Cloud vulnerability management: process, challenges, and how AI can help

What is cloud vulnerability management?

Cloud vulnerability management is the continuous process of identifying, classifying, prioritizing, and fixing security weaknesses across cloud infrastructure and workloads. It covers cloud workloads, virtual machines, containers, applications, operating systems, and other resources that may contain exploitable vulnerabilities.

Managing vulnerabilities in cloud environments is challenging because resources are constantly changing. Virtual machines, containers, and serverless workloads can be created, updated, or removed automatically, making it difficult to maintain an accurate asset inventory. Security teams must also correlate findings from multiple scanners, distinguish active workloads from unused resources, and determine which vulnerabilities are actually exploitable. Without enough cloud and runtime context, scanners report far more findings than can be exploited, and those false positives bury the remediation teams who have to work through them.

A security engineer with unlimited time would handle each finding by working out what has to be true for the vulnerability to be exploited, then checking the workload’s configuration, exposure, permissions, and runtime state to see whether those conditions exist. AI can now be built to think the same way, gathering that context for every finding, deciding whether it can be exploited in your environment, and showing the evidence behind each decision. It can also trace a vulnerability to its root cause, work out the fix, and find the developer who owns it, so security teams spend their time on the vulnerabilities that present real risk.

Typical stages in the cloud vulnerability management process:

1. Discover and inventory cloud assets: Continuously identify resources across cloud accounts, regions, and services to maintain an accurate inventory.
2. Collect vulnerability findings from scanners and security tools: Centralize findings across build pipelines, cloud infrastructure, and running workloads.
3. Deduplicate and normalize vulnerability findings: Correlate vulnerability and asset data to consolidate duplicate reports into actionable findings.
4. Add cloud, deployment, and runtime context: Enrich findings with exposure, permissions, ownership, data sensitivity, and component activity.
5. Determine vulnerability reachability and exploitability: Assess whether vulnerable code is reachable and whether the configuration and controls in your environment allow it to be exploited.
6. Prioritize vulnerabilities based on actual risk: Combine severity with exploitability, exposure, runtime activity, and business impact to rank remediation priorities.
7. Identify the root cause and remediation path: Trace vulnerabilities to their source and determine whether to update dependencies, images, or configurations.
8. Route fixes to the appropriate developer or team: Assign actionable remediation tasks with affected resources, risk context, and recommended fixes.
9. Validate remediation and continuously reassess risk: Rescan to confirm fixes and update priorities as deployments, configurations, and threats change.

Cloud vulnerability management best practices:

• Maintain a continuously updated cloud asset inventory: Automate discovery and capture ownership, environment, and business context for deployed resources.
• Consolidate and deduplicate findings from existing scanners: Normalize findings and group related resources to reduce duplicate alerts and remediation work.
• Distinguish running assets from dormant or removed assets: Track lifecycle and runtime status while accounting for assets and images that could return to service.
• Add code, cloud, and runtime context to every finding: Include reachability, exposure, permissions, ownership, and runtime activity to clarify risk.
• Prioritize exploitable vulnerabilities instead of severity alone: Focus on whether a vulnerability can be exploited in your environment and on its business impact, rather than on CVSS scores alone.
• Continuously reassess vulnerabilities as the cloud environment changes: Update priorities as conditions evolve and verify that fixes remain effective across deployments.

Why is cloud vulnerability management important?

Cloud vulnerability management helps organizations identify and address security weaknesses as cloud resources change. By combining continuous discovery with risk-based prioritization and remediation workflows, teams can reduce exposure and resolve high-risk vulnerabilities faster.

• Reduce the cloud attack surface: Identify vulnerable software, unused services, and exposed workloads so teams can patch, isolate, or remove affected resources. Asset and exposure context helps prioritize the weaknesses that create the greatest risk.
• Identify vulnerabilities across dynamic cloud environments: Continuous discovery and scanning help detect vulnerabilities in virtual machines, containers, and serverless workloads as resources and software change.
• Reduce the time between discovery and remediation: Prioritize findings by severity, exploitability, exposure, and business importance. Connect findings to asset owners and remediation workflows, then rescan to verify fixes.

Cloud vulnerability management vs. traditional vulnerability management

Traditional vulnerability management was designed primarily for relatively stable, on-premises environments. Assets such as physical servers, endpoints, and network devices usually have long lifecycles and predictable network locations. As a result, periodic scanning can provide a reasonably complete view of vulnerabilities.

Cloud vulnerability management relies more heavily on continuous asset discovery and frequent scanning to keep vulnerability data current, because cloud environments are more dynamic. Virtual machines, containers, and serverless resources can be created and removed automatically, sometimes within minutes.

Cloud vulnerability management also requires more context when prioritizing risk. A vulnerability’s severity alone does not show whether it can be exploited in your environment. Teams often consider whether the affected resource is internet-facing, contains sensitive data, has excessive permissions, or is connected to critical workloads.

Another difference is how remediation is performed. Traditional environments often depend on scheduled patching of persistent systems. In cloud environments, teams may instead rebuild container images, replace virtual machines, update infrastructure-as-code templates, or fix vulnerable dependencies in a deployment pipeline. This ties vulnerability management more closely to DevOps and cloud-native development workflows.

Aspect	Traditional vulnerability management	Cloud vulnerability management
Asset lifecycle	Mostly persistent assets with predictable locations	Dynamic resources that may exist for only minutes
Discovery and scanning	Periodic scans of relatively stable environments	Continuous asset discovery and frequent scanning
Risk prioritization	Often emphasizes vulnerability severity and asset criticality	Adds context such as internet exposure, sensitive data, and permissions
Remediation	Scheduled patching of persistent systems	Image rebuilds, resource replacement, and fixes in code or deployment pipelines

Related content: Read our guide to cloud application security

9 stages of the cloud vulnerability management process

1. Discover and inventory cloud assets

Cloud vulnerability management starts by identifying the resources running across cloud accounts, regions, and services. This inventory can include virtual machines, containers, container images, serverless functions, managed services, and application components.

Continuous discovery is important because cloud assets change frequently. An accurate inventory helps teams determine which resources need to be assessed and prevents short-lived or newly deployed workloads from remaining outside vulnerability management processes.

2. Collect vulnerability findings from scanners and security tools

The next step is to collect findings from vulnerability scanners and other security tools. These tools can identify vulnerable operating system packages, application dependencies, container images, libraries, and software components.

Organizations may use several scanners across different environments and stages of development. Centralizing their findings provides a broader view of vulnerabilities across build pipelines, cloud infrastructure, and running workloads.

3. Deduplicate and normalize vulnerability findings

Multiple security tools can report the same vulnerability using different formats or identifiers. Without normalization, security teams may receive several findings that represent a single underlying issue.

Cloud vulnerability management systems correlate these findings using information such as CVE identifiers, affected packages, versions, and cloud assets. Deduplication reduces alert volume and provides a more accurate view of the number of vulnerabilities requiring remediation.

4. Add cloud, deployment, and runtime context

A vulnerability finding becomes more useful when combined with information about the affected resource. Relevant context can include whether the workload is internet-facing, its cloud account, environment, network accessibility, permissions, data sensitivity, and application ownership.

Runtime information can show whether the vulnerable component is actually running or loaded. This helps distinguish vulnerabilities that represent immediate exposure from findings that have limited practical impact.

5. Determine vulnerability reachability and exploitability

Not every vulnerable package creates an exploitable path. Reachability analysis determines whether application execution can reach the vulnerable code, while exploitability analysis asks whether the vulnerability can be exploited given the configuration, enabled features, and security controls in this specific environment.

Earlier tools approximated this with signals such as public exploit code, EPSS scores, and the CISA Known Exploited Vulnerabilities catalog. Those signals say how likely a vulnerability is to be exploited somewhere, which is useful, but they can’t say whether it can be exploited in your environment. Answering that means researching what an exploit requires and checking each prerequisite against the workload, which gives a far more useful assessment than a severity score.

6. Prioritize vulnerabilities based on actual risk

Organizations may discover thousands of vulnerabilities, making it impractical to address every finding immediately. Prioritization helps teams focus remediation effort on the vulnerabilities most likely to cause meaningful damage.

Risk-based prioritization can combine CVSS severity with exploitability, reachability, internet exposure, asset importance, privileges, and runtime status. A critical CVE on an isolated, unused component may therefore receive lower priority than an exploitable vulnerability on a public production workload.

7. Identify the root cause and remediation path

The affected cloud resource is not always the correct place to apply a fix. For example, a vulnerable package running in a container may originate from its base image, application dependencies, or build configuration.

Identifying the root cause allows teams to fix the source rather than repeatedly patching individual deployments. Remediation may involve upgrading a dependency, updating a base image, applying an operating system patch, or modifying an infrastructure or build configuration.

8. Route fixes to the appropriate developer or team

Once a remediation path is known, the finding needs to reach the team that owns the affected component. Asset metadata, source repositories, deployment information, and ownership records can help identify the responsible developer or operations team.

Vulnerability management platforms can integrate with ticketing and development systems to create actionable remediation tasks. Providing the affected resource, vulnerable component, risk context, and recommended fix reduces the investigation required before work can begin.

9. Validate remediation and continuously reassess risk

Remediation is not complete until the organization verifies that the vulnerability is no longer present. Rescanning the workload, image, or application can confirm that a patch or dependency update removed the affected software version.

The process then continues as infrastructure and applications change. New deployments, software updates, configuration changes, and newly disclosed vulnerabilities can alter risk, so continuous reassessment is required to keep vulnerability data and remediation priorities current.

Related content: Read our article about CVE remediation in practice

How AI is transforming cloud vulnerability management

Automation has already taken a lot of manual work out of cloud vulnerability management. Continuous scanning keeps inventories current, deduplication collapses repeated findings, and rules that combine CVSS with exposure, asset tags, and threat intelligence give teams a better ranking than severity alone. Rules can only weigh the signals someone thought to write down, though, so they can’t tell whether a particular vulnerability can be exploited on a particular workload, and security teams still end up investigating thousands of findings by hand.

AI changes that by reasoning through each finding instead of scoring it. Think about how your best security engineer would handle a vulnerable container image if they had all the time in the world. They would check whether the affected package is actually running, look at how the workload is exposed and what it can reach, research what an exploit needs, and confirm whether those conditions exist. AI can now be built to work the same way, deciding what context each finding needs, gathering it from cloud configuration, code, deployment details, and runtime state, and returning a verdict with the evidence behind it. Teams can then prioritize on whether a vulnerability can be exploited in their environment instead of relying on severity scores such as CVSS.

AI also speeds up remediation by tracing vulnerabilities to their root causes and working out the right fix. It can analyze dependency trees, container images, and build configurations to recommend a single change that resolves many findings at once, suggest alternative mitigations when no patch is available, and route each fix to the developer who owns the affected code.

Common cloud vulnerability management challenges and how AI can help

Too many vulnerability findings

Cloud environments can generate thousands or millions of vulnerability findings across virtual machines, containers, application dependencies, and other resources. Most of them are false positives in practice, because the vulnerable package isn’t running, the feature it depends on is disabled, or a control already blocks it. Treating every finding as equally urgent creates large remediation backlogs and makes it difficult for teams to focus on the vulnerabilities that present the most risk.

Effective cloud vulnerability management reduces this noise by adding context to each finding. Severity can be combined with factors such as internet exposure, asset criticality, reachability, known exploitation, and runtime activity, so teams can prioritize the smaller set of vulnerabilities that require immediate attention.

How AI can help:

• Automated vulnerability triage: AI investigates large volumes of findings and identifies the vulnerabilities that require immediate attention.
• Context-aware risk prioritization: AI combines severity, exposure, runtime activity, and asset criticality to prioritize vulnerabilities based on actual risk.
• Noise reduction: AI closes findings that are not exploitable in your environment, with the reasoning attached, so security teams can focus on the ones that are.

Duplicate findings across security tools

Organizations often use multiple vulnerability scanners, cloud security platforms, container scanners, and application security tools. Several tools may identify the same CVE in the same workload, resulting in duplicate alerts and remediation tickets.

Deduplicating these findings can be difficult because tools may use different asset identifiers, package names, or reporting formats. Normalizing vulnerability and asset data allows teams to correlate related findings and create a single record for the underlying issue. This provides a clearer view of risk and prevents teams from fixing the same problem multiple times.

How AI can help:

• Automated finding correlation: AI analyzes vulnerability data from multiple security tools to identify findings that relate to the same underlying issue.
• Deduplication across tools: AI correlates CVEs, affected software components, and asset information to consolidate duplicate findings, even when tools name them differently.
• Root cause identification: AI traces related vulnerabilities to shared dependencies, container images, or configurations, helping teams address multiple findings with a single remediation action.

Lack of runtime context

A scanner can identify that a vulnerable package exists in a workload, but this does not necessarily show whether the package is used at runtime. Vulnerable components may be installed but inactive, or they may exist in code paths that the application never executes.

Without runtime context, teams can spend time remediating vulnerabilities that have limited exposure while more significant risks remain unresolved. Runtime data can show which workloads and components are active, how they communicate, and whether vulnerable code is loaded or executed. This context improves prioritization and helps teams focus on vulnerabilities that could affect running systems.

How AI can help:

• Automated runtime analysis: AI analyzes cloud workloads, containers, and deployment environments to distinguish actively running resources from inactive assets.
• Continuous environment monitoring: AI tracks changes in workload activity and deployment status, updating vulnerability assessments as resources are created, modified, or removed.
• Runtime-aware prioritization: AI combines runtime status with network exposure, deployment configurations, and business context to prioritize vulnerabilities affecting active production systems.

Difficulty determining exploitability

A high severity score does not automatically mean a vulnerability can be exploited in a specific cloud environment. Exploitation may require network access, particular configurations, specific application behavior, or execution of vulnerable code that never runs.

Determining exploitability requires combining vulnerability intelligence with environmental context. Teams may examine network exposure, attack paths, available exploits, runtime behavior, permissions, and reachability of vulnerable code. Without this analysis, organizations can over-prioritize severe vulnerabilities that can’t be exploited in their environment while overlooking lower-severity vulnerabilities that can.

How AI can help:

• Exploitability investigation: AI researches each CVE and evaluates whether the technical conditions required for exploitation exist in the organization’s environment.
• Context-aware attack path analysis: AI examines application code, cloud configurations, network exposure, runtime behavior, and security controls to determine whether a vulnerability can be exploited and what it would expose.
• Evidence-based exploitability verdicts: AI identifies missing exploitation prerequisites and provides technical evidence explaining why a vulnerability is or is not exploitable, reducing false positives and unnecessary remediation.

Cloud vulnerability management best practices

Maintain a continuously updated cloud asset inventory

Maintain an inventory that reflects resources currently deployed across cloud accounts, regions, clusters, and services. Include virtual machines, containers, images, serverless functions, managed services, and other assets that may contain vulnerable software.

Automate discovery instead of relying on manually maintained inventories or infrequent scans. Cloud resources can appear and disappear quickly, so vulnerability data should be associated with an inventory that changes along with the environment.

The inventory should also capture metadata such as resource owner, application, environment, deployment time, and business function. This information helps security teams connect vulnerabilities to the correct systems and determine who is responsible for remediation.

Consolidate and deduplicate findings from existing scanners

Organizations commonly use several scanners for workloads, containers, dependencies, and cloud infrastructure. Consolidate these findings into a common view so teams can evaluate vulnerabilities without switching between separate tools and data formats.

Normalize CVE identifiers, packages, software versions, and asset identities to identify duplicate findings. Deduplication reduces alert volume and helps ensure that one underlying vulnerability does not generate multiple remediation tasks for the same team.

Correlation should also account for relationships between cloud resources. For example, the same vulnerable container image may be deployed across dozens of workloads, and grouping these findings around their common source lets teams fix every affected resource with a single image update.

Distinguish running assets from dormant or removed assets

Vulnerability data can remain in security tools after the affected resource has stopped running or been deleted. If these findings are treated like vulnerabilities in active production workloads, they can distort risk metrics and waste remediation effort.

Track asset lifecycle and runtime status alongside vulnerability findings. Teams can then separate vulnerabilities affecting active workloads from findings associated with stopped, dormant, or removed resources while retaining historical data when needed.

Runtime status should not be used to discard vulnerabilities automatically. Dormant assets may return to service, and vulnerable images or templates can be used to create new workloads, so teams should check whether the underlying source remains available for future deployment.

Add code, cloud, and runtime context to every finding

A CVE and severity score provide limited information about the actual risk of a vulnerability. Enrich findings with context such as application ownership, environment, network exposure, permissions, data sensitivity, package usage, and whether the vulnerable component is active at runtime.

Code context can also indicate whether execution reaches the vulnerable function or library. Combining these signals gives teams enough information to understand where the vulnerability exists, how it could be reached, and what systems could be affected.

Cloud relationships provide another useful layer of context. Network paths, workload identities, IAM permissions, and connections to sensitive services show what a vulnerable workload can reach and what data it can touch, which helps teams evaluate potential impact rather than considering the vulnerable component in isolation.

Prioritize exploitable vulnerabilities instead of severity alone

Avoid using CVSS severity as the only basis for remediation priorities. A critical vulnerability may pose limited immediate risk if its vulnerable code cannot be reached, while a lower-severity vulnerability may require urgent remediation when it is exposed to the internet and has a reliable public exploit.

Prioritization should consider factors such as reachability, runtime activity, network exposure, known exploitation, asset criticality, and available attack paths. This approach directs remediation resources toward vulnerabilities that can actually be exploited in your environment.

Teams should also update priorities when new threat information becomes available. The publication of exploit code or evidence of active exploitation can turn a previously lower-priority finding into an urgent issue, so risk-based prioritization needs to be dynamic rather than a one-time classification.

Continuously reassess vulnerabilities as the cloud environment changes

A vulnerability’s risk can change without the vulnerability itself changing. A previously isolated workload may become internet-facing, a new exploit may become available, or an application update may begin using previously unreachable vulnerable code.

Continuously reassess findings against current cloud configuration, runtime behavior, asset state, and threat intelligence. Vulnerabilities should be reprioritized when these conditions change so remediation queues continue to reflect current risk rather than conditions observed during the original scan.

Reassessment should also verify completed remediation, because a patched workload can be replaced by another deployment built from an outdated image or dependency, causing the vulnerability to reappear. Continuous validation catches these regressions and confirms that fixes remain effective across later deployments.

Managing cloud vulnerabilities with Maze

While working with cloud security teams, Maze’s founders kept seeing tools report far more vulnerabilities than anyone could investigate, without the context needed to tell which ones could actually be exploited. Teams ended up patching by severity score and spending much of their time on vulnerabilities that were never a real risk in their environment. They built Maze so every vulnerability gets the investigation those teams never had time to run.

Maze Cloud investigates the vulnerabilities your existing scanners find in the workloads running in your cloud. It deduplicates findings across those scanners, then its AI works out what context each finding needs and gathers it from your cloud configuration, deployments, runtime state, and code, the way your best security engineer would if they had all the time in the world. On average, Maze finds that over 70% of vulnerabilities are not exploitable, and each one is closed with the full reasoning behind the decision. Maze Code brings the same investigation to your code, using AI to find the vulnerabilities in the code your team writes and the dependencies it pulls in, so a vulnerable library is assessed together with the workload it runs in.

Key features include:

• Investigate every vulnerability in your backlog: Maze investigates every finding your scanners report, across cloud and code, instead of a sample or only the criticals, so the thousands of vulnerabilities sitting in your backlog each get a verdict with evidence rather than waiting for someone to have time.
• One view of your cloud vulnerabilities: Maze pulls findings from the scanners you already run, deduplicates them, and maps your cloud and containers to separate running assets from those that aren’t, so every finding carries the context of where it lives.
• Exploitability investigation: Maze’s AI researches what each vulnerability needs in order to be exploited and checks whether those conditions exist in your environment, across configuration, controls, exposure, and runtime state, rather than relying on CVSS scores or whether a public exploit exists.
• Severity based on your environment: Maze scores each exploitable vulnerability on likelihood and impact using code and cloud context, so a critical finding in a staging service that never sees real traffic doesn’t outrank a medium one in your payment flow.
• Root cause fixes: Maze traces each vulnerability to its source, such as a base image or a dependency, and writes a fix that matches your code and environment. When no patch exists, it recommends a mitigation that makes the vulnerability not exploitable.
• Fixes where your developers work: Maze routes each fix to the owner through Jira, ServiceNow, or Azure DevOps or as a pull request, with the evidence attached. Fixes can run through CI/CD pipelines such as GitHub Actions, GitLab CI, and CircleCI, or go to the coding agent your team already uses, such as Claude Code or Cursor.
• Zero-day sweeps: When a new vulnerability is disclosed, Maze picks it up in near real time and checks every affected instance across your cloud and code, so teams know whether they’re affected before scanners have a signature for it.

See how Maze investigates and fixes cloud vulnerabilities like your best security engineer would. Learn more about Maze Cloud.