This is the fifth blog in our Azure Monitoring series, and we’re focusing on what’s most critical: keeping your environment secure and always available. Performance and cost mean nothing if your services go offline or your data is compromised. In this post, we’ll highlight the Azure metrics that help CloudOps teams detect threats early, build resilience into their stack, and stay ahead of outages before they impact users or compliance. Missed our earlier posts?Catch up.
The quick download
Security risks don’t just threaten data—they’re often the root cause of downtime.
Security lapses often cause availability issues. Tracking metrics like failed logins, privilege escalation, and lateral movement prevents incidents that disrupt uptime.
Monitor unresolved Defender recommendations, compliance drift, and low MFA adoption before they turn into breaches.
Use real user availability, error rates, and resource health patterns to detect degradations before users report them.
Try LogicMonitor to correlate Azure security and uptime signals in a unified monitoring platform.
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Uptime and security don’t exist in silos. In Azure environments, a single misconfiguration or login anomaly can just as easily trigger a breach as it can an outage. That’s why CloudOps teams rely on both Azure security metrics and Azure uptime metrics to maintain service integrity and resilience.
By combining these insights under a unified Azure monitoring metrics strategy, IT teams gain the context they need to detect risks before they escalate into real problems.
Why Monitoring Azure Uptime and Security is Inseparable?
For CloudOps teams, uptime and security aren’t isolated objectives; they’re deeply connected.
A spike in failed logins or a misconfigured permission can take down a critical service or expose sensitive data. That’s why it’s no longer enough to monitor availability alone without visibility into the threats that can impact it.
Monitoring Azure security metrics alongside Microsoft Azure uptime metrics helps organizations detect risks that affect both availability and integrity. Whether it’s identifying lateral movement, tracking degraded resources, or flagging access anomalies, security-focused telemetry plays a key role in keeping services both running and resilient.
So, if your goal is to deliver uninterrupted service, security must be part of your uptime strategy, not a separate initiative.
Understand the Shared Responsibility for Azure
In Azure, uptime and security are tightly linked and understanding the shared responsibility model is essential to monitoring both effectively.
While Microsoft manages core infrastructure, it’s up to you to protect the Azure resources you control, including data, identities, accounts, and access.
That’s why monitoring isn’t only about keeping systems online, it’s about capturing early indicators of misconfigurations, intrusions, or policy drift that could compromise both service continuity and security.
By knowing which components of the stack you’re accountable for, you can better prioritize the right Azure monitoring metrics. Whether you’re tracking app availability or scanning for lateral movement, good visibility helps you act early, troubleshoot faster, and maintain resilience.
Security Metrics: Spot Risks Before They Become Incidents
Security incidents rarely happen without warning; they build up through overlooked misconfigurations or weak access controls. That’s why tracking Azure security metrics across identity, network, and workload layers is critical for early Azure threat detection.
With the right Azure alert configuration, CloudOps teams can catch risks before they escalate into breaches.
Why Monitoring Azure Security Metrics is Essential
In Azure, security threats build up through patterns that are easy to miss without the right telemetry. Metrics give CloudOps teams a way to see those early signs and act before small issues become major incidents.
When you’re responsible for access, identities, and data protection, monitoring isn’t optional; it’s how you stay ahead of risk.
Spot unusual sign-in patterns before accounts are compromised
Detect configuration changes that weaken your security posture
Speed up Azure incident response by knowing where to look first
Use Azure security metrics to enforce least privilege and access hygiene
Without this visibility, threats often go unnoticed until users or auditors find them first.
Network Metrics for Detecting Lateral Movement
Network-based attacks in Azure often start quietly, after initial access has already been gained. Watching how traffic flows inside your environment helps spot suspicious movement early and protect uptime before services are disrupted.
Monitor the following metrics:
Blocked Inbound Connections: Shows how often Azure denies traffic at the network edge. These metrics come from Network Security Groups (NSGs) and Azure Firewall logs and are critical for understanding who tries to access your environment and from where. Tracking spikes or repeated blocks helps teams strengthen Azure network security and identify exposed services before attackers move inside.
Unexpected Protocol Use: Appears when traffic relies on ports or protocols that aren’t part of normal operations, such as RDP or SSH activity in environments where they aren’t expected. Monitoring this behavior helps detect misconfigurations or unauthorized access paths.
East‑West Traffic Anomalies: Occurs when internal systems start communicating in unusual ways, such as a VM suddenly scanning multiple services. These patterns signal lateral movement after credentials are compromised. Monitoring this allows CloudOps teams to detect threats early and limit their impact on availability.
Authentication Metrics for Detecting Unauthorized Access Attempts
Authentication-related events are often the earliest warning signs of compromised accounts. By tracking key login behaviors, you can enable proactive Azure monitoring and prevent threats before they escalate.
Here’s what you should monitor:
Failed Login Attempts by Time and Source: Indicates brute-force attacks or credential stuffing. Monitoring these patterns as part of your Azure security metrics helps you catch intrusions before access is granted.
Multi-Factor Authentication (MFA) Usage Trends: Reveals weak spots in account protection. Low MFA adoption among privileged users should be a red flag in your Azure monitoring metrics.
Account Lockouts: Points to mistyped credentials or repeated attack attempts. Reviewing lockout trends helps teams separate user error from targeted risk.
Privileged Access Activity: Signals internal misuse or compromised credentials. These metrics offer a focused lens into your environment’s most sensitive points of control.
Access Behavior Metrics for Identifying misuse of accounts
Once an account is authenticated, risky behavior often shows up in how access is used. These Azure security metrics, collected through Azure data collection telemetry tools, help detect Azure anomalies early and respond quickly during an Azure incident response:
Access Velocity: Measures how quickly a user or service account moves across multiple Azure resources. A sudden increase can indicate credential misuse or automated activity that needs immediate review.
First-Time Access Events: Reveal misconfigured permissions or accounts being used outside their normal scope.
Permission Utilization: Shows whether users are exercising rights they rarely or never use. Monitoring this helps identify over-privileged accounts that increase security risk even when no attack is underway.
Privileged Role Assignments: Changes to admin or high-privilege roles are some of the most sensitive actions in Azure. Tracking when and how these roles are assigned helps prevent silent privilege escalation.
Just-In-Time (JIT) Access Requests: Elevate permissions for specific tasks temporarily. Monitoring how often JIT is used and by whom keeps elevated access controlled and limited to only the necessary duration.
Compliance Metrics to Spot Weaknesses Before They’re Exploited
In Azure, compliance metrics serve as early indicators of risk exposure tied to misconfigurations or poor governance. These Azure security metrics are essential for maintaining continuous Azure infrastructure protection and improving your overall Azure monitoring metrics strategy:
Unresolved Recommendations: Microsoft Defender for Cloud flags security recommendations based on your current configuration. Monitoring the count of unresolved items identifies critical gaps in your security posture that attackers could exploit.
Policy Non-Compliance Rate: Shows how many Azure resources are out of alignment with defined policies. A high rate often points to uncontrolled growth or inconsistent deployments across teams.
Time to Remediate Vulnerabilities (MTTR): Measures how quickly you can respond to known issues. In regulated environments, faster remediation directly reduces compliance risk.
Compliance Drift: Catches subtle shifts before they escalate into audit failures.
Policy Enforcement Rate: Reflects how effectively Azure Policy is applied and adhered to across your subscriptions. A low enforcement rate suggests a lack of governance that can weaken your security baseline.
Application Metrics
Application‑level activity often reveals security problems before infrastructure alerts fire. These Azure security metrics, gathered through Azure Application Insights and Azure Log Analytics connect suspicious behavior to real application impact and strengthen their overall Azure monitoring metrics strategy:
Unauthorized API Calls: Shows when requests are rejected due to missing or invalid credentials. Tracking these events in Azure Application Insights helps identify exposed endpoints or abused tokens before they affect users or data.
Storage Account Access Failures: Indicates misconfigured permissions or attempted data access by unauthorized identities. Analyzing these events in Azure Log Analytics gives you visibility into potential data exposure risks.
SQL Authentication Failures: Highlights failed attempts to connect to databases using incorrect credentials. Monitoring these failures detects brute‑force attempts and protects sensitive data tied to core application performance workloads.
Availability metrics help CloudOps teams measure how reliably and consistently a service delivers value to users.
In complex environments like Azure, where services depend on multiple layers, tracking availability helps identify weak points and maintain end-to-end reliability.
Why Monitoring Azure Availability Metrics is Essential
Even when your infrastructure is technically “up,” users may still face errors, delays, or incomplete functionality. That’s why it’s essential to go beyond basic checks and monitor service behavior from the user’s perspective.
These metrics provide insights into degraded performance or localized failures, helping organizations respond quickly and maintain SLAs:
Identify outages masked by healthy infrastructure components
Catch slowdowns or API issues affecting end users
Understand regional differences in availability across Azure zones
Detect partial failures before they escalate
The following Azure availability metrics are central to any effort to optimize Azure performance and deliver a dependable experience:
Uptime: Measure More Than ‘Is It Running?’
To track Azure uptime effectively, you need visibility into actual service responsiveness, error behavior, and whether users can complete key workflows. These metrics form the foundation of actionable Azure monitoring metrics:
User-Perceived Availability: Evaluates whether users can access and interact with services as expected. In Azure, tools like Application Insights and Web Tests help simulate real interactions to verify full functionality.
Regional Performance Variations: Helps catch region-specific issues caused by routing, load balancing, or zone-level disruptions.
Functional Validation: Validate core business logic of your app works like being able to log in or complete a checkout. Azure Web Tests can mimic these user flows and validate functionality, not only reachability.
High Error Rates: Signal degraded services even if the infrastructure looks healthy. Monitoring these in real time gives a clear view of underlying problems impacting users.
Resource Health: Catch Failures Before They Happen
Resource health metrics provide early warning signs that something is going wrong, even before an outage occurs. Instead of waiting for users to report issues, IT teams can track how Azure resources behave during stress, instability, or automatic recovery. These metrics are foundational to Azure performance monitoring and help reduce downtime through faster diagnosis and response:
Degraded Performance States: Indicates when a resource is operational but not performing optimally, like a VM running but with disk I/O issues. It helps to address slowness or instability before they trigger alerts or user complaints.
Status Transition Frequency: Point to unstable infrastructure or misconfigurations. Monitoring these transitions helps surface recurring problems and improves service planning.
Self-Healing Patterns: Tracks when Azure automatically resolves a resource issue without manual intervention. Recognizing these patterns supports smarter capacity planning and strengthens resilience strategies using Azure monitoring metrics.
Resource Degradation Metrics
Some services don’t fail all at once; they degrade over time, showing signs like slowness, restarts, or intermittent errors.
The following resource degradation metrics help spot this decline early and maintain reliability before users are affected:
Service Degradation Frequency: Tracks how often a service enters a degraded state, such as slow responses or partial failures. A rising trend here may indicate underlying issues in performance or resource scaling.
Resource Health Transitions: Measures how frequently a resource switches between healthy, warning, and error states. Frequent transitions are a strong signal of instability and can inform proactive remediation.
Service Dependencies: Know What Breaks When Something Fails
Azure environments are built on interconnected services, where a single failure can ripple across multiple systems. Tracking dependency-related Azure monitoring metrics helps you understand impact quickly and restore services faster.
Service-to-Service Connectivity: Shows whether dependent services can communicate as expected. Breaks in connectivity may explain downstream failures even when individual services appear healthy.
Cross-Service Failure Correlation: Reveals which issues are causes versus side effects. This helps prioritize fixes and reduce mean time to recovery.
Dependency Risk Mapping: Highlights fragile links in your architecture. Knowing which services depend on each other helps anticipate blast radius and plan safer changes.
Disaster Recovery Readiness: Make Sure Failover Works
Disaster recovery metrics validate whether your recovery plans protect uptime when it matters most.
These Azure monitoring metrics move DR from documentation to operational readiness:
Recovery Time Actual vs. Recovery Time Objective (RTO): Compares how long recovery really takes versus what was planned. Large gaps indicate processes or automation that need improvement.
Failover Success Rate: Tracks how often failover completes without errors or manual fixes. A low success rate indicates hidden risks in your recovery design.
Automation Coverage: Measures how much of your recovery process runs automatically. Higher automation reduces human error and speeds up restoration during high-pressure incidents.
Build Security and Uptime into CloudOps
Security and uptime aren’t two separate goals in Azure; they’re interdependent.
A missed anomaly in Azure Monitor logs or an unpatched resource can quietly degrade system health, eventually taking services offline. To build resilient CloudOps workflows, teams must treat every security gap as a potential uptime risk.
Real-time monitoring makes that possible.
By tracking key Azure security metrics alongside Azure monitoring metrics, CloudOps teams can detect warning signs early, whether it’s a spike in failed logins, a policy update, or a slow-degrading resource.
The ability to track Azure uptime in context helps prioritize what needs fixing before users are impacted.
This unified view also accelerates incident response. Instead of chasing disconnected alerts, teams can correlate security indicators with performance metrics, understand dependencies, and respond fast when something is not working properly.
Security and Uptime Monitoring Best Practices for Azure Environments
Strong CloudOps teams don’t treat security and uptime as separate disciplines. In Azure, the same misconfiguration that weakens security can also take a service offline. Following best Azure monitoring practices means setting up monitoring in a way that protects access, data, and service continuity at the same time:
Security Best Practices
Here are some best security practices:
Lock down access to monitoring data: Use role-based access control in Log Analytics workspaces so users only see the data they need, reducing the risk of internal exposure or misuse.
Secure log ingestion and log data storage: Ensure activity logs are encrypted in transit using TLS 1.2+ and protected at rest, especially when sending security and audit data into Azure Monitor and Application Insights.
Audit and protect monitoring activity: Enable log query auditing and workspace locks so changes, queries, or deletions to security data are tracked and cannot be silently altered.
Uptime Best Practices
Here are some best uptime practices:
Monitor availability from the service layer: Combine Azure Monitor metrics with functional checks so availability reflects real user access, not only resource status.
Design alerts around impact, not noise: Configure alerts for degraded states and service health changes instead of only hard failures to catch issues before downtime occurs.
Validate recovery paths regularly: Track recovery time and failover success to ensure backup and disaster recovery plans actually work under real conditions.
How LogicMonitor Helps Monitor Azure Metrics
LogicMonitor brings Azure metrics, logs, and service context together in one place, so CloudOps teams can see how security events and performance issues impact uptime in real time.
Instead of switching between tools, you can correlate identity activity, network behavior, and service health to understand what’s happening and why faster.
With automated discovery, dynamic thresholds, and service-level views, LogicMonitor helps teams spot risk early and respond before users are affected. If you’re ready to simplify Azure monitoring and protect uptime more effectively, request a demo or sign up to see LogicMonitor in action.
How can I tell the difference between a real threat and a false positive in login anomalies?
Look for multiple signals like time of day, location, and MFA bypass attempts to determine if an event is unusual enough to investigate. This is key to real-time threat detection in Azure.
What’s the best way to track access velocity?
Set thresholds for unusual patterns, like a service account accessing more than X new resources in Y minutes, and only trigger alerts beyond those. It’s one of the smarter Azure monitoring best practices.
How do I measure “user-perceived availability” if I don’t have full end-to-end tests?
Use multi-step WebChecks to simulate key actions (like logins or API calls), and monitor response times and errors rather than uptime. This gives you a clearer view of Azure service availability monitoring.
What does it mean if a service is “degraded” but still technically online?
It means the service is available but underperforming, slow, timing out, or intermittently failing. Users might still be impacted even if no hard outage is detected, which is why tracking cloud infrastructure security metrics is as important as availability.
How often should I test disaster recovery failover to make sure it works?
At least quarterly or after any major infrastructure or app change. Failover tests should simulate real conditions, not simply walk through documentation. This is central to strong disaster recovery readiness metrics.
What should I do if I notice unusual protocol usage inside my network?
Immediately investigate which systems initiated the traffic, whether it’s expected behavior, and whether any ports or protocols need to be restricted.
By Nishant Kabra
Senior Product Manager for Hybrid Cloud Observability
Results-driven, detail-oriented technology professional with over 20 years of delivering customer-oriented solutions with experience in product management, IT Consulting, software development, field enablement, strategic planning, and solution architecture.
Disclaimer: The views expressed on this blog are those of the author and do not necessarily reflect the views of LogicMonitor or its affiliates.
