The Simple Guide to Performing a Cloud Application Security Test

Cloud security used to live in a silo - something handled by a specialist team after the fact. Today, it’s a shared responsibility. If you write, deploy, or maintain code, you’re part of the security chain. Every new service, container, or function expands the attack surface, and even the smallest misconfiguration can expose data or create an entry point for attackers.

65% of organizations experienced a cloud-related breach in the past year, and very few detected it within the first hour. For some organizations, it took more than a full day to respond.

The challenge isn’t awareness of security testing’s value; it’s the fragmentation. 

Tools for each cloud security segment mean noise, duplicate finds, and unchecked blind spots. A proper cloud application security test unifies code, infrastructure, and runtime results so teams can understand which issues matter most and act on them quickly.

What is a Cloud Application Security Test?

A cloud application security test is a process used to evaluate how well-protected an application is once it’s running in the cloud. It combines code review, infrastructure assessment, and configuration analysis to build a complete picture of your infrastructure and the cloud security controls protecting it.

Cloud environments evolve constantly. A modern cloud security test combines static and dynamic code analysis, infrastructure review, and live validation to capture the system's behavior under real operating conditions. It should run continuously, following the code flow through CI/CD pipelines, short-lived workloads, and infrastructure-as-code changes, where configuration drift often appears between build and runtime.



Components of a Cloud Application Architecture

A cloud application consists of many moving parts. Each one requires its own form of testing.

Identity and Access Management (IAM)

IAM governs who can access what within your environment. Weak or overly broad permissions remain among the most common causes of cloud breaches. Every identity, whether a user, service, or machine identity, should operate with the least privilege necessary to perform its function.

Application and API Security

Applications and APIs are exposed entry points that must handle input safely, enforce strict authentication, and avoid leaking sensitive data. Testing at this layer (through SAST and DAST tools) focuses on broken authorization, injection flaws, and insecure data handling issues.

Data Security and Encryption

Cloud applications process large volumes of sensitive information. Keys, secrets, and credentials must be securely managed to prevent accidental exposure from misconfigured storage. IaC scans and policy-as-code checks help verify that databases, buckets, and encryption keys follow encryption and access standards.

Infrastructure and Network Security

Every virtual network, container, and server should be scanned for open ports, unprotected consoles, and misconfigured firewall rules. The objective is to maintain isolation between systems and limit visibility only to what’s necessary. Continuous penetration testing helps validate these defenses under real-world attack conditions.

CI/CD Pipeline Security

The build and deployment pipeline itself can be a target. Testing should confirm that branch protections, access controls, and multi-factor authentication are enforced. Secret detection and configuration scans help protect credentials, tokens, and sensitive configuration values at every build stage.

Workload and Container Security

Workload and container security focus on protecting the environments where cloud applications actually run. It covers the build images, orchestration systems, and runtime configurations defining workloads' operations. Testing in this layer uncovers unverified images, vulnerable dependencies, or containers running with unnecessary privileges. 



A Step-by-Step Guide to Performing a Cloud Application Security Test

Step 1: Define Security Objectives, Compliance, and Threat Model

Start by listing the components you must protect: the applications, APIs, and cloud environments. From there, define the compliance standards that govern them, whether SOC 2, ISO 27001, or industry-specific frameworks like HIPAA or PCI DSS. 

With clear scope and requirements, you can develop a threat model that reflects how your system could be attacked. Focus on likely risks: data exposure, privilege escalation, and supply chain compromise are common starting points.

Include internal and third-party components in that model, mapping each to its potential blast radius so you understand how a single compromise could spread. Platforms like Jit make this process repeatable by codifying objectives as version-controlled security plans as code. Each plan reflects the organization’s goals and enforces consistent testing across environments, turning security into a living framework that evolves alongside the application.



Step 2: Map and Classify Your Cloud Environment and Assets

Inventory every asset in your cloud setup: apps, storage, functions, databases, and services. Use cloud APIs or CSPM tools for automated discovery, and keep that inventory under version control alongside your IaC to match what’s deployed.

Next, trace how data moves through the system and label each component by exposure level:

• Public: anything reachable from the internet, such as web apps, APIs, or S3 buckets.

• Internal: resources limited to private networks or VPN access — staging databases, internal dashboards, or APIs.

• Production: live workloads handling user data, payments, or customer transactions.

Cloud-native tools like AWS Config and Microsoft Defender for Cloud are helpful for surface-level visibility, but they only cover part of the picture. A centralized view through a unified security platform or internal dashboard helps security teams track assets across multiple cloud environments.

Step 3a: Assess Code Security

Now you can turn to the code itself. Run static analysis tools like Semgrep or Bandit to catch insecure patterns early in development. Additionally, SCA tools such as OSV-Scanner or Nancy can flag vulnerable dependencies before they make it into a build. Secrets detection adds another layer of assurance, surfacing any credentials or tokens that may have slipped into commits during development.

Embed these checks directly into your development workflow and run them automatically in pre-commit hooks or CI pipelines. That way, they become part of the standard review process. Lastly, fine-tune the rules to surface only issues that matter (the ones attackers can exploit) so your developers spend time fixing real problems.



Step 3b: Assess Infrastructure and Configuration Security

Once the code is covered, review the infrastructure it runs on. Use IaC scanners to catch insecure settings in Terraform, CloudFormation, or Kubernetes manifests before deployment. Tools like KICS, Kubescape, or Prowler can flag problems such as wide-open security groups, unencrypted storage, or missing network controls.

For containerized environments, scan base images and layers with a tool like Trivy to spot vulnerable packages or outdated libraries. Pay close attention to privilege settings — containers that run as root or with unnecessary permissions are an easy path for attackers if exploited.

Step 4: Test Access Controls and Permissions

Access control testing prevents privilege misuse. Review IAM and RBAC settings across AWS, Azure, and GCP. Then, you can use privileged access management solutions to discover roles with privileges or inherited permissions that go beyond what is needed. Check that inactive accounts are disabled and that high-privilege users and service accounts undergo multi-factor authentication.

Test user access and automation roles, service accounts, and identity federation trust relationships - attackers usually exploit these through privilege chaining. Enforce time-bound, just-in-time access for sensitive roles wherever feasible.



Step 5: Simulate Attacks and Validate Runtime Security

Once code and configurations are in place, the next step is to see how the system performs under real conditions. Runtime testing looks at three prongs of exposure: how the application behaves, how APIs handle requests, and how the environment holds up. 

• Application testing (DAST): Run dynamic scanners to detect injection flaws, authentication gaps, and logic errors.

• API testing: Review endpoints for excessive data exposure, missing authorization, or weak rate limiting.

• Runtime posture validation: Check for configuration drift, exposed admin panels, or workloads running with elevated privileges.

Step 6: Review, Prioritize, and Remediate Findings

When testing is complete, you’ll have findings that range from minor misconfigurations to critical exposures. Sorting through them requires context.

Aggregate results from all tools in one place to rank vulnerabilities by exploitability, severity, and business impact. AI in software testing adds value by automatically correlating overlapping results, highlighting high-risk patterns, and filtering noise. Hence, teams focus on the issues that actually matter. Push critical findings directly to your issue tracker or repository for fast remediation, and retest once fixes are applied to confirm the problem is closed.



Step 7: Continuous Monitoring and Iteration

Continuous testing ensures that security stays aligned with the current state of your environment. Modern approaches increasingly use AI in software testing to recognize patterns across scan results, detect anomalies, and reduce noise from false positives. 

That begins with automation. Automate scans so changes in dependencies or configuration are captured in real time, feeding directly into that process. From there, use telemetry to get context: authentication failures, privilege escalation attempts, or unusual traffic patterns can all indicate early signs of compromise. 

Jit’s Product Security Platform automates this process. It runs incremental scans on each code change, checking only what’s new. Security plans are enforced directly in the CI/CD pipeline, validating code, infrastructure, and runtime against policy before deployment. Results feed into a single report that tracks posture across every environment. 

One Workflow for Every Layer of Cloud Security

Cloud application security testing works best when it’s continuous, and not some occasional exercise. Every change to code or configuration should trigger the same checks automatically, and every result should feed back into development without slowing it down.

Jit makes that process practical for any team. It integrates scanning, prioritization, and remediation into a single automated system. The platform covers every layer of cloud security, and all of the tools mentioned in this article can be orchestrated through it. Each step happens automatically when code is written, reviewed, or deployed. The result is faster feedback, better coverage, and fewer surprises in production. Learn more here.