Threat Intelligence & Identity Ecosystem Connectors

Microsoft Sentinel’s capability can be greatly enhanced by integrating third-party threat intelligence (TI) feeds (e.g. GreyNoise, Team Cymru) with identity and access logs (e.g. OneLogin, PingOne). This article provides a detailed dive into each connector, data types, and best practices for enrichment and false-positive reduction. We cover how GreyNoise (including PureSignal/Scout), Team Cymru, OneLogin IAM, PingOne, and Keeper integrate with Sentinel – including available connectors, ingested schemas, and configuration. We then outline technical patterns for building TI-based lookup pipelines, scoring, and suppression rules to filter benign noise (e.g. GreyNoise’s known scanners), and enrich alerts with context from identity logs. We map attack chains (credential stuffing, lateral movement, account takeover) to Sentinel data, and propose KQL analytics rules and playbooks with MITRE ATT&CK mappings (e.g. T1110: Brute Force, T1595: Active Scanning). The report also includes guidance on deployment (ARM/Bicep examples), performance considerations for high-volume TI ingestion, and comparison tables of connector features. A mermaid flowchart illustrates the data flow from TI and identity sources into Sentinel analytics. All recommendations are drawn from official documentation and industry sources.

Threat Intel & Identity Connectors Overview

GreyNoise (TI Feed): GreyNoise provides “internet background noise” intelligence on IPs seen scanning or probing the Internet. The Sentinel GreyNoise Threat Intelligence connector (Azure Marketplace) pulls data via GreyNoise’s API into Sentinel’s ThreatIntelligenceIndicator table. It uses a daily Azure Function to fetch indicators (IP addresses and metadata like classification, noise, last_seen) and injects them as STIX-format indicators (Network IPs with provider “GreyNoise”). This feed can then be queried in KQL. Authentication requires a GreyNoise API key and a Sentinel workspace app with Contributor rights. GreyNoise’s goal is to help “filter out known opportunistic traffic” so analysts can focus on real threats. Official docs describe deploying the content pack and workbook template.

 Ingested data: IP-based indicators (malicious vs. benign scans), classifications (noise, riot, etc.), organization names, last-seen dates. All fields from GreyNoise’s IP lookup (e.g. classification, last_seen) appear in ThreatIntelligenceIndicator.NetworkDestinationIP, IndicatorProvider="GreyNoise", and related fields.

 Query:

ThreatIntelligenceIndicator
| where IndicatorProvider == "GreyNoise"
| summarize arg_max(TimeGenerated, *) by NetworkDestinationIP

This yields the latest GreyNoise record per IP.

Team Cymru Scout (TI Context): Team Cymru’s PureSignal™ Scout is a TI enrichment platform. The Team Cymru Scout connector (via Azure Marketplace) ingests contextual data (not raw logs) about IPs, domains, and account usage into Sentinel custom tables. It runs via an Azure Function that, given IP or domain inputs, populates tables like Cymru_Scout_IP_Data_* and Cymru_Scout_Domain_Data_CL. For example, an IP query yields multiple tables: Cymru_Scout_IP_Data_Foundation_CL, ..._OpenPorts_CL, ..._PDNS_CL, etc., containing open ports, passive DNS history, X.509 cert info, fingerprint data, etc. This feed requires a Team Cymru account (username/password) to access the Scout API.

Data types: Structured TI metadata by IP/domain. No native ThreatIndicator insertion; instead, analysts query these tables to enrich events (e.g. join on SourceIP). The Sentintel TechCommunity notes that Scout “enriches alerts with real-time context on IPs, domains, and adversary infrastructure” and can help “reduce false positives”.

OneLogin IAM (Identity Logs): The OneLogin IAM solution (Microsoft Sentinel content pack) ingests OneLogin platform events and user info via OneLogin’s REST API. Using the Codeless Connector Framework, it pulls from OneLogin’s Events API and Users API, storing data in custom tables OneLoginEventsV2_CL and OneLoginUsersV2_CL. Typical events include user sign-ins, MFA actions, app accesses, admin changes, etc. Prerequisites: create an OpenID Connect app in OneLogin (for client ID/secret) and register it in Azure (Global Admin). The connector queries hourly (or on schedule), within OneLogin’s rate limit of 5000 calls/hour.

Data mapping: OneLoginEventsV2_CL (CL suffix indicates custom log) holds event records (time, user, IP, event type, result, etc.); OneLoginUsersV2_CL contains user account attributes. These can be joined or used in analytics. For example, a query might look for failed login events:

OneLoginEventsV2_CL
| where Event_type_s == "UserSessionStart" and Result_s == "Failed"

(Actual field names depend on schema.)

PingOne (Identity Logs): The PingOne Audit connector ingests audit activity from the PingOne Identity platform via its REST API. It creates the table PingOne_AuditActivitiesV2_CL. This includes administrator actions, user logins, console events, etc. You configure a PingOne API client (Client ID/Secret) and set up the Codeless Connector Framework. Logs are retrieved (with attention to PingOne’s license-based rate limits) and appended to the custom table. Analysts can query, for instance, PingOne_AuditActivitiesV2_CL for events like MFA failures or profile changes.

Keeper (Password Vault Logs – optional): Keeper, a password management platform, can forward security events to Sentinel via Azure Monitor. As of latest docs, logs are sent to a custom log table (commonly KeeperLogs_CL) using Azure Data Collection Rules. In Keeper’s guide, you register an Azure AD app (“KeeperLogging”) and configure Azure Monitor data collection; then in the Keeper Admin Console you specify the DCR endpoint. Keeper events (e.g. user logins, vault actions, admin changes) are ingested into the table named (e.g.) Custom-KeeperLogs_CL. Authentication uses the app’s client ID/secret and a monitor endpoint URL. This is a bulk ingest of records, rather than a scheduled pull. 

Data ingested: custom Keeper events with fields like user, action, timestamp. Keeper’s integration is essentially via Azure Monitor (in the older Azure Sentinel approach).

Connector Configuration & Data Ingestion

• Authentication and Rate Limits: Most connectors require API keys or OAuth credentials. GreyNoise and Team Cymru use single keys/credentials, with the Azure Function secured by a Managed Identity. OneLogin and PingOne use client ID/secret and must respect their API limits (OneLogin ~5k calls/hour; PingOne depends on licensing). GreyNoise’s enterprise API allows bulk lookups; the community API is limited (10/day for free), so production integration requires an Enterprise plan.

• Sentinel Tables: Data is inserted either into built-in tables or custom tables. GreyNoise feeds the ThreatIntelligenceIndicator table, populating fields like NetworkDestinationIP and ThreatSeverity (higher if classified “malicious”). Team Cymru’s Scout connector creates many Cymru_Scout_*_CL tables. OneLogin’s solution populates OneLoginEventsV2_CL and OneLoginUsersV2_CL. PingOne yields PingOne_AuditActivitiesV2_CL. Keeper logs appear in a custom table (e.g. KeeperLogs_CL) as shown in Keeper’s guide. Note: Sentinel’s built-in identity tables (IdentityInfo, SigninLogs) are typically for Microsoft identities; third-party logs can be mapped to them via parsers or custom analytic rules but by default arrive in these custom tables.
• Data Types & Schema:

• Threat Indicators: In ThreatIntelligenceIndicator, GreyNoise IPs appear as NetworkDestinationIP with associated fields (e.g. ThreatSeverity, IndicatorProvider="GreyNoise", ConfidenceScore, etc.). (Future STIX tables may be used after 2025.)
• Custom CL Logs: OneLogin events may include fields such as user_id_s, user_login_s, client_ip_s, event_time, etc. (The published parser issues indicate fields like app_name_s, role_id_d, etc.) PingOne logs include eventType, user, clientIP, result. Keeper logs contain Action, UserName, etc. These raw fields can be normalized in analytic rules or parsed by data transformations.

• Identity Info: Although not directly ingested, identity attributes from OneLogin/PingOne (e.g. user roles, group IDs) could be periodically fetched and synced to Sentinel (via custom logic) to populate IdentityInfo records, aiding user-centric hunts.

Configuration Steps :

• GreyNoise: In Sentinel Content Hub, install the GreyNoise ThreatIntel solution. Enter your GreyNoise API key when prompted. The solution deploys an Azure Function (requires write access to Functions) and sets up an ingestion schedule. Verify the ThreatIntelligenceIndicator table is receiving GreyNoise entries
• Team Cymru: From Marketplace install “Team Cymru Scout”. Provide Scout credentials. The solution creates an Azure Function app. It defines a workflow to ingest or lookup IPs/domains. (Often, analysts trigger lookups rather than scheduled ingestion, since Scout is lookup-based.) Ensure roles: the Function’s managed identity needs Sentinel contributor rights.
• OneLogin: Use the Data Connectors UI. Authenticate OneLogin by creating a new Sentinel Web API authentication (with OneLogin’s client ID/secret). Enable both “OneLogin Events” and “OneLogin Users”. No agent is needed. After setup, data flows into OneLoginEventsV2_CL.
• PingOne: Similarly, configure the PingOne connector. Use the PingOne administrative console to register an OAuth client. In Sentinel’s connector blade, enter the client ID/secret and specify desired log types (Audit, maybe IDP logs). Confirm PingOne_AuditActivitiesV2_CL populates hourly.

• Keeper: Register an Azure AD app (“KeeperLogging”) and assign it Monitoring roles (Publisher/Contributor) to your workspace and data collection endpoint. Create an Azure Data Collection Rule (DCR) and table (e.g. KeeperLogs_CL). In Keeper’s Admin Console (Reporting & Alerts → Azure Monitor), enter the tenant ID, client ID/secret, and the DCR endpoint URL (format: https://<DCE>/dataCollectionRules/<DCR_ID>/streams/<table>?api-version=2023-01-01). Keeper will then push logs.
• KQL Lookup: To enrich a Sentinel event with these feeds, you might write:
  
  

OneLoginEventsV2_CL
| where EventType == "UserLogin" and Result == "Success"
| extend UserIP = ClientIP_s
| join kind=inner (
    ThreatIntelligenceIndicator
    | where IndicatorProvider == "GreyNoise" and ThreatSeverity >= 3
    | project NetworkDestinationIP, Category
  ) on $left.UserIP == $right.NetworkDestinationIP

            This joins OneLogin sign-ins with GreyNoise’s list of malicious scanners.

Enrichment & False-Positive Reduction

IOC Enrichment Pipelines: A robust TI pipeline in Sentinel often uses Lookup Tables and Functions. For example, ingested TI (from GreyNoise or Team Cymru) can be stored in reference data or scheduled lookup tables to enrich incoming logs. Patterns include: - Normalization: Normalize diverse feeds into common STIX schema fields (e.g. all IPs to NetworkDestinationIP, all domains to DomainName) so rules can treat them uniformly.
- Confidence Scoring: Assign a confidence score to each indicator (from vendor or based on recency/frequency). For GreyNoise, for instance, you might use classification (e.g. “malicious” vs. “benign”) and history to score IP reputation. In Sentinel’s ThreatIntelligenceIndicator.ConfidenceScore field you can set values (higher for high-confidence IOCs, lower for noisy ones).
- TTL & Freshness: Some indicators (e.g. active C2 domains) expire, so setting a Time-To-Live is critical. Sentinel ingestion rules or parsers should use ExpirationDateTime or ValidUntil on indicators to avoid stale IOCs. For example, extend ValidUntil only if confidence is high.
- Conflict Resolution: When the same IOC comes from multiple sources (e.g. an IP in both GreyNoise and TeamCymru), you can either merge metadata or choose the highest confidence. One approach: use the highest threat severity from any source. Sentinel’s ThreatType tags (e.g. malicious-traffic) can accommodate multiple providers.

False-Positive Reduction Techniques:
- GreyNoise Noise Scoring: GreyNoise’s primary utility is filtering. If an IP is labeled noise=true (i.e. just scanning, not actively malicious), rules can deprioritize alerts involving that IP. E.g. suppress an alert if its source IP appears in GreyNoise as benign scanner.
- Team Cymru Reputation: Use Scout data to gauge risk; e.g. if an IP’s open port fingerprint or domain history shows no malicious tags, it may be low-risk. Conversely, known hostile IP (e.g. seen in ransomware networks) should raise alert level. Scout’s thousands of context tags help refine a binary IOC.
- Contextual Identity Signals: Leverage OneLogin/PingOne context to filter alerts. For instance, if a sign-in event is associated with a high-risk location (e.g. new country) and the IP is a GreyNoise scan, flag it. If an IP is marked benign, drop or suppress. Correlate login failures: if a single IP causes many failures across multiple users, it might be credential stuffing (T1110) – but if that IP is known benign scanner, consider it low priority.
- Thresholding & Suppression: Build analytic suppression rules. Example: only alert on >5 failed logins in 5 min from IP and that IP is not noise. Or ignore DNS queries to domains that TI flags as benign/whitelisted. Apply tag-based rules: some connectors allow tagging known internal assets or trusted scan ranges to avoid alerts.

Use GreyNoise to suppress alerts:

SecurityEvent
| where EventID == 4625 and Account != "SYSTEM"
| join kind=leftanti (
    ThreatIntelligenceIndicator
    | where IndicatorProvider == "GreyNoise" and Classification == "benign"
    | project NetworkSourceIP
  ) on $left.IPAddress == $right.NetworkSourceIP

This rule filters out Windows 4625 login failures originating from GreyNoise-known benign scanners.

Identity Attack Chains & Detection Rules

Modern account attacks often involve sequential activities. By combining identity logs with TI, we can detect advanced patterns. Below are common chains and rule ideas:

• Credential Stuffing (MITRE T1110): Often seen as many login failures followed by a success. Detection: Look for multiple failed OneLogin/PingOne sign-ins for the same or different accounts from a single IP, then a success. Enrich with GreyNoise: if the source IP is in GreyNoise (indicating scanning), raise severity. Rule:
  
  

     let SuspiciousIP =
  OneLoginEventsV2_CL
  | where EventType == "UserSessionStart" and Result == "Failed"
  | summarize CountFailed=count() by ClientIP_s
  | where CountFailed > 5;
OneLoginEventsV2_CL
| where EventType == "UserSessionStart" and Result == "Success"
  and ClientIP_s in (SuspiciousIP | project ClientIP_s)
| join kind=inner (
    ThreatIntelligenceIndicator
    | where ThreatType == "ip"
    | extend GreyNoiseClass = tostring(Classification)
    | project IP=NetworkSourceIP, GreyNoiseClass
  ) on $left.ClientIP_s == $right.IP
| where GreyNoiseClass == "malicious"
| project TimeGenerated, Account_s, ClientIP_s, GreyNoiseClass 

            Tactics: Initial Access (T1110) – Severity: High.

• Account Takeover / Impossible Travel (T1198): Sign-ins from unusual geographies or devices. Detection: Compare user’s current sign-in location against historical baseline. Use OneLogin/PingOne logs: if two logins by same user occur in different countries with insufficient time to travel, trigger. Enrich: if the login IP is also known infrastructure (Team Cymru PDNS, etc.), raise alert. Rule:
  
  

     PingOne_AuditActivitiesV2_CL
| where EventType_s == "UserLogin"
| extend loc = tostring(City_s) + ", " + tostring(Country_s)
| sort by TimeGenerated desc
| partition by User_s
    (
      where TimeGenerated < ago(24h) // check last day
      | summarize count(), min(TimeGenerated), max(TimeGenerated)
    )
| where max_TimeGenerated - min_TimeGenerated < 1h and count_>1 and (range(loc) contains ",")
| project User_s, TimeGenerated, loc 

            (This pseudo-query checks multiple locations in <1 hour.) Tactics: Reconnaissance / Initial Access – Severity: Medium.

• Lateral Movement (T1021): Use of an account on multiple systems/apps. Detection: Two or more distinct application/service authentications by same user within a short time. Use OneLogin app-id fields or audit logs for access. If these are followed by suspicious network activity (e.g. contacting C2 via GreyNoise), escalate. Tactics: Lateral Movement – Severity: High.
• Privilege Escalation (T1098): If an admin account is changed or MFA factors reset in OneLogin/PingOne, especially after anomalous login. Detection: Monitor OneLogin admin events (“User updated”, “MFA enrolled/removed”). Cross-check the actor’s IP against threat feeds. Tactics: Credential Access – Severity: High.

Analytics Rules (KQL)

Below are six illustrative Sentinel analytics rules combining TI and identity logs. Each rule shows logic, tactics, severity, and MITRE IDs. (Adjust field names per your schemas and normalize CL tables as needed.)

1. Multiple Failed Logins from Malicious Scanner (T1110) – High severity. Detect credential stuffing by identifying >5 failed login attempts from the same IP, where that IP is classified as malicious by GreyNoise.
   
   

  let BadIP =
  OneLoginEventsV2_CL
  | where EventType == "UserSessionStart" and Result == "Failed"
  | summarize attempts=count() by SourceIP_s
  | where attempts >= 5;
OneLoginEventsV2_CL
| where EventType == "UserSessionStart" and Result == "Success"
  and SourceIP_s in (BadIP | project SourceIP_s)
| join (
    ThreatIntelligenceIndicator
    | where IndicatorProvider == "GreyNoise" and ThreatSeverity >= 4
    | project MaliciousIP=NetworkDestinationIP
  ) on $left.SourceIP_s == $right.MaliciousIP
| extend AttackFlow="CredentialStuffing", MITRE="T1110"
| project TimeGenerated, UserName_s, SourceIP_s, MaliciousIP

            Logic: Correlate failed-then-success login from same IP plus GreyNoise-malign classification.

1. Impossible Travel / Anomalous Geo (T1198) – Medium severity. A user signs in from two distant locations within an hour.

     // Get last two logins per user
let lastLogins =
  PingOne_AuditActivitiesV2_CL
  | where EventType_s == "UserLogin" and Outcome_s == "Success"
  | sort by TimeGenerated desc
  | summarize first_place=arg_max(TimeGenerated, City_s, Country_s, SourceIP_s, TimeGenerated) by User_s;
let prevLogins =
  PingOne_AuditActivitiesV2_CL
  | where EventType_s == "UserLogin" and Outcome_s == "Success"
  | sort by TimeGenerated desc
  | summarize last_place=arg_min(TimeGenerated, City_s, Country_s, SourceIP_s, TimeGenerated) by User_s;
lastLogins
| join kind=inner prevLogins on User_s
| extend
    dist=geo_distance_2points(first_place_City_s, first_place_Country_s, last_place_City_s, last_place_Country_s)
| where dist > 1000 and (first_place_TimeGenerated - last_place_TimeGenerated) < 1h
| project Time=first_place_TimeGenerated, User=User_s, From=last_place_Country_s, To=first_place_Country_s, MITRE="T1198"

            Logic: Compute geographic distance between last two logins; flag if too far too fast.

1. Suspicious Admin Change (T1098) – High severity. Detect a change to admin settings (like role assign or MFA reset) via PingOne, from a high-risk IP (Team Cymru or GreyNoise) or after failed logins.
   
   

     PingOne_AuditActivitiesV2_CL
| where EventType_s in ("UserMFAReset", "UserRoleChange") // example admin events
| extend ActorIP = tostring(InitiatingIP_s)
| join (
    ThreatIntelligenceIndicator
    | where ThreatSeverity >= 3
    | project BadIP=NetworkDestinationIP
  ) on $left.ActorIP == $right.BadIP
| extend MITRE="T1098"
| project TimeGenerated, ActorUser_s, Action=EventType_s, ActorIP

            Logic: Raise if an admin action originates from known bad IP.

1. Malicious Domain Access (T1498): Medium severity. Internal logs (e.g. DNS or Web proxy) show access to a domain listed by Team Cymru Scout as C2 or reconnaissance.

DeviceDnsEvents
| where QueryType == "A"
| join kind=inner (
    Cymru_Scout_Domain_Data_CL
    | where ThreatTag_s == "Command-and-Control"
    | project DomainName_s
  ) on $left.QueryText == $right.DomainName_s
| extend MITRE="T1498"
| project TimeGenerated, DeviceName, QueryText

            Logic: Correlate internal DNS queries with Scout’s flagged C2 domains. (Requires that domain data is ingested or synced.)

1. Brute-Force Firewall Blocked IP (T1110): Low to Medium severity. Firewall logs show an IP blocked for many attempts, and that IP is not noise per GreyNoise (i.e., malicious scanner).

AzureDiagnostics
| where Category == "NetworkSecurityGroupFlowEvent" and msg_s contains "DIRECTION=Inbound" and Action_s == "Deny"
| summarize attemptCount=count() by IP = SourceIp_s, FlowTime=bin(TimeGenerated, 1h)
| where attemptCount > 50
| join kind=leftanti (
    ThreatIntelligenceIndicator
    | where IndicatorProvider == "GreyNoise" and Classification == "benign"
    | project NoiseIP=NetworkDestinationIP
  ) on $left.IP == $right.NoiseIP
| extend MITRE="T1110"
| project IP, attemptCount, FlowTime

            Logic: Many inbound denies (possible brute force) from an IP not whitelisted by GreyNoise.

1. New Device Enrolled (T1078): Low severity. A user enrolls a new device or location for MFA after unusual login.
   
   

OneLoginEventsV2_CL
| where EventType == "NewDeviceEnrollment"
| join kind=inner (
    OneLoginEventsV2_CL
    | where EventType == "UserSessionStart" and Result == "Success"
    | top 1 by TimeGenerated asc // assume prior login
    | project User_s, loginTime=TimeGenerated, loginIP=ClientIP_s
  ) on User_s
| where loginIP != DeviceIP_s
| extend MITRE="T1078"
| project TimeGenerated, User_s, DeviceIP_s, loginIP

            Logic: Flag if new device added (strong evidence of account compromise).

Note: The above rules are illustrative. Tune threshold values (e.g. attempt counts) to your environment. Map the event fields (EventType, Result, etc.) to your actual schema. Use Severity mapping in rule configs as indicated and tag with MITRE IDs for context.

TI-Driven Playbooks and Automation

Automated response can amplify TI. Patterns include:
- IOC Blocking: On alert (e.g. suspicious IP login), an automation runbook can call Azure Firewall, Azure Defender, or external firewall APIs to block the offending IP. For instance, a Logic App could trigger on the analytic alert, use the TI feed IP, and call AzFWNetworkRule PowerShell to add a deny rule.
- Enrichment Workflow: After an alert triggers, an Azure Logic App playbook can enrich the incident by querying TI APIs. E.g., given an IP from the alert, call GreyNoise API or Team Cymru Scout API in real-time (via HTTP action), add the classification into incident details, and tag the incident accordingly (e.g. GreyNoiseStatus: malicious). This adds context for the analyst.
- Alert Suppression: Implement playbook-driven suppression. For example, an alert triggered by an external IP can invoke a playbook that checks GreyNoise; if the IP is benign, the playbook can auto-close the alert or mark as false-positive, reducing analyst load.
- Automated TI Feed Updates: Periodically fetch open-source or commercial TI and use a playbook to push new indicators into Sentinel’s TI store via the Graph API.
- Incident Enrichment: On incident creation, a playbook could query OneLogin/PingOne for additional user details (like department or location via their APIs) and add as note in the incident.

Performance, Scalability & Costs

TI feeds and identity logs can be high-volume. Key considerations:
- Data Ingestion Costs: Every log and TI indicator ingested into Sentinel is billable by the GB. Bulk TI indicator ingestion (like GreyNoise pulling thousands of IPs/day) can add storage costs. Use Sentinel’s Data Collection Rules (DCR) to apply ingestion-time filters (e.g. only store indicators above a confidence threshold) to reduce volume. GreyNoise feed is typically modest (since it’s daily, maybe thousands of IPs). Identity logs (OneLogin/PingOne) depend on org size – could be megabytes per day. Use sentinel ingestion sl analytic filters to drop low-value logs.
- Query Performance: Custom log tables (OneLogin, PingOne, KeeperLogs_CL) can grow large. Periodically archive old data (e.g. export >90 days to storage, then purge). Use materialized views or scheduled summary tables for heavy queries (e.g. pre-aggregate hourly login counts). For threat indicator tables, leverage built-in indices on IndicatorId and NetworkIP for fast joins. Use project-away _* to remove metadata from large join queries.
- Retention & Storage: Configure retention per table. If historical TI is less needed, set shorter retention. Use Azure Monitor’s tiering/Archive for seldom-used data. For large TI volumes (e.g. feeding multiple TIPs), consider using Sentinel Data Lake (or connecting Log Analytics to ADLS Gen2) to offload raw ingest cheaply.
- Scale-Out Architecture: For very large environments, use multiple Sentinel workspaces (e.g. regional) and aggregate logs via Azure Lighthouse or Sentinel Fusion. TI feeds can be shared: one workspace collects TI, then distribute to others via Azure Sentinel’s TI management (feeds can be published and shared cross-workspaces).
- Connector Limits: API rate limits dictate update frequency. Schedule connectors accordingly (e.g. daily for TI, hourly for identity events). Avoid hourly pulls of already static data (users list can be daily). For OneLogin/PingOne, use incremental tokens or webhooks if possible to reduce load.
- Monitoring Health: Use Sentinel’s Log Analytics and Monitor metrics to track ingestion volume and connector errors. For example, monitor the Functions running GreyNoise/Scout for failures or throttling.

Deployment Checklist & Guide

1. Prepare Sentinel Workspace: Ensure a Log Analytics workspace with Sentinel enabled. Record the workspace ID and region.
2. Register Applications: In Azure AD, create and note any Service Principal needed for functions or connectors (e.g. a Sentinel-managed identity for Functions). In each vendor portal, register API apps and credentials (OneLogin OIDC App, PingOne API client, Keeper AD app).
3. Network & Security: If needed, configure firewall rules to allow outbound to vendor APIs.
4. Install Connectors: In Sentinel Content Hub or Marketplace, install the solutions for GreyNoise TI, Team Cymru Scout, OneLogin IAM, PingOne. Follow each wizard to input credentials. Verify the “Data Types” (Logs, Alerts, etc.) are enabled.
5. Create Tables & Parsers (if manual): For Keeper or unsupported logs, manually create custom tables (via DCR in Azure portal). Import JSON to define fields as shown in Keeper’s docs
6. Test Data Flow: After each setup, wait 1–24 hours and run a simple query on the destination table (e.g. OneLoginEventsV2_CL | take 5) to confirm ingestion.
7. Deploy Ingestion Rules: Use Sentinel Threat intelligence ingestion rules to fine-tune feeds (e.g. mark high-confidence feeds to extend expiration). Optionally tag/whitelist known good.
8. Configure Analytics: Enable or create rules using the KQL above. Place them in the correct threat hunting or incident rule categories (Credential Access, Lateral Movement, etc.). Assign appropriate alert severity.
9. Set up Playbooks: For automated actions (alert enrichment, IOC blocking), create Logic App playbooks. Test with mock alerts (dry run) to ensure correct API calls.
10. Tuning & Baseline: After initial alerts, tune queries (thresholds, whitelists) to reduce noise. Maintain suppression lists (e.g. internal pentest IPs). Use the MITRE mapping in rule details for clarity.
11. Documentation & Training: Document field mappings (e.g. OneLoginEvents fields), and train SOC staff on new TI-enriched alert fields.

Connectors Comparison

Data Flow Diagram

This flowchart shows GreyNoise (GN) feeding the Threat Intelligence table, Team Cymru feeding enrichment tables, and identity sources pushing logs. All data converges into Sentinel, where enrichment lookups inform analytics and automated responses.