What is cloud vulnerability scanning
Cloud vulnerability scanning is the automated process of identifying security flaws within your cloud infrastructure, workloads, and configurations. Unlike traditional scanning designed for static, on-premises servers, cloud scanning is built to handle the dynamic nature of the cloud. It continuously inspects resources that spin up and down, such as containers and serverless functions, to ensure nothing is missed.
This process involves four core components: asset discovery, vulnerability detection, risk assessment, and remediation guidance. A cloud security scanner first builds an inventory of everything you have running. It then checks those assets against databases of known issues to find weaknesses.
Because cloud environments are constantly changing, scanning must be continuous rather than periodic. You need a vulnerability scanning platform that covers the full stack, including:
IaaS: Virtual machines and storage.
PaaS: Managed databases and application platforms.
SaaS: Third-party applications (assessed via SSPM for configuration posture rather than traditional workload vulnerability scanning). For example, SSPM tools check whether your Salesforce or Microsoft 365 configurations follow security best practices, while workload scanners analyze your compute resources for software vulnerabilities.
Containers: Kubernetes clusters and registries.
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Why cloud vulnerability scanning matters in modern environments
As organizations move to the cloud, their attack surface expands rapidly. Every new virtual machine or bucket creates a potential entry point for attackers if it contains misconfigurations or unpatched vulnerabilities, particularly concerning given the growing volume of sensitive data in cloud environments. Organizations store customer records, financial data, intellectual property, and regulated information across cloud workloads, making vulnerability management critical for data protection. Cloud vulnerability assessment helps you close these gaps before they become breaches.
Compliance frameworks also mandate this level of vigilance. Standards like PCI DSS, HIPAA, SOC 2, and ISO 27001 require vulnerability management and regular assessment. For example, PCI DSS mandates quarterly external and internal scans plus scans after significant changes, while HIPAA, SOC 2, and ISO 27001 require risk-based vulnerability management processes rather than a specific scanning cadence. This aligns with the shared responsibility model, where the cloud provider secures the hardware, but you must secure your workloads and configurations.
Investing in proactive scanning is far less expensive than dealing with a breach. Automated scanning keeps pace with the speed of cloud development cycles, catching issues as developers push code. This is essential for managing shadow IT, where untracked cloud resources often go unnoticed by security teams.
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How cloud vulnerability scanning works
The process begins with asset discovery across all your cloud providers and services. The scanner identifies every workload, from virtual machines and containers to serverless functions and databases. This creates a complete asset inventory so you know exactly what needs protection.
Next, the scanner analyzes these assets for vulnerabilities. It checks installed software packages and configurations against CVE databases like the National Vulnerability Database (NVD) and curated threat intelligence feeds such as CISA's Known Exploited Vulnerabilities catalog. This step identifies known security flaws that attackers could exploit.
Advanced scanners then validate exploitability through runtime signals. They observe which processes actually execute, which libraries load into memory, and which network connections form. For example, if a container image contains a vulnerable version of Log4j but the application never loads that library at runtime, the scanner can deprioritize it. Runtime validation using technologies like eBPF (extended Berkeley Packet Filter) reduces false positives by focusing on vulnerabilities in actively executing code paths rather than every package present on disk.
Modern scanners then gather context to understand the real risk. A graph-based approach links vulnerabilities to network exposure, identities, and data paths to reveal actual attack paths rather than isolated findings. For example, the scanner maps relationships between a vulnerable web server (node), its network route to the internet (edge), its IAM role permissions (edge), and the customer database it can access (node). This graph structure makes it intuitive to query for toxic combinations like 'internet-exposed servers with admin privileges to sensitive data.'
Finally, the system prioritizes risks and provides remediation guidance. It tells you what to fix first based on the potential impact. The process operates on a continuous monitoring loop, rescanning automatically as your environment changes.
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Types of cloud vulnerability scanning approaches
Different scanning methods offer different levels of visibility and operational overhead. Understanding these types helps you choose the right vulnerability scanning service for your needs.
Agentless scanning
Agentless scanning uses cloud provider APIs to analyze resource metadata and, where enabled, disk or image snapshots of workloads. For example, it reads AWS EC2 instance metadata, pulls container image layers from Amazon ECR, and analyzes Azure VM disk snapshots to inventory installed packages. This method provides complete coverage because it can see every asset in your account without requiring installation. It has zero performance impact on your applications and requires no maintenance.
This approach is ideal for scanning offline workloads and ephemeral resources that exist only briefly. However, snapshot- and metadata-based methods can miss in-memory conditions or runtime-only signals that surface during active execution. For example, a vulnerable library present in an image might never load at runtime, or a process might execute with reduced privileges that limit exploitability.
Agent-based scanning
Agent-based scanning involves installing lightweight software directly on your workloads. This provides deep visibility into runtime behavior and process-level activity. It allows for real-time detection of active threats and in-memory exploits.
However, agents introduce deployment complexity and performance overhead. You must manage updates and ensure compatibility across all operating systems. Coverage gaps often occur because agents cannot be installed on every resource type, such as managed services.
Hybrid approaches
Hybrid approaches combine the breadth of agentless scanning with the depth of agents. You use agentless scanning to get 100% visibility across your entire estate. Then, you deploy agents only on critical workloads where runtime threat detection and behavioral monitoring are necessary.
Key capabilities for effective cloud vulnerability scanning
To effectively secure a modern cloud estate, your vulnerability scanning platform needs specific capabilities. These features ensure you can manage risk at scale without slowing down development.
Complete multi-cloud coverage
You need a scanner that works seamlessly across AWS, Azure, GCP, and other providers. It must scan VMs, containers, Kubernetes, serverless functions, and managed services in a single view. This ensures you can maintain consistent security policies regardless of where your infrastructure lives.
Context-aware risk prioritization
Context transforms a long list of vulnerabilities into actionable intelligence. Effective tools analyze factors that elevate risk:
Internet exposure: Is the asset reachable from the web?
Data sensitivity: Does the asset have access to sensitive data?
Privileges: Does the asset have high-level permissions?
This analysis reveals "toxic combinations" where multiple minor issues create a critical attack path. Context-aware prioritization based on these factors reduces noise and focuses limited security resources on exploitable risks. By filtering out vulnerabilities in offline systems, internal-only services, or packages not loaded at runtime, teams can concentrate on the small percentage of findings that represent actual attack paths. The security graph is a key tool here, visualizing the relationships between vulnerabilities and assets.
Integration with development pipelines
Security should start before deployment. You can scan container images and check Infrastructure as Code (IaC) templates for misconfigurations in tools like Terraform. This "shift-left" security prevents vulnerabilities from ever reaching production. Use a single policy framework across CI/CD and cloud to prevent risky builds and enforce consistent controls post-deploy. For example, a policy that blocks container images with critical CVEs in your build pipeline should also flag those same images if they somehow reach production. This unified approach eliminates gaps where different tools apply different rules at different stages.
Code-to-cloud traceability
Trace issues from production workloads back to the originating image, repository, and owning team to accelerate remediation. When a vulnerability is detected in a running container, the platform should automatically identify:
The container image and tag deployed
The registry and repository where the image was built
The Git commit and branch that triggered the build
The development team and service owner responsible
This traceability enables targeted notifications and allows teams to fix vulnerabilities at the source (updating base images or dependencies) rather than just patching individual running instances.
Automated remediation workflows
Speed is critical when fixing vulnerabilities. Automated workflows route tickets to the responsible teams using their preferred tools. The system should provide remediation guidance with specific fix instructions, such as which patch to apply. Integration with ITSM platforms helps track progress and manage Service Level Agreements (SLAs).
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Implementation challenges and best practices
Deploying a cloud vulnerability management program comes with hurdles. Addressing these challenges early ensures a smoother implementation and better security outcomes.
Common implementation challenges
Scanning across multiple cloud accounts and subscriptions can be complex to configure. As your environment scales, maintaining 100% scanning coverage becomes difficult without automation. False positives are another common issue and frequently cited by security teams as a moderate to significant challenge. Scanners often flag vulnerabilities in packages that aren't actually exploitable in your specific configuration, creating alert fatigue and slowing down remediation of real risks.
Security teams also struggle to correlate vulnerabilities across siloed tools. Small teams managing large cloud estates often face resource constraints that make manual triage impossible. Finally, getting developer buy-in for remediation can be tough if the process disrupts their workflow.
Phased deployment roadmap
Deploy your cloud vulnerability management program in stages:
Days 1-30: Foundation
Connect agentless scanning to production cloud accounts (AWS, Azure, GCP)
Map asset ownership using tags (team, service, environment)
Establish baseline policies using CIS Benchmarks or NIST guidelines
Integrate with ticketing systems (Jira, ServiceNow) for remediation workflows
Days 31-60: Refinement
Tune policies based on initial false positive rates
Define exception workflows for accepted risks
Set remediation SLAs by severity (critical: 7 days, high: 30 days)
Expand coverage to development and staging environments
Days 61-90: Maturity
Implement shift-left scanning in CI/CD pipelines
Deploy runtime sensors on critical workloads for validation
Establish metrics dashboards tracking MTTR and coverage
Conduct quarterly policy reviews and tune prioritization rules
Key performance indicators for vulnerability management
Track these metrics to measure program effectiveness:
| Metric | Definition | Target |
|---|---|---|
| MTTD (Mean Time to Detect) | Average time from CVE disclosure to detection in your environment | < 24 hours |
| MTTR (Mean Time to Remediate) | Average time from detection to fix for exploitable vulnerabilities | Critical: < 7 days, High: < 30 days |
| Asset Coverage | Percentage of cloud resources actively scanned |
|
| Exploitable Vulnerability Rate | Percentage of detected CVEs that are internet-exposed, have loaded packages, or sit on critical attack paths | Track trend downward |
| False Positive Rate | Percentage of flagged vulnerabilities that teams mark as not exploitable in context | < 20% |
| Patch SLA Adherence | Percentage of vulnerabilities remediated within defined SLAs |
|
Monitor these metrics weekly and review trends monthly to identify process improvements.
How Wiz transforms cloud vulnerability management with unified context
Wiz Unified Vulnerability Management (UVM) modernizes vulnerability management through a horizontal security modelthat maintains shared context and consistent policy across every stage:
Code: Scan source code and dependencies before commit
Pipeline: Block risky container images and IaC templates in CI/CD
Cloud: Continuously assess deployed workloads via agentless scanning
Runtime: Validate which vulnerabilities are actually exploitable in production
This unified approach eliminates blind spots between disconnected tools. The Wiz Security Graph correlates vulnerabilities with network exposure, misconfigurations, and identities to surface toxic combinations, revealing the full attack path so you can focus on what matters. The Wiz sensor validates which packages and code paths actually load at runtime—if a vulnerable library exists in an image but never executes and the workload isn't internet-exposed, it's deprioritized behind exposed services with active vulnerable code. Wiz UVM automatically traces vulnerabilities from production back to specific repositories and developers, speeding up remediation.
Security, Dev, and Ops share one prioritized risk queue with clear ownership and AI-assisted remediation. Developers receive contextualized notifications in their existing tools (Slack, Jira, GitHub) with specific fix guidance, making remediation faster and more efficient.
Ready to move beyond vulnerability lists and focus on exploitable risks? Get a demo to see how Wiz UVM turns vulnerability noise into actionable security intelligence.
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