Multi-Cloud Visibility: AI-Powered Correlation Across AWS, Azure, and GCP

Multi-cloud environments create visibility challenges. AWS, Azure, and GCP each have their own logging formats, consoles, and query languages. Kubernetes adds another layer of complexity. Security teams cannot correlate threats across cloud boundaries. Operations teams struggle to trace issues through hybrid architectures.

LogZilla AI CloudOps solves this problem with unified multi-cloud visibility. Events from all platforms correlate in a single interface. AI analysis spans cloud boundaries to identify security issues, performance problems, and cost anomalies.

The Multi-Cloud Challenge

Most enterprises use multiple cloud platforms:

Each platform generates logs in different formats:

• AWS: CloudTrail, CloudWatch, VPC Flow Logs, GuardDuty
• Azure: Activity Logs, Diagnostic Logs, Azure AD, Sentinel
• GCP: Cloud Audit Logs, Cloud Logging, Security Command Center
• Kubernetes: API server, controller manager, kubelet, application logs

Correlating events across these sources requires manual effort and deep expertise in each platform.

AI-Powered Cloud Correlation

LogZilla AI CloudOps provides unified analysis:

Example prompt: "Analyze all cloud events from the last 2 hours compared to baseline. Identify security issues, performance problems, and cost anomalies across AWS, Azure, and GCP."

AI response includes:

• Executive summary with cross-cloud findings
• Security misconfigurations by platform
• Performance issues with root cause
• Cost anomalies with attribution
• Kubernetes cluster health
• Prioritized remediation recommendations

Download sample CloudOps output (PDF)

Key Capabilities

Cross-Cloud Security Monitoring

LogZilla AI identifies security issues across all platforms:

IAM and Access Control:

• Overly permissive policies (AWS IAM, Azure RBAC, GCP IAM)
• Service account key exposure
• Cross-account access anomalies
• Privilege escalation attempts

Data Security:

• Public storage buckets (S3, Blob, GCS)
• Unencrypted data stores
• Data exfiltration patterns
• Backup policy violations

Network Security:

• Security group misconfigurations
• Public endpoint exposure
• VPC/VNet peering anomalies
• Firewall rule violations

Example finding: "AWS S3 bucket 'prod-data-exports' made public at 14:32 UTC by user [email protected]. Bucket contains 2.3 TB of customer data. Azure AD shows same user authenticated from unusual IP 203.0.113.50 at 14:28 UTC. Recommend immediate bucket policy reversion and user account review."

Kubernetes Visibility

LogZilla provides deep Kubernetes insights:

Cluster Health:

• Control plane component status
• Node resource utilization
• Pod scheduling issues
• Persistent volume claims

Application Health:

• Container restart patterns
• Resource limit violations
• Liveness/readiness probe failures
• Service mesh issues

Security:

• RBAC violations
• Network policy gaps
• Secret access patterns
• Image vulnerability correlation

Example finding: "Kubernetes cluster prod-east showing 47 pod restarts in last hour, up from baseline of 3. Root cause: memory limits exceeded on payment-service deployment. Correlated with AWS RDS connection pool exhaustion. Recommend increasing memory limits and connection pool size."

Cost Anomaly Detection

LogZilla AI identifies unexpected cloud spending:

Spend Analysis:

• Daily/weekly/monthly trends
• Service-level attribution
• Tag-based cost allocation
• Reserved instance utilization

Anomaly Detection:

• Spending spikes vs. baseline
• Unused resource identification
• Right-sizing recommendations
• Commitment optimization

Example finding: "Azure compute spending increased 340% yesterday. Correlated with auto-scaling event triggered by failed health checks on app-gateway-01. Health check failures caused by expired SSL certificate. Estimated unnecessary spend: $4,200. Recommend certificate renewal and auto-scaling policy review."

Platform Integration

AWS Integration

LogZilla collects from AWS sources:

• CloudTrail: API activity and management events
• CloudWatch Logs: Application and system logs
• VPC Flow Logs: Network traffic metadata
• GuardDuty: Threat detection findings
• Security Hub: Aggregated security findings
• Config: Resource configuration changes

Azure Integration

LogZilla collects from Azure sources:

• Activity Log: Subscription-level events
• Diagnostic Logs: Resource-level logs
• Azure AD: Authentication and authorization
• Azure Sentinel: Security events
• Azure Monitor: Metrics and logs
• Azure Policy: Compliance events

GCP Integration

LogZilla collects from GCP sources:

• Cloud Audit Logs: Admin and data access
• Cloud Logging: Application and system logs
• VPC Flow Logs: Network traffic
• Security Command Center: Security findings
• Cloud Asset Inventory: Resource changes

Kubernetes Integration

LogZilla collects from Kubernetes:

• API Server: All API requests
• Controller Manager: Controller operations
• Scheduler: Pod scheduling decisions
• Kubelet: Node-level operations
• Application Logs: Container stdout/stderr
• Events: Cluster events

Real-World Example

A LogZilla customer detected a cross-cloud security incident:

Prompt: "Analyze cloud events from the last 4 hours. Identify security issues and unusual activity across all platforms."

Results (550 events analyzed):

• Azure AD brute force attack detected (202 failed authentications)
• Attacker successfully authenticated to compromised account
• AWS console access from same source IP
• S3 bucket policy modified to allow public access
• 2.1 GB data downloaded before detection

The AI correlated events across Azure AD and AWS to identify the attack chain. Traditional single-platform monitoring would have shown isolated events without the connection.

Compliance Across Clouds

LogZilla AI maps findings to compliance frameworks:

Reports include specific control references and evidence for auditors.

Cloud Cost Optimization

AI CloudOps identifies cost optimization opportunities across platforms:

Unused Resource Detection

Right-Sizing Recommendations

AI analyzes resource utilization patterns to recommend right-sizing:

text
Wrap: OnCopyFullscreen
Cost Optimization Report - AWS Account 123456789
================================================
Instance: prod-web-01 (m5.4xlarge)
  Current cost: $489/month
  Average CPU: 12%
  Average memory: 28%
  Recommendation: Downsize to m5.xlarge
  Projected savings: $367/month (75%)

Instance: prod-db-01 (r5.2xlarge)
  Current cost: $362/month
  Average CPU: 67%
  Average memory: 82%
  Recommendation: Keep current size
  Note: Memory-optimized workload, utilization appropriate

Reserved Instance Optimization

AI identifies opportunities for commitment-based savings:

Architecture Options

Centralized Collection

All cloud logs forward to LogZilla:

text
Wrap: OnCopyFullscreen
[AWS CloudWatch] ─────┐
[Azure Monitor] ──────┼──→ [LogZilla] ──→ [AI Analysis]
[GCP Cloud Logging] ──┤
[Kubernetes] ─────────┘

Benefits: Single pane of glass, cross-cloud correlation, unified retention.

Hybrid Collection

LogZilla complements cloud-native tools:

text
Wrap: OnCopyFullscreen
[AWS] ──→ [CloudWatch] ──→ [LogZilla]
[Azure] ──→ [Sentinel] ──→ [LogZilla]
[GCP] ──→ [Chronicle] ──→ [LogZilla]

Benefits: Leverage existing investments, add AI correlation layer.

Implementation Approach

Phase 1: Cloud Connectivity (Week 1)

1. Configure AWS CloudWatch log forwarding
2. Set up Azure Event Hub streaming
3. Enable GCP Pub/Sub export
4. Deploy Kubernetes log collectors

Phase 2: Normalization (Week 2)

1. Map cloud-specific fields to common schema
2. Configure identity correlation across platforms
3. Establish resource tagging standards
4. Validate cross-cloud event correlation

Phase 3: AI Analysis (Week 3+)

1. Enable AI CloudOps analysis
2. Configure security monitoring rules
3. Set up cost anomaly detection
4. Integrate with incident response workflows

Multi-Cloud Challenges Solved

AI CloudOps addresses specific multi-cloud operational challenges:

Identity Correlation

Users have different identities across clouds:

LogZilla correlates these identities to track user activity across platforms. A single user's actions in AWS, Azure, and GCP appear as unified activity.

Event Time Synchronization

Cloud providers use different timestamp formats and time zones. LogZilla normalizes all timestamps to UTC for accurate correlation:

• AWS CloudTrail: ISO 8601 format
• Azure Activity Log: ISO 8601 with timezone
• GCP Audit Logs: Protobuf timestamp
• Kubernetes: RFC 3339

Normalized timestamps enable accurate timeline reconstruction across clouds.

Resource Naming Conventions

Resources have different naming patterns across clouds:

LogZilla maps these resources to a common taxonomy for unified reporting.

Micro-FAQ

What is AI CloudOps?

AI CloudOps uses artificial intelligence to correlate events across multiple cloud platforms. It provides unified visibility into AWS, Azure, GCP, and Kubernetes environments from a single interface.

How does LogZilla correlate events across clouds?

LogZilla normalizes events from all cloud platforms into a common format while preserving cloud-specific details. AI analysis identifies relationships and dependencies across cloud boundaries.

Can LogZilla detect cloud security misconfigurations?

Yes. LogZilla AI identifies security issues including overly permissive IAM policies, public storage buckets, unencrypted resources, and compliance violations across all cloud platforms.

Does LogZilla help with cloud cost management?

LogZilla AI detects cost anomalies, identifies unused resources, and correlates spending spikes with operational events. Reports include specific cost optimization recommendations.

Next Steps

Multi-cloud environments require unified visibility. LogZilla AI CloudOps correlates events across AWS, Azure, GCP, and Kubernetes to identify security issues, performance problems, and cost anomalies. Watch the AI CloudOps demo to see cross-cloud correlation in action.