Introduction:
Organizations are rapidly adopting hybrid and multi-cloud strategies, but traditional SIEM setups often lack unified visibility across providers
attackers exploit these blind spots—a compromised AWS account may be used in conjunction with Azure services to move laterally or exfiltrate data.
QRadar can be as a solution for normalizing, correlating, and detecting threats across these distributed environments.
The move to hybrid and multi-cloud environments has given organizations flexibility and scale—but it has also created new security blind spots. A single enterprise may run workloads across AWS, Azure, and Google Cloud Platform (GCP), each generating logs in different formats and surfaced in different dashboards.
Attackers know this complexity works in their favor. A compromised Azure account could be leveraged to stage lateral movement in AWS, while sensitive data is quietly exfiltrated from GCP. Without a way to correlate these activities, security teams are left with fragmented visibility and slower response times.
This is where IBM QRadar comes in. With its ability to normalize events from diverse log sources and apply powerful correlation rules, QRadar can turn multi-cloud complexity into a unified, actionable security view.
Multi-Cloud Security:
Cite recent industry trends (e.g., over 70% of enterprises use two or more cloud providers).
Recent studies show that more than 70% of enterprises now use two or more cloud providers. While this strategy reduces vendor lock-in, it complicates security operations:
Inconsistent formats: AWS CloudTrail, Azure Activity Logs, and GCP Audit Logs all structure data differently.
Fragmented monitoring: Teams often rely on multiple cloud-native dashboards, each with its own limitations.
Lack of correlation: Events may look harmless in isolation but reveal real threats when combined.
QRadar addresses these gaps by acting as a central nervous system for multi-cloud security monitoring.
Challenges:
Cloud-native logging formats are inconsistent.
Security events are scattered across multiple dashboards.
Lack of correlated insights = slower detection/response.
How/why QRadar Fits In:
Cloud Connectors & Integrations
Normalization: QRadar’s DSM (Device Support Modules) translate raw events into a common taxonomy.
Correlation: Building rules that connect seemingly isolated events across clouds.
QRadar integrates seamlessly with all major cloud providers:
AWS (CloudTrail, GuardDuty, VPC Flow Logs)
Azure (Activity Logs, Azure AD, Sentinel alerts)
GCP (Audit Logs, Security Command Center)
With Device Support Modules (DSMs), QRadar automatically normalizes diverse logs into its taxonomy. Once normalized, correlation rules connect the dots between cloud environments.
Example scenario:
Event 1: Suspicious login from an unusual geo-location in Azure AD.
Event 2: Large data download from AWS S3 within 30 minutes.
QRadar Action: Offense generated, triggering an automated playbook to suspend accounts and alert analysts.
Use cases:
Cloud Account Takeover – Detects an MFA bypass attempt in Azure, followed by privilege escalation in AWS.
Data Exfiltration – Flags abnormal VPC traffic in AWS correlated with file access in GCP.
Insider Threat Detection – Monitors user identities across Office 365, Dropbox, and cloud storage for policy violations.
Implementation Steps:
Ingest Cloud Logs via QRadar Cloud Connect.
Normalize Events with DSMs.
Create Correlation Rules for cross-cloud patterns.
Leverage Reference Sets to track malicious IPs across environments.
Automate Response with QRadar SOAR (block account, disable API keys).
Standard/best practices:
Best Practices
Use tags and naming conventions for cloud log sources to keep them organized.
Continuously tune rules to avoid false positives.
Combine network flow (NetFlow/IPS) with cloud logs for stronger context.
Regularly update DSM & integration packs from IBM.