Dynamic Code Scanning Best Practices for Cloud Security

Understanding dynamic code scanning in cloud environments

Dynamic code scanning is security testing against a live application. This means you point a scanner or test harness at a running app and watch how it responds to good and bad input.

Dynamic application security testing (DAST) is the most common form. DAST treats your app like a black box: it sends requests to URLs and APIs, then looks for signs of vulnerabilities such as injection, broken access control, or misconfigurations.

In cloud environments, this matters because what runs in production often looks different from what you see in code. Configuration, identity, and network paths can all change behavior at runtime.

When you first hear "dynamic code scanning," you can think in terms of three simple questions you want to answer:

What is exposed? Which web apps, APIs, and endpoints are reachable.
How do they behave? How they handle input, errors, sessions, and permissions.
What could go wrong? Which behaviors can turn into real attack paths.

Dynamic scanning does not replace static analysis. Instead, it gives you the "real world" view that static tools cannot see.

Secure Coding Best Practices [Cheat Sheet]

Gain practical tips and coding examples to write secure code from the start and reduce vulnerabilities.

Static analysis vs dynamic analysis

Static analysis and dynamic analysis look at the same system from different angles. You almost always want both.

Static analysis is code scanning without running the app. This means tools read your source code or binaries and look for dangerous patterns before deployment.

DAST is behavior testing of the application while it runs in a controlled environment like staging or ephemeral test deployments. DAST tools interact with the app over HTTP, APIs, or event triggers and observe outputs, errors, and side effects—for example, sending a SQL injection payload to /api/users and checking whether the response leaks database errors or unauthorized data.

You can think of it like this:

Static analysis: "Is the code safe?"
Dynamic analysis: "Is the deployed system safe in practice?"

Here is a quick comparison you can keep in mind:

Aspect	Static analysis (SAST)	Dynamic analysis (DAST)
Input	Code, binaries	Live endpoints, APIs, running services
When you run it	On commits, pull requests, and builds	After deploy to test or staging; sometimes in production-safe mode
Strengths	Finds bugs early in dev, no runtime needed	Sees real configs, identities, and data flows
Blind spots	No awareness of live infra, network, or cloud permissions	Cannot see dead code or unreachable branches

The key is to connect both views. You want to be able to say, "This dynamic issue on /api/orders comes from this file, in this repo, owned by this team."

Essential dynamic scanning strategies for cloud-native applications

Cloud-native applications expose many surfaces: web UIs, internal and external APIs, mobile backends, and admin tools. You want your dynamic scanning strategy to cover these in a structured way.

Start by listing your main entry points:

User-facing web apps and portals
Public APIs and mobile backends
Partner and B2B APIs
Internal tools and admin consoles

Then, for each entry point, plan how you will scan it.

Map the surface: Use crawling, OpenAPI definitions, and API gateway metadata to discover all routes.
Test as real users: Configure the scanner with real authentication (cookies, OAuth tokens, service accounts).
Exercise real workflows: Go beyond a single request and follow multi-step flows like registration, checkout, or data export.

A simple pattern you can follow is:

Use a broad, shallow scan on all exposed endpoints to find obvious issues.
Use narrow, deep scans on high‑risk services, such as anything handling payments or personal data.

You do not need to be an expert on every vulnerability category to start. The scanner will bring those patterns; your job is to make sure it sees the right parts of your app.

Academia Wiz

Container Security Scanning: From Detection to Deployment

Container security scanning detects vulnerabilities early for an efficient DevSecOps process. Discover how it safeguards containers throughout the lifecycle.

Implementing dynamic scanning in CI/CD pipelines

CI/CD is the best place to make dynamic scanning repeatable. Treat it like any other automated test stage—consistent, fast, and policy‑driven—rather than a one‑off exercise.

A simple pipeline pattern looks like this:

On feature branches: Deploy a short‑lived test environment and run a fast, targeted DAST scan focused on changed services and APIs. Supply the scanner with OpenAPI diffs and real auth context (cookies, OAuth tokens) and enforce a tight time budget so developers get feedback within minutes.

On main branch: Deploy to staging and run a deeper scan that covers more routes and attack types, including multi‑step workflows. Test across multiple user roles, replay a regression pack of high‑risk checks, and capture artifacts (requests, responses, evidence) for auditing.

Before production: Use risk‑based gates. For example, block deploys if a critical issue appears on an internet‑facing endpoint or if a vulnerable path touches sensitive data. Optionally run a post‑deploy, production‑safe smoke scan (passive checks, strict rate limits) to confirm configs match your baseline.

Make it reliable by: using seeded, disposable test data; allowlisting scanner IPs and rate limiting; tagging each finding with commit, service, and owner; and publishing results to PR comments or build gates so fixes land quickly.

Advanced runtime security and threat detection

Dynamic scanning stops at the point where you are satisfied with your tests. The real world does not stop there.

Runtime security is watching what actually happens after you ship. This means you monitor workloads, logs, and cloud events for suspicious behavior.

Cloud-native runtime telemetry: Blend cloud logs (CloudTrail, Azure Activity Log, GCP Cloud Audit Logs), workload sensors (process monitoring, file integrity, network connections), and identity signals (IAM role assumptions, service account usage) so responders see the full blast radius when a DAST‑found weakness is probed. For example, if an attacker exploits a known API vulnerability, runtime telemetry shows not just the HTTP request but also which IAM role the workload used, which S3 buckets it accessed, and which other services it communicated with—the complete attack path in real time.

Runtime threat detection focuses on signals such as:

Unusual process activity in containers and VMs.
Strange patterns in API calls or login attempts.
Access to data that does not match normal usage.

You can think of runtime security as dynamic scanning driven by real traffic instead of synthetic tests.

To make this work in practice:

Deploy lightweight sensors to your compute platforms (VMs and container hosts), and collect serverless telemetry from cloud logs (CloudWatch, Azure Monitor), distributed tracing (X-Ray, OpenTelemetry), and service integrations (Lambda extensions, function middleware).
Stream cloud logs from services, gateways, and identity systems.
Use rules and behavior models to catch anomalies early.

Watch 5-min demo

Learn about the full power of the Wiz cloud security platform. Built to protect your cloud environment from code to runtime.

Watch now

Prioritizing vulnerabilities with cloud context

Dynamic scanners will always find more issues than you can fix at once. Cloud context is how you decide what actually matters—organizations receive an average of 4,080 cloud security alerts per month yet experience only 7 true security events per year.

Instead of starting with the scanner's severity score, start with where and how the app runs.

Graph-based toxic combinations: Combine exposure, identities, and sensitive data paths to surface only the findings that form real attack paths. For example, a medium-severity SQL injection becomes critical when the vulnerable endpoint is public, the backing workload uses an IAM role with database admin rights, and that database contains customer PII. A graph-based view shows this complete chain in one place, while isolated tools show three separate, unconnected findings.

Exposure: Is the endpoint public, internal, or restricted to a private network?
Identity: What cloud roles or service accounts does the backing workload use.
Data: Does that workload access or store sensitive information.
Blast radius: What else can you reach from there using network and identity paths.

Here is a helpful way to read a dynamic finding:

"Low risk" if the endpoint is internal only, the workload has minimal permissions, and no critical data sits behind it.
"Medium risk" if it is exposed but connects only to low-sensitivity services.
"High or critical risk" if it is public, has strong permissions, and touches sensitive data.

Scanning containers and serverless architectures

Containers and serverless change how you reach and scan your applications, but the goal stays the same: test the live behavior.

For containers and Kubernetes, you should:

Discover services through Kubernetes APIs, service meshes, and ingress controllers.
Scan from inside the cluster where possible, using Kubernetes Jobs or sidecar containers, to avoid egress restrictions, NetworkPolicies, and service mesh authorization rules that would block external scanners.
Align scans with deployment patterns like blue/green or canary, so each version gets tested.

For serverless functions, you can:

Enumerate all triggers (HTTP, queues, events, schedules).
Build test events that simulate attacker input for non-HTTP triggers.
Use logs and runtime telemetry to validate that your test inputs are executed and handled correctly.

A useful pattern is:

Container images: Scan statically for known CVEs and bad configurations.
Container workloads: Scan dynamically at the service level to see how those images behave.
Serverless functions: Combine targeted events with strong runtime logging and detection.

Production environment scanning best practices

You can run dynamic scans against production, but you need to be careful. The goal is to gain confidence, not to cause an outage.

Good practices include:

Prefer staging for heavy scans. Keep the full, aggressive test sets for environments that mirror production but are not customer-facing.
Tightly control production tests. Use allowlisted IPs, strict rate limits, and off‑peak windows if you must scan live.
Avoid destructive actions. Do not run tests that write or delete large amounts of data in production.

You can still learn a lot from production by:

Mirroring real traffic to a test environment and replaying it with extra checks.
Using passive inspection of HTTP headers, TLS configs, and error patterns.
Monitoring for unusual patterns that suggest probing or exploitation.

Think of production as a place to verify assumptions from staging, not as your primary dynamic test bed.

Academia Wiz

Agentless scanning best practices for cloud security

Agentless scanning inspects cloud environments for security risks without installing software agents on workloads, using cloud provider APIs and snapshot analysis instead

Correlating dynamic findings with infrastructure risks

A dynamic finding becomes truly useful when you can answer "what else is connected to this." That is why correlation with infrastructure is so important.

When you look at a DAST result, you want to know:

Which pod, VM, or function served this request.
What cluster, account, or project it lives in.
Which roles, policies, and security groups apply to it.
Which databases, queues, buckets, and secrets it can reach.

With that view, you can suddenly see complete stories like:

Input validation bug → container → IAM role → S3 bucket with backups.
Broken access control → Lambda → role that can manage other roles.
Misconfigured header → web app → shared Redis cache with session data.

Unified policy engine: Apply consistent, lifecycle-wide policies across code, pipeline, cloud, and runtime so the same control that blocks a risky API change in CI also flags drift in staging and alerts on exploitation attempts in production. For example, a policy like 'no public endpoints with admin IAM roles' can prevent the misconfiguration during infrastructure-as-code review, block the deployment if it slips through, and trigger an alert if the configuration changes post-deployment. This eliminates the gaps that occur when code scanning, DAST, cloud posture, and runtime tools each enforce different, uncoordinated rules.

Monitoring and incident response for runtime threats

During an incident, dynamic testing and runtime data come together. You want answers quickly, in plain language—especially critical since 89% of organizations report their current processes fail to detect active threats.

When an alert fires on a suspicious pattern, quickly ask:

Which endpoint is involved.
Is there a known vulnerability on that endpoint from previous scans.
What can an attacker reach from there.

A good incident playbook combines:

Dynamic scan history for that service.
Cloud audit logs and workload logs.
Identity and network paths from the affected resource.

Building a comprehensive dynamic security program

A strong dynamic security program is not just "we run a scanner." It is how you weave dynamic testing into your normal way of building and running software.

You can think about it in four steps:

1. Inventory: Get a complete view of your exposed web apps, APIs, and cloud front doors.
2. Automate: Plug dynamic scans into CI/CD so every change gets at least basic coverage.
3. Contextualize: Feed results into a cloud-aware platform so you see real risk, not just raw findings.
4. Close the loop with runtime: Use runtime detections to tune future scans and update policies so dynamic testing improves with each incident or anomaly. For example, if runtime monitoring detects an attacker probing /api/internal/admin—an endpoint that wasn't in your DAST scope—add it to your scan configuration and create a policy that alerts on any new internal endpoints exposed to the internet. This feedback loop ensures your dynamic testing evolves with your actual threat landscape, not just your documented API surface.

Over time, this flow becomes normal:

A developer opens a pull request.
Tests run, including fast dynamic checks.
Deeper scans run in staging on merge.
Findings show up with clear owners and fixes.
Runtime systems watch for anything that slips through.

How Wiz transforms dynamic code scanning for cloud environments

Dynamic scanners find vulnerabilities. The hard part is knowing which ones matter right now. While Wiz doesn't offer native DAST (yet!), it fully ingests and contextualizes third-party DAST findings inside the Wiz Security Graph—linking them to exposure, identities, data, application endpoints, and ownership—so you can act with confidence.

Each third‑party DAST finding is mapped to the live service that served it, the IAM roles it runs with, the data stores it can reach, and the repo and team that own the code. The Security Graph correlates findings with Application Endpoints and real infrastructure and identity paths, elevating issues that create end‑to‑end attack routes and suppressing noise and duplicates. With code‑to‑cloud mapping and clear ownership, results flow into your pipeline and developer tools, so teams can prioritize, fix, and verify changes quickly.

With Wiz, you get:

Real risk over noise: The Security Graph prioritizes findings that form actual attack paths, so you fix what reduces risk fastest.
Third‑party DAST + code‑to‑cloud mapping: Wiz ingests and enriches results from scanners like CyCognito, Invicti, and Escape, and links each finding to the service, Application Endpoint, repo, owning team, and commit.
Built into your pipeline: Surface DAST results as pull request comments, build gates, or Jira tickets so developers see and fix issues where they work—backed by unified policy enforcement across code, pipeline, cloud, and runtime.
Runtime aware: Wiz Defend correlates active probing or exploitation with past findings, turning backlog items into incidents with full context.
Complete attack surface coverage: Wiz ASM continuously discovers your external footprint and Application Endpoints so scans cover what attackers can actually reach.
More value from your existing DAST: Wiz transforms scanner findings into risk-based insights by adding cloud context and enforcing one policy across build, deploy, and runtime—unlocking higher signal and faster remediation without changing your tools.

DAST alone isn’t enough. Wiz Code provides comprehensive application security posture management during build time—scanning dependencies, container images, IaC, and pipeline settings—and gives developers real‑time, cloud‑aware feedback in the IDE and pull requests. It traces issues back to source and connects them to runtime cloud infrastructure, enabling developer‑native remediation.

Want to see how Wiz secures everything you build and run, from code to cloud? Request a demo.

Secure your cloud from code to production

Learn why CISOs at the fastest growing companies trust Wiz to accelerate secure cloud development.