Enterprise Cloud Architecture Checklist

Cloud Strategy

Required

Multi-Cloud Strategy
Implementation Questions:
- Have you performed a TCO analysis comparing AWS, Azure, GCP costs for your workloads?
- Which workloads require multi-cloud for vendor lock-in avoidance vs single cloud optimization?
- Have you evaluated data egress costs between cloud providers for your architecture?
- What is your strategy for managing different cloud provider APIs and services?
- Have you identified which services require cloud-agnostic solutions (Kubernetes, Terraform)?
- How will you handle identity federation and SSO across multiple cloud providers?
Key Considerations:
- Establish cloud broker patterns for workload placement based on cost and performance
- Design for data gravity to minimize cross-cloud transfer costs
- Implement cloud-agnostic IaC using Terraform or Pulumi for portability
- Create abstraction layers for cloud-specific services to enable provider switching
Red Flags:
- Using multiple clouds without clear business justification or cost analysis
- Ignoring data transfer costs which can exceed compute costs in multi-cloud
- No unified monitoring strategy across cloud providers
- Lack of skills or tooling to manage complexity of multi-cloud operations
Cloud Governance Model
Implementation Questions:
- Have you defined cloud account structure and organizational unit hierarchy?
- What are your tagging standards for cost allocation and resource management?
- How will you enforce naming conventions across all cloud resources?
- Have you established approval workflows for cloud resource provisioning?
- What guardrails prevent deployment of non-compliant resources?
- How do you track and manage cloud service limits and quotas?
Key Considerations:
- Implement policy-as-code using AWS SCPs, Azure Policy, or GCP Organization Policies
- Establish mandatory tags for cost center, environment, owner, and data classification
- Create landing zones with pre-configured security and network controls
- Define clear RACI matrix for cloud operations and decision-making
Red Flags:
- No automated enforcement of governance policies leading to configuration drift
- Missing cost allocation tags resulting in inability to track spending
- Allowing direct production access without change management processes
- No regular audits of cloud resource compliance with governance standards
Service Selection
Implementation Questions:
- Have you evaluated managed services vs self-managed for TCO and operational overhead?
- What are your criteria for PaaS vs IaaS vs SaaS selection?
- How do you assess vendor lock-in risk for proprietary cloud services?
- Have you benchmarked performance requirements against service SLAs?
- What is your evaluation matrix for serverless vs container vs VM deployments?
- How do you validate cloud service compliance with your security requirements?
Key Considerations:
- Prioritize managed services to reduce operational overhead and improve reliability
- Evaluate services based on RTO/RPO requirements and availability SLAs
- Consider data residency and sovereignty requirements for service selection
- Assess integration complexity with existing systems and data flows
Red Flags:
- Selecting services based solely on features without considering operational costs
- Choosing cutting-edge services without stable terraform providers or APIs
- Not considering regional availability leading to architecture limitations
- Ignoring service limits that could impact scalability requirements
Migration Strategy
Implementation Questions:
- Have you completed application portfolio analysis using the 7Rs framework?
- What is your wave planning strategy for migration sequencing?
- How will you handle database migrations with minimal downtime?
- Have you identified technical debt that must be addressed before migration?
- What is your rollback strategy if migrations fail?
- How will you validate performance and functionality post-migration?
Key Considerations:
- Start with low-risk, high-value workloads to build migration expertise
- Implement automated discovery tools to map application dependencies
- Design for hybrid connectivity during transition period
- Plan for data migration bandwidth requirements and transfer windows
Red Flags:
- Lift-and-shift approach without optimization leading to excessive cloud costs
- No testing of disaster recovery procedures post-migration
- Underestimating data transfer time and costs for large datasets
- Missing critical application dependencies discovered only during cutover

Suggested

Cloud Center of Excellence
Implementation Questions:
- Have you defined the CCoE charter, roles, and responsibilities?
- What is your cloud skills assessment and training program?
- How will the CCoE provide self-service capabilities to development teams?
- Have you established cloud architecture review boards and processes?
- What metrics will measure CCoE effectiveness and cloud adoption success?
- How will you capture and share cloud best practices across teams?
Key Considerations:
- Create reusable IaC modules and reference architectures
- Establish cloud advisory services for architecture and migration planning
- Implement inner-source model for sharing cloud automation
- Define cloud maturity model and assessment framework
Red Flags:
- CCoE becoming a bottleneck instead of an enabler for cloud adoption
- Lack of executive sponsorship and authority for CCoE initiatives
- No clear success metrics or accountability for cloud transformation
- Insufficient staffing or skills to support enterprise cloud needs
Cloud Maturity Model
Implementation Questions:
- Have you baselined current cloud maturity across people, process, and technology?
- What are your target maturity levels for each cloud capability area?
- How often will you reassess cloud maturity and track progress?
- Have you mapped maturity improvements to business value metrics?
- What is your roadmap for advancing from current to target maturity?
- How do you benchmark against industry cloud maturity standards?
Key Considerations:
- Use frameworks like AWS CAF or Azure WAF for maturity assessment
- Focus on automation maturity as a key driver of operational efficiency
- Align maturity goals with business outcomes, not just technical metrics
- Create capability maps showing current vs desired state
Red Flags:
- Pursuing advanced capabilities without mastering foundational practices
- No clear linkage between maturity improvements and business value
- Inconsistent maturity across teams leading to integration challenges
- Focusing only on technology maturity while ignoring process and people

Infrastructure Design

Required

Network Architecture
Implementation Questions:
- Have you designed VPC/VNet architecture with proper CIDR planning for growth?
- What is your network segmentation strategy using subnets and security groups?
- How will you implement hub-spoke vs mesh network topologies?
- Have you planned for private connectivity using VPN or Direct Connect/ExpressRoute?
- What is your DNS and IPAM strategy for cloud resources?
- How will you implement network flow logging and traffic analysis?
Key Considerations:
- Implement transit gateway or virtual WAN for scalable multi-VPC connectivity
- Use private endpoints/Private Link to avoid internet exposure
- Design for overlapping IP ranges in M&A scenarios
- Implement network firewall or NVAs for east-west traffic inspection
Red Flags:
- Using default VPCs without proper network segmentation
- No network flow logs leading to blind spots in security monitoring
- Exposing resources to internet when private connectivity is available
- CIDR blocks too small to accommodate future growth
Identity Architecture
Implementation Questions:
- Have you implemented SAML/OIDC federation with your corporate identity provider?
- What is your strategy for managing service accounts and API keys?
- How do you enforce MFA for all human and privileged access?
- Have you implemented just-in-time access and privilege escalation?
- What is your approach to cross-account role assumption?
- How do you manage and rotate credentials for applications?
Key Considerations:
- Implement zero-trust principles with continuous verification
- Use managed identities/instance profiles instead of static credentials
- Implement privileged access management (PAM) for admin access
- Design for break-glass scenarios with proper auditing
Red Flags:
- Using root accounts or over-privileged service accounts
- Static API keys embedded in code or configuration files
- No MFA enforcement for console or API access
- Lack of regular access reviews and certification
Security Architecture
Implementation Questions:
- Have you implemented defense-in-depth with multiple security layers?
- What is your strategy for workload isolation and microsegmentation?
- How do you implement security scanning in CI/CD pipelines?
- Have you deployed cloud-native security tools (GuardDuty, Security Center)?
- What is your incident response plan for cloud security events?
- How do you ensure encryption at rest and in transit for all data?
Key Considerations:
- Implement CSPM for continuous compliance monitoring
- Deploy CWPP for runtime protection of cloud workloads
- Use cloud-native KMS for encryption key management
- Implement security hub/center for centralized visibility
Red Flags:
- Relying solely on perimeter security without workload protection
- No automated remediation for security misconfigurations
- Missing security logs or insufficient retention periods
- Unencrypted data storage or transmission paths
Storage Strategy
Implementation Questions:
- Have you mapped storage tiers to data access patterns and costs?
- What are your retention policies and automated lifecycle transitions?
- How do you handle backup, replication, and disaster recovery for data?
- Have you implemented data classification and tagging standards?
- What is your strategy for data archival and long-term retention?
- How do you optimize storage costs while meeting performance SLAs?
Key Considerations:
- Use intelligent tiering to automatically optimize storage costs
- Implement cross-region replication for critical data
- Design for immutable backups to prevent ransomware attacks
- Consider cold storage (Glacier/Archive) for compliance data
Red Flags:
- No lifecycle policies leading to accumulation of unnecessary data costs
- Using premium storage tiers for infrequently accessed data
- Missing backup testing and recovery validation procedures
- Storing sensitive data without proper encryption or access controls

Suggested

Edge Computing
Implementation Questions:
- Have you identified use cases requiring sub-10ms latency at the edge?
- What is your strategy for managing distributed edge infrastructure?
- How will you handle data synchronization between edge and cloud?
- Have you evaluated AWS Outposts, Azure Stack Edge, or GCP Anthos?
- What is your approach to edge security and device management?
- How do you monitor and troubleshoot distributed edge deployments?
Key Considerations:
- Design for intermittent connectivity and offline operations
- Implement edge caching strategies to reduce bandwidth costs
- Use containerization for consistent edge deployments
- Plan for edge hardware lifecycle and refresh cycles
Red Flags:
- Deploying edge without clear latency or bandwidth requirements
- No strategy for edge device security and patching
- Underestimating operational complexity of distributed edge
- Missing data governance for edge-collected sensitive data
IoT Integration
Implementation Questions:
- Have you evaluated IoT platforms (AWS IoT Core, Azure IoT Hub)?
- What is your device provisioning and certificate management strategy?
- How will you handle IoT data ingestion at scale (millions of events)?
- Have you designed for bi-directional communication and OTA updates?
- What is your approach to IoT device security and authentication?
- How do you process and analyze IoT streaming data in real-time?
Key Considerations:
- Implement device shadows/twins for offline state management
- Use MQTT or lightweight protocols for constrained devices
- Design for device fleet management and bulk operations
- Plan for time-series data storage and analytics
Red Flags:
- No device identity management leading to security vulnerabilities
- Direct database connections from IoT devices causing scaling issues
- Missing rate limiting and throttling for device communications
- No strategy for handling device firmware updates securely

High Availability

Required

Multi-Region Design
Implementation Questions:
- Have you designed for active-active vs active-passive multi-region?
- What is your data replication strategy across regions?
- How do you handle split-brain scenarios and conflict resolution?
- Have you implemented global load balancing with health checks?
- What is your RPO/RTO for regional failover scenarios?
- How do you test regional failover without impacting production?
Key Considerations:
- Use global databases (DynamoDB Global, Cosmos DB) for multi-region data
- Implement circuit breakers to prevent cascading failures
- Design for eventual consistency in distributed systems
- Plan for data sovereignty and compliance requirements
Red Flags:
- Multi-region architecture without clear availability requirements
- No regular testing of failover procedures
- Ignoring cross-region data transfer costs in architecture
- Synchronous replication causing performance degradation
Load Balancing
Implementation Questions:
- Have you implemented geo-routing based on user location?
- What health check strategies validate application availability?
- How do you handle SSL termination and certificate management?
- Have you configured appropriate load balancing algorithms (round-robin, least connections)?
- What is your strategy for DDoS protection at the load balancer level?
- How do you implement session affinity when required?
Key Considerations:
- Use Application Load Balancers for HTTP/HTTPS with WAF integration
- Implement connection draining for graceful instance termination
- Configure cross-zone load balancing for even distribution
- Use Global Accelerator or Front Door for improved global performance
Red Flags:
- Single point of failure with only one load balancer
- Health checks that don't validate actual application functionality
- No rate limiting exposing backend to overload
- Missing access logs for security and troubleshooting
Auto Scaling
Implementation Questions:
- Have you identified appropriate scaling metrics (CPU, memory, custom)?
- What are your scale-up and scale-down thresholds and cooldown periods?
- How do you handle application warm-up time during scaling events?
- Have you implemented predictive scaling based on usage patterns?
- What is your strategy for database connection pooling during scaling?
- How do you prevent flapping and unnecessary scaling actions?
Key Considerations:
- Use target tracking policies for maintaining specific metric values
- Implement step scaling for gradual capacity adjustments
- Configure maximum and minimum capacity limits for cost control
- Test scaling policies under load to validate effectiveness
Red Flags:
- Auto-scaling based solely on CPU without considering memory or I/O
- No capacity limits leading to unlimited scaling and costs
- Scaling policies that react too slowly to traffic spikes
- Database becoming bottleneck during application scaling
Failover Strategy
Implementation Questions:
- Have you documented detailed runbooks for failover scenarios?
- What is your automated vs manual failover decision criteria?
- How do you validate data consistency after failover?
- Have you implemented automated failover testing in non-production?
- What is your communication plan during failover events?
- How do you handle failback after primary region recovery?
Key Considerations:
- Implement automated health checks to trigger failover
- Use Route 53 or Traffic Manager for DNS-based failover
- Design for graceful degradation during partial failures
- Document clear RPO/RTO targets for each application tier
Red Flags:
- Failover procedures never tested in production-like environment
- No clear decision authority for initiating failover
- Missing rollback procedures if failover causes issues
- DNS TTL values too high preventing quick failover

Suggested

Chaos Engineering
Implementation Questions:
- Have you established chaos engineering principles and boundaries?
- What failure scenarios are included in your chaos experiments?
- How do you implement gradual failure injection (blast radius)?
- Have you automated chaos experiments in CI/CD pipelines?
- What metrics measure system resilience during chaos tests?
- How do you ensure chaos experiments don't impact customers?
Key Considerations:
- Start with non-production environments before production testing
- Use tools like Chaos Monkey, Gremlin, or Litmus
- Focus on common failures: network latency, instance termination, AZ failure
- Implement abort mechanisms to stop experiments if needed
Red Flags:
- Running chaos experiments without proper observability
- No hypothesis or learning objectives for chaos tests
- Testing in production without gradual rollout strategy
- Lack of communication about chaos engineering activities
SLA Monitoring
Implementation Questions:
- Have you defined SLIs, SLOs, and error budgets for each service?
- What synthetic monitoring validates availability from user perspective?
- How do you calculate and report composite SLAs for dependencies?
- Have you implemented real-time SLA dashboards and alerting?
- What is your process for SLA breach investigation and remediation?
- How do you track and report SLA compliance to stakeholders?
Key Considerations:
- Monitor from multiple geographic locations for global SLAs
- Include performance metrics beyond just availability
- Implement burn rate alerts for error budget consumption
- Automate SLA reporting with historical trend analysis
Red Flags:
- SLAs defined without baseline measurements
- No consideration of third-party service SLAs in calculations
- Missing SLA monitoring leading to surprise breaches
- Overly aggressive SLAs that are consistently missed

Cost Optimization

Required

Cost Monitoring
Implementation Questions:
- Have you implemented real-time cost monitoring with anomaly detection?
- What cost allocation tags enable departmental chargeback?
- How do you track and optimize unused or underutilized resources?
- Have you set up budget alerts at multiple threshold levels?
- What tools provide cost visibility (AWS Cost Explorer, Azure Cost Management)?
- How do you forecast future costs based on growth projections?
Key Considerations:
- Implement automated daily/weekly cost reports to stakeholders
- Use third-party tools (CloudHealth, Cloudability) for multi-cloud visibility
- Create cost anomaly detection rules for unexpected spikes
- Track unit economics (cost per transaction, per user)
Red Flags:
- No proactive cost monitoring leading to bill shock
- Missing or inconsistent tagging preventing cost attribution
- No regular review of cost optimization recommendations
- Lack of accountability for cloud spending by teams
Resource Optimization
Implementation Questions:
- Have you implemented automated rightsizing recommendations?
- What is your strategy for identifying and removing unused resources?
- How do you automatically stop/start non-production resources?
- Have you evaluated spot instances for fault-tolerant workloads?
- What automation identifies and deletes unattached volumes/IPs?
- How do you optimize data transfer costs between services?
Key Considerations:
- Use AWS Compute Optimizer or Azure Advisor for recommendations
- Implement automated scheduling for development environments
- Leverage ARM-based instances (Graviton, Ampere) for better price-performance
- Optimize container packing density in ECS/Kubernetes
Red Flags:
- Running instances 24/7 when only needed during business hours
- Over-provisioned instances never rightsized
- Paying for idle resources like unused load balancers
- No automation for cost optimization recommendations
Reserved Capacity
Implementation Questions:
- Have you analyzed usage patterns to identify reservation candidates?
- What is your strategy for RIs vs Savings Plans vs Spot?
- How do you manage reservation utilization and coverage?
- Have you evaluated convertible vs standard reservations?
- What is your process for reservation modifications and exchanges?
- How do you handle reservations for growing/shrinking workloads?
Key Considerations:
- Start with 1-year terms before committing to 3-year reservations
- Use Savings Plans for flexibility across instance families
- Monitor RI utilization weekly to maximize value
- Consider RI marketplace for selling unused reservations
Red Flags:
- Purchasing reservations without analyzing actual usage
- No monitoring of reservation utilization rates
- Over-committing to reservations limiting flexibility
- Missing renewal dates leading to rate increases
Budget Controls
Implementation Questions:
- Have you set budgets at account, project, and tag levels?
- What are your alert thresholds (50%, 80%, 100%, forecast)?
- How do you implement hard limits to prevent overspending?
- Have you configured actions for budget threshold breaches?
- What is your approval process for budget increases?
- How do you track budget vs actual spending monthly?
Key Considerations:
- Set both actual and forecasted budget alerts
- Implement service quotas to prevent runaway costs
- Use SCPs or Azure Policy to restrict expensive services
- Create separate budgets for production vs non-production
Red Flags:
- No enforced spending limits allowing unlimited consumption
- Budget alerts that no one monitors or acts upon
- Missing forecasted spend alerts for proactive management
- No process for investigating and remediating budget overruns

Suggested

FinOps Practices
Implementation Questions:
- Have you established a FinOps team with defined responsibilities?
- What is your cloud cost optimization cadence and review process?
- How do you enable engineering teams to see cost impact of changes?
- Have you implemented showback or chargeback models?
- What KPIs measure FinOps program effectiveness?
- How do you balance cost optimization with performance/reliability?
Key Considerations:
- Establish FinOps maturity model (Crawl, Walk, Run)
- Implement cost as a metric in CI/CD pipelines
- Create cloud cost optimization playbooks
- Foster cost-aware culture through training and gamification
Red Flags:
- FinOps as purely finance function without engineering involvement
- No regular cadence for cost optimization reviews
- Lack of tooling providing cost visibility to developers
- Cost optimization efforts that compromise reliability
Cost Attribution
Implementation Questions:
- Have you implemented mandatory tagging for cost allocation?
- What is your cost attribution model (direct, shared, overhead)?
- How do you allocate shared service costs fairly?
- Have you automated chargeback report generation?
- What tools enable self-service cost visibility for teams?
- How do you handle untagged or shared resource costs?
Key Considerations:
- Enforce tagging compliance through automation and policies
- Implement hierarchical cost allocation (org/dept/project)
- Create cost dashboards for team-level visibility
- Use account structure to simplify cost attribution
Red Flags:
- Significant unallocated costs reducing accountability
- Manual cost allocation processes prone to errors
- No enforcement of tagging standards
- Teams unaware of their cloud spending impact

Security & Compliance

Required

Cloud Security Controls
Implementation Questions:
- Have you enabled cloud-native threat detection services?
- What is your strategy for vulnerability scanning and patching?
- How do you implement network segmentation and microsegmentation?
- Have you configured security information and event management (SIEM)?
- What controls prevent data exfiltration and insider threats?
- How do you ensure secure configuration baselines?
Key Considerations:
- Enable AWS GuardDuty, Azure Sentinel, or GCP Security Command Center
- Implement Web Application Firewall (WAF) for public endpoints
- Use cloud security posture management (CSPM) tools
- Configure VPC Flow Logs and network traffic analysis
Red Flags:
- No centralized security monitoring across cloud accounts
- Missing critical security logs or insufficient retention
- Default security group rules allowing unrestricted access
- No automated response to security findings
Data Protection
Implementation Questions:
- Have you implemented encryption at rest for all storage services?
- What is your key management strategy (KMS, HSM, BYOK)?
- How do you ensure TLS/SSL for all data in transit?
- Have you implemented data loss prevention (DLP) controls?
- What is your approach to secrets management?
- How do you handle data residency and sovereignty requirements?
Key Considerations:
- Use customer-managed keys (CMK) for sensitive data
- Implement automatic key rotation policies
- Use secrets managers instead of hardcoded credentials
- Enable S3 bucket encryption and block public access
Red Flags:
- Unencrypted databases or object storage
- Secrets stored in plain text in code or configuration
- No key rotation leading to compromised encryption
- Missing encryption for backup data
Compliance Framework
Implementation Questions:
- Have you mapped compliance requirements to cloud controls?
- What automated compliance scanning validates configurations?
- How do you maintain evidence for audit requirements?
- Have you implemented compliance-as-code practices?
- What is your process for compliance gap remediation?
- How do you track compliance across multiple frameworks?
Key Considerations:
- Use AWS Config, Azure Policy, or GCP Security Health Analytics
- Implement continuous compliance monitoring
- Maintain compliance artifacts in centralized repository
- Use cloud compliance programs (SOC2, PCI-DSS, HIPAA)
Red Flags:
- No automated compliance checking leading to drift
- Missing audit trails for compliance evidence
- Manual compliance processes that don't scale
- Lack of visibility into compliance posture
Access Management
Implementation Questions:
- Have you implemented least privilege access principles?
- What is your process for access provisioning and deprovisioning?
- How do you manage service accounts and API keys?
- Have you implemented privileged access management (PAM)?
- What is your approach to cross-account access?
- How often do you perform access reviews and recertification?
Key Considerations:
- Use role-based access control (RBAC) aligned with job functions
- Implement just-in-time access for privileged operations
- Enforce MFA for all console and programmatic access
- Use temporary credentials instead of long-lived keys
Red Flags:
- Overly permissive IAM policies (e.g., wildcard permissions)
- Service accounts with excessive privileges
- No regular review of access permissions
- Shared accounts or credentials among team members

Suggested

Security Automation
Implementation Questions:
- Have you automated security scanning in CI/CD pipelines?
- What automated remediation responds to security findings?
- How do you implement policy-as-code for security controls?
- Have you automated compliance checking and reporting?
- What is your approach to automated incident response?
- How do you validate security controls effectiveness?
Key Considerations:
- Use AWS Systems Manager, Azure Automation, or GCP Cloud Functions
- Implement automated patching for operating systems
- Create security runbooks for common remediation tasks
- Use infrastructure-as-code scanning tools
Red Flags:
- Manual security processes that can't scale
- No automated response to critical security findings
- Security testing only at deployment, not continuously
- Lack of security metrics and KPIs
Cloud Workload Protection Platform (CWPP)
Implementation Questions:
- Have you evaluated CWPP solutions (Prisma Cloud, Dome9, Aqua)?
- What runtime protection covers containers and serverless?
- How do you detect and prevent runtime attacks?
- Have you implemented file integrity monitoring?
- What is your strategy for vulnerability management?
- How do you handle zero-day threat protection?
Key Considerations:
- Deploy agents or agentless scanning based on workload type
- Implement admission control for Kubernetes deployments
- Enable behavioral analysis for anomaly detection
- Integrate with SIEM for centralized alerting
Red Flags:
- No runtime protection leaving workloads vulnerable
- CWPP not covering all workload types (VMs, containers, serverless)
- Missing integration with incident response processes
- No baseline behavior established for anomaly detection
Cloud Security Posture Management (CSPM)
Implementation Questions:
- Have you deployed CSPM across all cloud accounts?
- What compliance frameworks does your CSPM validate?
- How do you prioritize and remediate security findings?
- Have you customized policies for your security requirements?
- What is your process for exception management?
- How do you track security posture improvement over time?
Key Considerations:
- Enable auto-remediation for critical misconfigurations
- Create custom policies for organization-specific requirements
- Implement risk scoring to prioritize remediation
- Generate compliance reports for auditors
Red Flags:
- CSPM alerts ignored leading to configuration drift
- No integration between CSPM and change management
- Missing coverage for some cloud services or regions
- No metrics tracking security posture improvement
Automated Security Compliance Checks
Implementation Questions:
- Have you implemented CIS benchmarks for cloud platforms?
- What tools automate compliance validation (AWS Config, Azure Policy)?
- How do you prevent non-compliant resources from deployment?
- Have you created custom compliance rules for your standards?
- What is your remediation timeline for compliance violations?
- How do you maintain compliance evidence for audits?
Key Considerations:
- Shift compliance left with pre-deployment validation
- Use policy-as-code for consistent enforcement
- Implement continuous compliance monitoring
- Create compliance dashboards for stakeholders
Red Flags:
- Manual compliance checks that miss violations
- No preventive controls allowing non-compliant deployments
- Lack of audit trail for compliance changes
- Compliance checking only during audits

Operations & Management

Required

Monitoring Strategy
Implementation Questions:
- Have you implemented distributed tracing for microservices?
- What metrics, logs, and traces form your observability pillars?
- How do you correlate events across different monitoring tools?
- Have you defined SLIs and SLOs for critical services?
- What is your log aggregation and retention strategy?
- How do you monitor third-party service dependencies?
Key Considerations:
- Use CloudWatch, Azure Monitor, or Google Cloud Operations Suite
- Implement APM tools (DataDog, New Relic, AppDynamics)
- Enable detailed monitoring for all cloud services
- Create custom metrics for business KPIs
Red Flags:
- Monitoring gaps leaving blind spots in system visibility
- No correlation between metrics, logs, and traces
- Missing alerting for critical system components
- Insufficient log retention for troubleshooting
Automation Framework
Implementation Questions:
- Have you adopted Infrastructure as Code for all deployments?
- What is your GitOps strategy for cloud operations?
- How do you manage IaC modules and versioning?
- Have you automated routine operational tasks?
- What orchestration tools manage complex workflows?
- How do you test IaC changes before production deployment?
Key Considerations:
- Use Terraform, CloudFormation, or ARM templates consistently
- Implement automated testing for infrastructure code
- Create reusable modules for common patterns
- Use state management best practices (remote backend, locking)
Red Flags:
- Manual cloud deployments leading to configuration drift
- No version control for infrastructure code
- Missing automated testing for IaC changes
- Inconsistent use of automation tools across teams
Backup Strategy
Implementation Questions:
- Have you defined RPO/RTO for each application and data type?
- What is your backup strategy (snapshots, continuous, point-in-time)?
- How do you test backup restoration regularly?
- Have you implemented cross-region backup replication?
- What is your approach to database backup and recovery?
- How do you ensure backup encryption and access control?
Key Considerations:
- Automate backup scheduling and retention policies
- Implement immutable backups for ransomware protection
- Use native cloud backup services (AWS Backup, Azure Backup)
- Document and test disaster recovery procedures
Red Flags:
- No regular testing of backup restoration
- Backups stored in same region as primary data
- Missing backup monitoring and alerting
- No encryption for backup data
Change Management
Implementation Questions:
- Have you implemented change advisory board (CAB) for cloud changes?
- What is your change approval workflow and authorization matrix?
- How do you assess risk and impact of cloud changes?
- Have you automated change deployment through CI/CD?
- What is your rollback strategy for failed changes?
- How do you track and audit all cloud configuration changes?
Key Considerations:
- Use pull requests and code reviews for IaC changes
- Implement blue-green or canary deployments
- Maintain change calendar with blackout periods
- Enable AWS CloudTrail, Azure Activity Log for audit
Red Flags:
- Direct production changes without approval process
- No audit trail of who made what changes when
- Missing rollback procedures for failed changes
- Change management bypassed for "emergency" fixes

Suggested

AIOps Integration
Implementation Questions:
- Have you implemented ML-based anomaly detection?
- What AIOps platforms reduce alert noise and false positives?
- How do you use predictive analytics for capacity planning?
- Have you automated root cause analysis for incidents?
- What is your strategy for intelligent alert correlation?
- How do you measure AIOps effectiveness and ROI?
Key Considerations:
- Start with specific use cases (anomaly detection, log analysis)
- Train models on historical data for accuracy
- Integrate AIOps with existing monitoring tools
- Maintain human oversight for critical decisions
Red Flags:
- Over-reliance on AIOps without human validation
- Insufficient training data leading to false positives
- No feedback loop to improve model accuracy
- AIOps silos not integrated with operations workflows
Service Catalog
Implementation Questions:
- Have you created standardized service offerings with approval workflows?
- What guardrails ensure compliance in self-service provisioning?
- How do you manage service catalog versioning and updates?
- Have you implemented cost estimation for catalog items?
- What is your strategy for service catalog governance?
- How do you track usage and satisfaction metrics?
Key Considerations:
- Use AWS Service Catalog or Azure Blueprints
- Include cost information and quotas in catalog items
- Implement approval workflows based on cost/risk
- Provide pre-approved, secure configurations
Red Flags:
- Complex catalog requiring extensive training to use
- No governance leading to uncontrolled provisioning
- Outdated catalog items with security vulnerabilities
- Missing integration with cost management tools

Performance & Optimization

Required

Performance Monitoring
Implementation Questions:
- Have you established performance baselines for all services?
- What synthetic monitoring simulates user transactions?
- How do you identify and resolve performance bottlenecks?
- Have you implemented real user monitoring (RUM)?
- What is your approach to database performance tuning?
- How do you optimize network latency and throughput?
Key Considerations:
- Monitor from multiple geographic locations
- Use APM tools for code-level visibility
- Implement caching strategies (CDN, Redis, ElastiCache)
- Optimize database queries and indexing
Red Flags:
- No performance baselines making degradation hard to detect
- Performance monitoring only after user complaints
- Missing correlation between performance and business metrics
- No capacity planning leading to performance issues
Capacity Planning
Implementation Questions:
- Have you analyzed historical usage patterns and growth trends?
- What models forecast future capacity requirements?
- How do you plan for seasonal or event-driven spikes?
- Have you identified capacity constraints and limits?
- What is your lead time for capacity procurement?
- How do you balance capacity planning with cost optimization?
Key Considerations:
- Use predictive auto-scaling for dynamic capacity
- Plan for both compute and data storage growth
- Consider service quotas and limits in planning
- Include buffer capacity for unexpected growth
Red Flags:
- Reactive capacity management leading to outages
- Over-provisioning due to poor forecasting
- No consideration of service limits in planning
- Missing capacity planning for data and network
Resource Optimization
Implementation Questions:
- Have you analyzed resource utilization metrics over time?
- What tools provide right-sizing recommendations?
- How do you identify and eliminate idle resources?
- Have you optimized instance families for workload types?
- What is your process for implementing optimization recommendations?
- How do you balance performance requirements with cost?
Key Considerations:
- Review utilization metrics over at least 2 weeks
- Consider burstable instances for variable workloads
- Use latest generation instances for better price-performance
- Implement automated shutdown for non-production resources
Red Flags:
- Resources consistently under 20% utilization
- Using previous generation instance types
- No regular review of optimization recommendations
- Right-sizing without considering peak requirements
Caching Strategy
Implementation Questions:
- Have you identified content suitable for caching?
- What CDN strategy improves global performance?
- How do you implement application-level caching?
- Have you configured cache invalidation strategies?
- What is your approach to database query caching?
- How do you monitor cache hit rates and effectiveness?
Key Considerations:
- Use CloudFront, Azure CDN, or Cloud CDN for static content
- Implement Redis or Memcached for session/data caching
- Configure appropriate TTL values for cache entries
- Use cache headers for browser caching optimization
Red Flags:
- No caching strategy leading to unnecessary load
- Incorrect cache invalidation serving stale content
- Caching sensitive data without proper security
- Cache misconfigurations causing performance degradation

Suggested

Performance Testing
Implementation Questions:
- Have you established performance testing scenarios and criteria?
- What tools simulate production-like load patterns?
- How do you test auto-scaling and failover scenarios?
- Have you implemented performance testing in CI/CD?
- What is your approach to stress and chaos testing?
- How do you validate performance SLAs before release?
Key Considerations:
- Use cloud-based load testing tools (K6, Locust, JMeter)
- Test from multiple geographic locations
- Include database and API performance testing
- Establish performance gates in deployment pipeline
Red Flags:
- No performance testing until production issues occur
- Testing with unrealistic data volumes or patterns
- Missing performance regression testing
- No correlation between test and production environments
Optimization Automation
Implementation Questions:
- Have you implemented auto-tuning for database parameters?
- What automation adjusts resource allocation based on demand?
- How do you automatically optimize container resource limits?
- Have you automated performance anomaly detection?
- What is your approach to automated remediation?
- How do you validate optimization effectiveness?
Key Considerations:
- Use ML-based optimization tools where available
- Implement feedback loops for continuous improvement
- Set guardrails to prevent over-optimization
- Monitor impact of optimizations on other metrics
Red Flags:
- Automated optimization without human oversight
- Optimizing for one metric while degrading others
- No rollback mechanism for failed optimizations
- Missing validation of optimization recommendations