Advanced SRE Interview Questions for Experienced Engineers [2025]

Advanced Reliability Engineering

1. What advanced techniques optimize SLOs in distributed systems?

Optimizing SLOs in distributed systems involves leveraging percentile-based SLIs, such as p99 latency, aggregated via Prometheus. I collaborate with stakeholders to define composite SLOs, accounting for service dependencies. This ensures end-to-end reliability while balancing performance and cost. Machine learning models predict SLO breaches, enabling proactive adjustments in enterprise microservices architectures.

• Percentile SLIs: Use p99 for precision.
• Composite SLOs: Aggregate multi-service metrics.
• ML Predictions: Forecast breaches early.
• Dependency Mapping: Account for service interactions.
• Cost Optimization: Balance reliability and budget.
• Stakeholder Input: Align with business goals.
• Automation: Adjust thresholds dynamically.

2. Why prioritize dynamic error budgets in enterprise SRE?

Dynamic error budgets adapt to workload changes, ensuring reliability without stifling innovation. They use real-time SLIs to adjust thresholds, guiding release decisions. This approach prevents over-conservative SLOs, fostering agility in enterprise systems while maintaining user trust.

It aligns development velocity with reliability goals effectively.

3. When do SREs recalibrate SLOs in complex systems?

Recalibrate SLOs during architectural shifts, like adopting serverless, or post-incident analysis revealing outdated metrics. Using Grafana to analyze historical SLIs, I set realistic targets, ensuring enterprise systems evolve with business demands while preventing overcommitment.

4. Where do SREs implement advanced observability in multi-cloud?

• Cross-Cloud Metrics: Federated Prometheus instances.
• Central Dashboards: Grafana for unified visibility.
• Distributed Tracing: Jaeger for service interactions.
• Log Aggregation: ELK Stack for real-time analysis.
• Alert Integration: Alertmanager with PagerDuty.
• Long-Term Storage: Thanos for historical data.
• Compliance Tracking: Audit-ready observability.

Implementation ensures enterprise-wide system insights.

5. Who drives advanced SRE adoption in enterprises?

SRE leads, engineering managers, and DevOps architects drive adoption, collaborating to integrate SLOs and automation. I engage stakeholders to align on reliability goals, ensuring enterprise-wide buy-in for scalable practices.

6. Which tools enhance advanced observability?

Tools like Cortex for scalable Prometheus, Grafana with Loki, and OpenTelemetry for tracing enhance observability. They provide granular insights, critical for enterprise system health and proactive issue detection.

7. How do SREs automate advanced incident response?

I automate incident response using AI-driven runbooks in GitLab CI/CD, triggered by PagerDuty. Machine learning triages alerts, auto-scales Kubernetes pods, and logs actions for postmortems. This reduces MTTR, ensuring enterprise systems recover swiftly from disruptions.

incident-response: stage: respond script: - ansible-playbook incident.yml --tags auto-triage

8. What is the role of AI in advanced SRE?

AI enhances SRE by predicting failures, optimizing alerts, and automating root cause analysis. Using Kubeflow, I analyze metrics to forecast SLO breaches, reducing false positives. This drives proactive management, ensuring enterprise reliability and efficiency in complex environments.

• Anomaly Detection: ML identifies irregularities.
• Alert Optimization: Reduces notification noise.
• Root Cause: Speeds up diagnostics.
• Scaling: Predicts resource needs.
• Automation: Enhances runbook execution.
• Learning: Improves with data.
• Compliance: Supports audit trails.

9. Why use predictive analytics in SRE?

Predictive analytics forecasts system failures using ML models on Prometheus data, enabling preemptive action. It minimizes downtime, optimizes resources, and aligns with enterprise goals for high availability and cost efficiency.

Explore analytics in AI-powered testing.

10. When to implement advanced chaos engineering?

Implement chaos engineering in production-like environments to test resilience under complex failures. Using Chaos Toolkit in GitLab CI/CD, I simulate cascading outages, ensuring enterprise systems maintain SLOs during disruptions.

11. Where do SREs store advanced monitoring data?

Store data in Thanos for long-term Prometheus retention or Cortex for scalability. Centralized storage in Grafana ensures enterprise accessibility for historical analysis and compliance reporting.

12. Who collaborates with SREs on advanced automation?

• AI Specialists: Build predictive models.
• DevOps Teams: Integrate CI/CD pipelines.
• Developers: Optimize code for reliability.
• Security Teams: Ensure compliance automation.
• Product Managers: Align automation with SLOs.
• Ops Engineers: Reduce manual toil.
• Data Scientists: Enhance analytics.

Collaboration drives enterprise automation success.

13. Which metrics are critical for advanced SRE?

Metrics like p99 latency, saturation, and error rates, tracked in Prometheus, are critical. They provide granular insights, ensuring enterprise systems meet stringent reliability requirements.

14. How do SREs implement advanced distributed tracing?

I implement tracing with OpenTelemetry, sampling high-volume traces in Jaeger. Integrated with GitLab CI/CD, it ensures enterprise visibility into microsecond-level service interactions, optimizing performance.

tracing: stage: trace script: - opentelemetry-collector --config collector.yaml

Complex Incident Orchestration

15. What is the process for orchestrating complex incidents?

Orchestrating complex incidents involves defining roles like incident commander, using PagerDuty for alerts, and automating triage with AI. I coordinate cross-team efforts, document in GitLab issues, and conduct ML-driven postmortems to prevent recurrence, ensuring enterprise recovery.

16. Why use AI-driven incident triage?

AI-driven triage prioritizes alerts based on impact, reducing MTTD. Using ML models with Prometheus data, I identify critical issues, ensuring enterprise systems minimize downtime and maintain user trust.

• Prioritization: Focuses on high-impact issues.
• Speed: Reduces detection time.
• Accuracy: Minimizes false positives.
• Automation: Triggers runbooks.
• Scalability: Handles large incidents.
• Learning: Improves with incidents.
• Documentation: Enhances postmortems.

17. When do SREs escalate complex incidents?

Escalate when cascading failures impact multiple services or MTTR exceeds thresholds. Using PagerDuty for tiered alerts, I ensure enterprise coordination with stakeholders for rapid resolution.

18. Where do SREs document incident orchestration?

Document orchestration in GitLab wikis, detailing AI-assisted runbooks and escalation paths. Centralized storage ensures enterprise accessibility during high-stakes incidents.

19. Who leads complex incident response?

Incident commanders, supported by SREs and DevOps leads, drive response. I assign roles, use data-driven insights from Prometheus, and coordinate with stakeholders to ensure enterprise recovery.

20. Which tools support advanced incident orchestration?

• PagerDuty: Dynamic alert routing.
• Slack: Real-time team communication.
• GitLab: Runbook versioning.
• Prometheus: Real-time metrics.
• Alertmanager: Predictive alerts.
• Opsgenie: Escalation workflows.
• Kubeflow: AI-driven triage.

Tools streamline enterprise incident management.

21. How do you automate complex incident workflows?

I automate workflows with AI-driven runbooks in GitLab CI/CD, integrating Prometheus for real-time alerts. Auto-scaling Kubernetes and logging actions reduce MTTR, ensuring enterprise efficiency.

incident-workflow: stage: respond script: - ansible-playbook workflow.yml --tags auto-scale

22. What is the impact of poor incident orchestration?

Poor orchestration increases MTTR, risking SLO breaches and user dissatisfaction. It disrupts enterprise operations, highlighting the need for AI-driven automation and clear processes.

23. Why conduct blameless postmortems for complex incidents?

Blameless postmortems foster open discussion, using ML to analyze patterns. They identify systemic issues, preventing recurrence and enhancing enterprise reliability through collaborative learning.

Explore postmortems in incident runbooks.

24. When to declare a major complex incident?

Declare major incidents when multiple SLOs breach or cascading failures affect critical services. This activates enterprise-wide response teams for rapid resolution.

25. Where do SREs coordinate incident response?

Coordinate in Slack for real-time updates and PagerDuty for escalations. Centralized war rooms ensure enterprise-wide alignment during complex incidents.

26. Who benefits from effective incident orchestration?

End-users, teams, and stakeholders benefit. Effective orchestration minimizes downtime, ensures enterprise reliability, and maintains trust in high-stakes environments.

27. Which strategies improve incident response times?

• AI Triage: Prioritize critical alerts.
• Automation: Runbooks for quick fixes.
• Clear Roles: Defined incident commanders.
• Real-Time Metrics: Prometheus monitoring.
• Escalation Paths: PagerDuty integration.
• Training: Simulate complex scenarios.
• Documentation: Detailed postmortems.

Strategies drive enterprise response efficiency.

28. How do you measure incident orchestration success?

Measure success with MTTR and MTTD, tracked via PagerDuty with ML predictions. Analyze postmortems for systemic improvements, ensuring enterprise SLO compliance.

Observability Architectures

29. What is the role of advanced observability architectures?

Advanced observability architectures provide end-to-end visibility using metrics, logs, and traces. Integrating Prometheus, Jaeger, and ELK, I ensure enterprise systems detect issues proactively, optimizing performance and reliability.

30. Why use federated Prometheus in observability?

Federated Prometheus aggregates metrics across clusters, ensuring scalability. It supports enterprise observability by providing unified insights for multi-cloud and hybrid environments.

• Scalability: Handles large clusters.
• Unified Metrics: Cross-cloud visibility.
• Performance: Optimized data collection.
• Retention: Long-term storage with Thanos.
• Alerts: Integrated with Alertmanager.
• Compliance: Audit-ready data.
• Cost Efficiency: Reduces duplication.

31. When to implement AI-driven observability?

Implement AI-driven observability during high-traffic periods or system expansions. Using Kubeflow with Prometheus, I predict failures, ensuring enterprise proactive management.

32. Where do SREs centralize observability data?

Centralize data in Grafana, integrating Prometheus for metrics, ELK for logs, and Jaeger for traces. This ensures enterprise accessibility for real-time analysis and compliance.

33. Who designs observability architectures?

SREs, architects, and data engineers design architectures, aligning on SLOs. I incorporate stakeholder input to ensure enterprise systems meet performance and regulatory needs.

34. Which components are critical for observability?

Metrics (Prometheus), logs (ELK), and traces (Jaeger) are critical. They provide comprehensive insights, ensuring enterprise systems maintain high reliability and performance.

35. How do you optimize observability for latency?

Optimize by sampling traces in OpenTelemetry and aggregating metrics in Cortex. I configure Grafana for low-latency dashboards, ensuring enterprise real-time visibility.

observability: stage: monitor script: - opentelemetry-collector --config low-latency.yaml

36. What is the impact of poor observability?

Poor observability delays issue detection, increasing MTTD and downtime. It risks enterprise SLO breaches, necessitating robust architectures for proactive management.

37. Why prioritize tracing in observability?

Tracing pinpoints latency in microservices, critical for enterprise performance. Using Jaeger, I identify bottlenecks, ensuring rapid troubleshooting and reliability.

Explore tracing in observability vs. traditional.

38. When to review observability architectures?

• Post-Incident: Integrate lessons learned.
• System Upgrades: Align with new services.
• Quarterly: Update for business growth.
• Tool Updates: Ensure compatibility.
• Compliance: Meet regulatory standards.
• Performance: Optimize latency.
• Feedback: Incorporate team insights.

Reviews ensure enterprise observability relevance.

39. Where do SREs visualize observability data?

Visualize data in Grafana dashboards, integrating Prometheus and Jaeger. This provides enterprise teams with actionable insights for performance optimization.

40. Who benefits from advanced observability?

Developers, SREs, and stakeholders benefit. Advanced observability ensures enterprise systems remain reliable, supporting rapid issue resolution and user satisfaction.

41. Which tools enhance observability scalability?

Tools like Thanos, Cortex, and VictoriaMetrics enhance scalability. They manage large-scale metrics, ensuring enterprise observability supports growing systems.

42. How do you implement AI in observability?

Implement AI with Kubeflow, analyzing Prometheus metrics for anomaly detection. Integrate with Alertmanager for predictive alerts, ensuring enterprise proactive management.

ai-observability: stage: monitor script: - kubeflow run anomaly-detection.py

Automation Frameworks

43. What is the role of advanced automation frameworks?

Advanced automation frameworks reduce toil, enabling SREs to focus on strategic tasks. Using Terraform and GitLab CI/CD, I automate provisioning and incident response, ensuring enterprise scalability and reliability.

44. Why automate complex SRE workflows?

Automating complex workflows minimizes errors and MTTR, critical for enterprise systems. It frees SREs for innovation, aligning with business goals for high availability.

• Error Reduction: Eliminates manual mistakes.
• Speed: Accelerates response times.
• Scalability: Handles growing workloads.
• Innovation: Frees time for strategic tasks.
• Compliance: Automates audit trails.
• Reliability: Ensures consistent execution.
• Cost Efficiency: Optimizes resources.

45. When to prioritize automation in SRE?

Prioritize automation when toil exceeds 30% of tasks or during system scaling. I identify repetitive processes, automating with Ansible to ensure enterprise efficiency.

46. Where do SREs deploy automation frameworks?

Deploy frameworks in GitLab CI/CD for pipelines and Kubernetes for orchestration. This ensures enterprise systems automate provisioning, scaling, and incident response effectively.

47. Who develops automation frameworks?

SREs, DevOps engineers, and developers collaborate to develop frameworks. I integrate stakeholder requirements, ensuring enterprise automation aligns with reliability goals.

48. Which automation tools are critical for SRE?

• Terraform: Infrastructure as code.
• Ansible: Configuration management.
• GitLab CI/CD: Pipeline automation.
• Kubernetes: Workload orchestration.
• Prometheus: Automated monitoring.
• Chaos Toolkit: Resilience testing.
• PagerDuty: Automated escalations.

Tools drive enterprise automation efficiency.

49. How do you measure automation effectiveness?

Measure effectiveness with toil reduction percentage and MTTR, tracked in PagerDuty. I analyze automation ROI, ensuring enterprise systems achieve reliability and cost goals.

50. What is the impact of automation on SRE?

Automation reduces toil, enabling focus on innovation. It ensures enterprise systems scale reliably, minimizing errors and aligning with business objectives for high availability.

51. Why use CI/CD in SRE automation?

CI/CD automates deployments, ensuring consistent releases. Using GitLab, I integrate reliability checks, reducing risks in enterprise systems and supporting rapid iteration.

Explore CI/CD in immutable infrastructure.

52. When to scale automation frameworks?

Scale frameworks during traffic spikes or system expansions, using Kubernetes for orchestration. This ensures enterprise systems handle increased loads without compromising reliability.

53. Where do SREs test automation frameworks?

Test frameworks in staging environments with Chaos Toolkit, simulating production loads. This ensures enterprise automation reliability before deployment.

54. Who benefits from SRE automation?

Teams, stakeholders, and end-users benefit. Automation reduces errors, speeds responses, and ensures enterprise systems maintain high reliability and user satisfaction.

55. Which metrics validate automation success?

Metrics like deployment frequency, MTTR, and toil reduction validate success. Tracked in Prometheus, they ensure enterprise automation meets reliability goals.

56. How do you integrate AI in automation?

Integrate AI with Kubeflow for predictive scaling and anomaly detection in GitLab CI/CD. This ensures enterprise systems automate proactively, reducing manual intervention.

ai-automation: stage: automate script: - kubeflow run predictive-scaling.py

Cloud Resilience Strategies

57. What is the role of SRE in cloud resilience?

SREs ensure cloud resilience by defining SLOs and automating failover with Terraform. Monitoring with Prometheus, I maintain enterprise system uptime during outages.

58. Why use multi-region deployments?

Multi-region deployments enhance resilience, mitigating single-region failures. Using Kubernetes, I ensure enterprise systems maintain availability and low latency across geographies.

• Failover: Seamless region switching.
• Latency: Optimized user access.
• Redundancy: Mitigates outages.
• Compliance: Meets data residency.
• Scalability: Handles traffic spikes.
• Monitoring: Prometheus integration.
• Cost Efficiency: Balanced resource use.

59. When to implement multi-cloud resilience?

Implement multi-cloud resilience to avoid vendor lock-in or during high-availability requirements. Using Terraform, I ensure enterprise systems remain robust against provider outages.

Explore multi-cloud in multi-cloud DevOps.

60. Where do SREs deploy resilience strategies?

Deploy strategies in Kubernetes clusters across regions, using Terraform for IaC. This ensures enterprise systems maintain uptime and performance during disruptions.

61. Who collaborates on cloud resilience?

SREs, cloud architects, and security teams collaborate. I align on SLOs and failover plans, ensuring enterprise systems meet reliability and compliance needs.

62. Which tools enhance cloud resilience?

• Terraform: Automated provisioning.
• Kubernetes: Multi-region orchestration.
• Prometheus: Real-time monitoring.
• Chaos Toolkit: Resilience testing.
• PagerDuty: Failover alerts.
• AWS Route 53: DNS failover.
• Azure Traffic Manager: Load balancing.

Tools ensure enterprise system robustness.

63. How do you test cloud resilience?

Test resilience with Chaos Toolkit in GitLab CI/CD, simulating regional failures. Monitor with Prometheus, ensuring enterprise systems recover without SLO breaches.

chaos-test: stage: test script: - chaos run region-failure.yaml

64. What is the impact of poor cloud resilience?

Poor resilience increases downtime, risking SLO breaches and user trust. It disrupts enterprise operations, necessitating robust failover and monitoring strategies.

65. Why prioritize failover automation?

Failover automation ensures rapid recovery, minimizing downtime. Using Terraform, I automate region switches, ensuring enterprise systems maintain high availability.

66. When to review resilience strategies?

Review strategies post-incident or quarterly, updating for new services. This ensures enterprise systems adapt to evolving reliability requirements.

67. Where do SREs monitor resilience metrics?

Monitor metrics in Grafana, integrating Prometheus for uptime and latency. This provides enterprise teams with real-time resilience insights.

68. Who benefits from cloud resilience?

End-users, teams, and stakeholders benefit. Resilience ensures enterprise systems deliver consistent performance, maintaining trust and operational continuity.

69. Which metrics validate resilience?

Metrics like uptime, failover time, and recovery speed validate resilience. Tracked in Prometheus, they ensure enterprise systems meet SLOs.

70. How do you automate failover processes?

Automate failover with Terraform scripts in GitLab CI/CD, triggered by Prometheus alerts. This ensures enterprise systems switch regions seamlessly, minimizing downtime.

failover: stage: recover script: - terraform apply -var="region=us-west-2"

Troubleshooting Strategies

71. What is the approach to advanced troubleshooting?

Advanced troubleshooting involves isolating issues with Jaeger traces, analyzing metrics in Prometheus, and automating diagnostics with AI. I document findings in GitLab, ensuring enterprise systems resolve issues efficiently.

72. Why use AI in troubleshooting?

AI accelerates troubleshooting by predicting root causes from Prometheus data. It reduces MTTD, ensuring enterprise systems address complex issues swiftly and accurately.

• Prediction: Identifies failure patterns.
• Speed: Reduces diagnostic time.
• Accuracy: Minimizes false leads.
• Automation: Triggers diagnostic scripts.
• Scalability: Handles large systems.
• Documentation: Enhances postmortem data.
• Learning: Improves with incidents.

73. When to escalate troubleshooting efforts?

Escalate when diagnostics exceed MTTD thresholds or involve cross-service issues. Using PagerDuty, I ensure enterprise coordination for rapid resolution.

74. Where do SREs log troubleshooting data?

Log data in ELK Stack for real-time analysis and GitLab issues for postmortems. This ensures enterprise accessibility for future reference and compliance.

75. Who collaborates on troubleshooting?

SREs, developers, and ops teams collaborate. I use data-driven insights from Jaeger to align efforts, ensuring enterprise systems resolve issues effectively.

76. Which tools support advanced troubleshooting?

• Jaeger: Distributed tracing.
• Prometheus: Real-time metrics.
• ELK Stack: Log analysis.
• Grafana: Visualization dashboards.
• Kubeflow: AI diagnostics.
• PagerDuty: Escalation management.
• GitLab: Issue tracking.

Tools enhance enterprise troubleshooting efficiency.

77. How do you automate troubleshooting?

Automate with AI scripts in GitLab CI/CD, analyzing traces and metrics. Integrate with Alertmanager for alerts, ensuring enterprise systems resolve issues proactively.

troubleshoot: stage: diagnose script: - kubeflow run diagnose.py

78. What is the impact of poor troubleshooting?

Poor troubleshooting prolongs downtime, risking SLO breaches and user dissatisfaction. It disrupts enterprise operations, necessitating robust diagnostic strategies.

79. Why prioritize tracing in troubleshooting?

Tracing identifies latency sources in microservices, critical for enterprise performance. Using OpenTelemetry, I pinpoint issues, ensuring rapid resolution and reliability.

80. When to review troubleshooting processes?

Review processes post-incident or quarterly, updating runbooks for new patterns. This ensures enterprise troubleshooting remains effective and aligned with system growth.

81. Where do SREs visualize troubleshooting data?

Visualize data in Grafana, integrating Jaeger and Prometheus. This provides enterprise teams with actionable insights for rapid issue resolution.

82. Who benefits from effective troubleshooting?

End-users, teams, and stakeholders benefit. Effective troubleshooting minimizes downtime, ensuring enterprise systems maintain reliability and user trust.

83. Which metrics validate troubleshooting success?

Metrics like MTTD, MTTR, and resolution accuracy validate success. Tracked in Prometheus, they ensure enterprise troubleshooting meets reliability goals.

84. How do you integrate AI in troubleshooting?

Integrate AI with Kubeflow, analyzing Prometheus metrics for root cause prediction. This ensures enterprise systems resolve issues swiftly, reducing manual effort.

Advanced On-Call Management

85. What is the role of advanced on-call management?

Advanced on-call management ensures 24/7 coverage with minimal fatigue. Using AI-driven rotations in PagerDuty, I automate alerts, ensuring enterprise systems maintain high availability.

86. Why use AI for on-call scheduling?

AI optimizes scheduling by predicting workload, reducing fatigue. Integrated with PagerDuty, it ensures enterprise teams respond efficiently to incidents.

• Prediction: Balances workloads.
• Fatigue Reduction: Optimizes rotations.
• Efficiency: Speeds alert response.
• Automation: Integrates with tools.
• Scalability: Supports large teams.
• Fairness: Distributes on-call duties.
• Compliance: Tracks schedules.

87. When to adjust on-call rotations?

Adjust rotations during team growth or high-incident periods. Using AI insights, I ensure enterprise coverage aligns with system demands and team well-being.

88. Where do SREs manage on-call schedules?

Manage schedules in PagerDuty, integrating with Slack for notifications. This ensures enterprise teams access real-time on-call data for incident response.

89. Who coordinates on-call management?

SRE leads and team managers coordinate, using AI for scheduling. I align rotations with SLOs, ensuring enterprise systems maintain continuous coverage.

90. Which tools support advanced on-call?

• PagerDuty: Dynamic scheduling.
• Opsgenie: Escalation workflows.
• Slack: Real-time notifications.
• VictorOps: Alert routing.
• Prometheus: Incident metrics.
• Kubeflow: AI scheduling.
• GitLab: Runbook access.

Tools ensure enterprise on-call efficiency.

91. How do you reduce on-call fatigue?

Reduce fatigue with AI-driven rotations in PagerDuty, limiting alerts via automation. I implement time-off policies, ensuring enterprise team morale and reliability.

Explore fatigue in self-service platforms.

92. What is the impact of poor on-call management?

Poor management increases fatigue, slowing responses and risking SLO breaches. It disrupts enterprise operations, necessitating robust scheduling and automation.

93. Why automate on-call alerts?

Automating alerts with Alertmanager reduces manual intervention, ensuring rapid response. It aligns with enterprise goals for high availability and team efficiency.

94. When to escalate on-call incidents?

Escalate when incidents exceed MTTR or involve critical services. Using PagerDuty, I ensure enterprise coordination for swift resolution.

95. Where do SREs document on-call processes?

Document processes in GitLab wikis, detailing escalation paths and runbooks. This ensures enterprise accessibility during high-pressure incidents.

96. Who benefits from effective on-call management?

Teams, stakeholders, and end-users benefit. Effective management ensures enterprise systems maintain uptime, supporting rapid incident resolution and user trust.

97. Which metrics validate on-call success?

Metrics like response time, alert volume, and fatigue rates validate success. Tracked in PagerDuty, they ensure enterprise on-call aligns with reliability goals.

98. How do you implement AI in on-call?

Implement AI with Kubeflow for predictive scheduling in PagerDuty. This optimizes rotations, ensuring enterprise teams respond efficiently without burnout.

oncall-ai: stage: schedule script: - kubeflow run schedule-optimizer.py

Compliance and Security Integration

99. What is the role of SRE in compliance?

SREs ensure compliance by integrating security metrics into SLOs, using SAST in GitLab CI/CD. I automate audit trails, ensuring enterprise systems meet regulatory standards.

100. Why integrate DevSecOps with SRE?

Integrating DevSecOps ensures security is embedded in reliability practices. Using SAST and DAST, I align SLOs with compliance, ensuring enterprise systems are secure and reliable.

• Security Metrics: Include in SLOs.
• Automation: Security scans in pipelines.
• Compliance: Meets regulatory needs.
• Reliability: Balances security and uptime.
• Collaboration: Aligns with DevOps.
• Visibility: Tracks security incidents.
• Efficiency: Reduces manual checks.

101. When to implement compliance automation?

Implement automation during regulatory audits or system expansions. Using Terraform, I ensure enterprise systems maintain compliance with minimal manual effort.

Explore compliance in SBOM compliance.

102. Where do SREs track compliance metrics?

Track metrics in Prometheus, integrated with Grafana for visualization. This ensures enterprise systems provide audit-ready compliance data.

103. How do you ensure security in SRE workflows?

Ensure security by integrating SAST in GitLab CI/CD, monitoring vulnerabilities with Prometheus. I align SLOs with security goals, ensuring enterprise systems remain compliant and reliable.

security-scan: stage: security include: - template: Security/SAST.gitlab-ci.yml