Critical SaaS audit logs
- Custom application security events
The routing question is simple:
Which telemetry source helps the SOC detect, investigate, hunt, respond, or prove a security outcome?
If the source does not support one of those outcomes, it needs a clear business reason before ingestion.
A — Acquire Logs Through the Right Connector Path
Microsoft Sentinel supports multiple acquisition paths.
The engineering decision is to select the correct connector pattern for the source.
| Connector Path | Best Fit |
|---|---|
| Native Microsoft connector | Microsoft security, identity, cloud, and productivity sources |
| Azure service connector | Azure Activity, resource logs, Defender for Cloud, platform services |
| API connector | SaaS and third-party services with supported API integration |
| Azure Monitor Agent | Syslog and CEF ingestion from Linux collectors and appliances |
| Data Collection Rules | Source filtering, facility selection, transformation path, workspace routing |
| Logs Ingestion API | Custom log ingestion and modern custom table pipelines |
| Custom connector framework | Structured custom ingestion scenarios |
| Syslog connector | Linux and network device event ingestion |
| CEF connector | Security appliances emitting Common Event Format |
| Content Hub solution | Packaged connector, analytic, workbook, parser, and hunting content |
Connector selection should preserve execution context:
- Source system
- Connector type
- Authentication method
- Collection host
- Agent state
- Data Collection Rule
- Target workspace
- Destination table
- Parser strategy
- Detection use case
- Owner
- Review date
That context is what makes Sentinel connector engineering auditable.
H — Harmonize Schemas with ASIM and KQL Parsers
Raw logs usually arrive with source-specific field names.
That is expected designed behavior.
A firewall, identity system, DNS server, proxy, endpoint platform, and SaaS audit API may all describe security events differently.
ASIM helps the SOC normalize those differences.
ASIM provides normalized schemas and parsers so that different log sources can be queried with consistent field names and detection logic.
Normalized models help align telemetry categories such as:
- Authentication
- DNS
- Network session
- Web session
- File activity
- Process activity
- Registry activity
- Audit events
The design benefit is direct:
Normalize once, detect across many sources.
Without normalization, every analytics rule becomes tightly coupled to one vendor table.
With normalization, Sentinel can support portable detection logic across multiple data sources.
S — Secure the Ingestion Pipeline
Connector engineering is also a trust-boundary discipline.
The ingestion path must be protected from weak credentials, unmanaged collectors, unclear ownership, excessive permissions, and uncontrolled data flow.
Secure ingestion design should account for:
- Connector permissions
- API credentials
- OAuth consent
- Service principals
- Managed identities
- Workspace access
- Collector host hardening
- AMA extension health
- Syslog and CEF forwarding configuration
- Data Collection Rule scope
- Table access
- Log retention
- Transformation logic
- Network path
- Change approval
- Evidence ownership
For Syslog and CEF, the collector is not just infrastructure.
It is part of the security data trust boundary.
The SOC should know:
- Which devices forward logs
- Which facilities are collected
- Which severities are included
- Which collector receives events
- Which DCR routes data
- Which table receives records
- Which parser normalizes events
- Which analytics rules depend on the data
That is how raw log forwarding becomes defensible engineering.
I — Intelligence-Enable the Data
A connector is not complete when data starts arriving.
A connector is complete when the telemetry supports security operations.
Detection-ready data should feed:
- Analytics rules
- Scheduled detections
- Near-real-time detections where appropriate
- Hunting queries
- Workbooks
- Incident enrichment
- Watchlists
- Automation rules
- Playbooks
- Threat intelligence matching
- Entity mapping
- Investigation graphs
- CVE evidence packs
- Executive reporting
The final question is:
What does this data enable the SOC to do?
If telemetry does not support an outcome, the connector needs tuning.
Sentinel Connector Engineering Chain
Source
→ Connector
→ Collection path
→ Schema
→ Parser
→ Normalized fields
→ Analytics
→ Hunting
→ Incident
→ Automation
→ Evidence
Each layer has a role.
| Layer | Engineering Question | Sentinel Outcome |
|---|---|---|
| Source | Is this telemetry valuable? | Prioritized collection |
| Connector | How does the data enter Sentinel? | Native, API, AMA, Syslog, CEF, custom |
| Collection path | How is the data routed? | DCR, workspace, table |
| Schema | Where does the record land? | Native or custom table |
| Parser | How is the source interpreted? | KQL or ASIM parser |
| Normalization | Can detections use common fields? | ASIM-aligned queries |
| Analytics | What should alert? | Detection logic |
| Hunting | What should analysts search? | Threat hypothesis |
| Incident | What should become a case? | Triage and investigation |
| Automation | What should run next? | Response workflow |
| Evidence | What proves the path? | Audit and CVE record |
Why Connector Prioritization Matters
Microsoft Sentinel can ingest large volumes of security data.
That does not mean every source should be onboarded at once.
Connector prioritization should balance:
- Detection value
- Investigation value
- Incident response value
- Business criticality
- Identity relevance
- Internet exposure
- Privileged access relevance
- CVE response need
- Data quality
- Parser readiness
- Cost impact
- Retention requirement
- Ownership clarity
A strong starting order is often:
- Identity logs
- Cloud control-plane logs
- Endpoint and Defender telemetry
- Firewall and network security logs
- DNS and proxy logs
- VPN and remote access logs
- Critical SaaS audit logs
- Syslog and CEF security devices
- Custom application security logs
This sequence gives the SOC early coverage for identity, cloud, endpoint, and network paths.
Native Connectors vs Syslog, CEF, API, and Custom Ingestion
Connector engineering is not one pattern.
Each path has a different trust boundary.
Native Microsoft Connectors
Use native connectors where Microsoft provides first-party integration.
These usually offer cleaner onboarding, stronger schema alignment, and better content integration.
Examples include:
- Microsoft Entra ID
- Microsoft Defender XDR
- Microsoft Defender for Cloud
- Microsoft 365
- Azure Activity
- Azure resources
- Microsoft Purview sources where available
API Connectors
Use API connectors when the source exposes supported SaaS or third-party integration.
Engineering focus:
- Authentication
- API rate limits
- Permissions
- Polling interval
- Data freshness
- Error visibility
- Table mapping
- Parser support
AMA-Based Syslog and CEF
Use Azure Monitor Agent with Data Collection Rules for Linux Syslog and CEF collection.
Engineering focus:
- Collector host health
- AMA installation
- DCR association
- Facility selection
- Severity selection
- Forwarder configuration
- Source device routing
- Table mapping
- Parser readiness
- Troubleshooting path
Logs Ingestion API and Custom Tables
Use Logs Ingestion API and modern custom ingestion patterns when source data requires custom tables or application-specific schemas.
Engineering focus:
- Data Collection Endpoint
- Data Collection Rule
- Custom table schema
- Transformation logic
- Authentication
- Payload structure
- Error handling
- Retention
- Parser development
- Detection mapping
ASIM Normalization: The Detection Multiplier
ASIM is the detection multiplier inside Microsoft Sentinel.
It helps the SOC avoid writing separate detections for every vendor format.
A normalized query can search across different products if those products map into a common schema.
Example: Authentication Normalization
imAuthentication
| where EventResult == "Failure"
| summarize FailureCount = count() by SrcIpAddr, TargetUsername, bin(TimeGenerated, 15m)
| where FailureCount > 20
The purpose is not to hide source detail.
The purpose is to give analysts a consistent schema while preserving source-specific fields for deep investigation.
Microsoft-aligned operating line:
Keep source fidelity, but expose normalized fields for detection and hunting.
KQL Parser Engineering
Parsers are the bridge between source logs and detection-ready intelligence.
A strong parser should:
- Preserve original fields
- Map important fields to normalized names
- Convert data types correctly
- Extract entities
- Handle missing fields safely
- Document assumptions
- Support analytics rules
- Support hunting queries
- Support workbook visualization
- Support incident triage
Parser engineering questions:
- Which table contains the source records?
- Which fields represent user, IP, host, action, result, process, URL, or device?
- Which fields map to ASIM?
- Which fields should remain vendor-specific?
- Which detections depend on this parser?
- Which hunting queries validate the parser?
- Which owner approves parser updates?
CVE-Specific Connector Evidence
CVE response depends on evidence.
For Sentinel connector engineering, CVE-ready evidence should show:
- CVE identifier
- Affected product or service
- Relevant telemetry source
- Connector path
- Table name
- Parser name
- Normalized schema
- Detection rule
- Hunting query
- Incident link
- Automation action
- Time range
- Owner
- Validation result
- Closure decision
The CVE evidence chain should look like this:
CVE
→ affected technology
→ required telemetry
→ connector path
→ table
→ parser
→ detection
→ hunting query
→ incident evidence
→ remediation decision
This keeps vulnerability response inside a clear execution context.
This article explains Microsoft’s design philosophy.
Use language like:
- Designed behavior
- Trust boundary
- Execution context
- Connector discipline
- Schema alignment
- Normalization path
- Detection outcome
- Evidence chain
- How Copilot honors labels in practice
The engineering question is not whether Microsoft Sentinel collects data.
The engineering question is whether the organization has routed and shaped the data so that Sentinel can turn it into detection-ready intelligence.
Connector Engineering Checklist
Source Readiness
- [ ] Source has a named business owner
- [ ] Security value is documented
- [ ] Detection use case is defined
- [ ] CVE relevance is known
- [ ] Data freshness requirement is defined
Connector Readiness
- [ ] Connector type is selected
- [ ] Authentication path is approved
- [ ] Workspace is selected
- [ ] Table is known
- [ ] DCR or collection rule is documented where applicable
- [ ] Agent or API health is visible
Schema Readiness
- [ ] Table fields are reviewed
- [ ] Important entities are identified
- [ ] Data types are validated
- [ ] Retention is appropriate
- [ ] Cost impact is understood
Parser Readiness
- [ ] KQL parser exists or is planned
- [ ] ASIM mapping is reviewed
- [ ] Original fields are preserved
- [ ] Parser owner is assigned
- [ ] Parser test queries are stored
Detection Readiness
- [ ] Analytics rule is mapped
- [ ] Hunting query is mapped
- [ ] Entity mapping is defined
- [ ] Incident grouping is considered
- [ ] Automation path is documented
Evidence Readiness
- [ ] CVE evidence path is known
- [ ] Validation query is stored
- [ ] Review cadence is assigned
- [ ] Owner approval is recorded
- [ ] Closure note is retained
Example KQL Validation Queries
Confirm table activity
union withsource=TableName *
| summarize Records=count(), LastSeen=max(TimeGenerated) by TableName
| order by LastSeen desc
Validate Syslog ingestion
Syslog
| summarize Records=count(), LastSeen=max(TimeGenerated) by Computer, Facility, SeverityLevel
| order by LastSeen desc
Validate CommonSecurityLog ingestion
CommonSecurityLog
| summarize Records=count(), LastSeen=max(TimeGenerated) by DeviceVendor, DeviceProduct
| order by LastSeen desc
Check authentication normalization
imAuthentication
| summarize Records=count(), LastSeen=max(TimeGenerated) by EventVendor, EventProduct, EventResult
| order by LastSeen desc
Find high-volume sources
Usage
| where TimeGenerated > ago(7d)
| summarize GB=sum(Quantity) / 1000 by DataType
| order by GB desc
Practical SOC Blueprint
A mature Sentinel connector program should operate like this:
- Build a telemetry source register.
- Prioritize sources by detection value.
- Choose the correct connector path.
- Confirm workspace and table design.
- Secure credentials, agents, collectors, and DCRs.
- Validate ingestion with KQL.
- Normalize with ASIM where useful.
- Build or tune parsers.
- Attach analytics rules.
- Attach hunting queries.
- Attach incident automation.
- Attach CVE evidence logic.
- Review cost, retention, and source quality.
- Recertify connector value on a cadence.
This keeps Sentinel focused on outcomes instead of raw volume.
Microsoft Sentinel connector engineering turns logs into intelligence when the SOC can prove the complete path:
Right source
→ right connector
→ right schema
→ right parser
→ right detection
→ right incident
→ right automation
→ right evidence
That is the RAHSI Framework™ interpretation of Microsoft Sentinel:
- Route the right telemetry sources.
- Acquire logs through native, API, agent, Syslog, or CEF paths.
- Harmonize schemas with ASIM and KQL parsers.
- Secure ingestion pipelines, credentials, agents, and forwarders.
- Intelligence-enable the data for analytics, hunting, incidents, automation, and CVE evidence.
Raw logs are the starting point.
Detection-ready intelligence is the outcome.
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