SAST vs. DAST: 6 Key Differences and How to Choose

Introducing SAST and DAST 

SAST (Static Application Security Testing) and DAST (Dynamic Application Security Testing) are complementary AppSec methods. SAST is “white-box” testing that analyzes source code early in development for coding flaws. DAST is “black-box” testing that attacks a running application from the outside to find exploitable runtime vulnerabilities.

SAST involves analyzing source code, bytecode, or binary code for security vulnerabilities without executing the program. SAST tools scan the codebase to identify potential issues such as SQL injection, cross-site scripting, buffer overflows, and insecure coding practices. Unlike SAST, DAST does not require access to the source code; instead, it interacts with the application from the outside, simulating attacks that a malicious actor might attempt. 

Here is how they break down side-by-side:

Feature	SAST (Static)	DAST (Dynamic)
Approach	Analyzes internal source code, byte code, or binaries without running the app.	Probes the running application from the outside, like an attacker.
When to Use	Early in the SDLC (often integrated into IDEs, Git, or CI/CD pipelines).	Later in the SDLC (typically in staging or testing environments before production).
What it Finds	Code quality issues, logic flaws, hardcoded secrets, and cryptographic failures.	Runtime misconfigurations, API errors, authentication issues, and security header misconfigurations.
Advantages	Fast, pinpoints exact vulnerable lines of code, and prevents bugs from making it far.	Language-agnostic and yields highly realistic, externally exploitable vulnerability data.
Limitations	Often language-specific and can generate more false positives due to lack of runtime context.	Cannot pinpoint exact source code locations and requires a fully functional app to run.

In this article:

• How SAST Works
• How DAST Works
• SAST vs. DAST: The Key Differences
• SAST Pros and Cons
• DAST Pros and Cons
• DAST vs. SAST: How to Choose

How SAST Works

SAST tools integrate with development environments or CI/CD pipelines to scan code as it is written or committed. These tools parse the code, build a model of the application’s logic, and use rule sets to detect patterns associated with known vulnerabilities. The analysis is static, meaning it does not require the application to be running, which allows issues to be found before the software is compiled or executed.

After scanning, SAST tools generate reports highlighting vulnerabilities, their locations, and sometimes suggestions for remediation. Developers can review these findings directly in their IDEs or through dashboards, enabling quick fixes. The effectiveness of SAST depends on its rule sets and the ability of developers to interpret and act on the results.

How DAST Works 

DAST tools send requests to a running application and analyze the responses to uncover vulnerabilities. These tools crawl the application’s interfaces, such as web forms, APIs, and URLs, looking for exploitable weaknesses. By observing how the application responds to crafted input, DAST can detect vulnerabilities like improper input validation, exposed sensitive data, or session management flaws.

The testing process is typically automated and can be integrated into QA or staging environments. After scanning, DAST tools provide reports listing detected vulnerabilities, affected endpoints, and sometimes recommendations for remediation. Because DAST tests the application in a production-like state, it identifies issues that static analysis may miss but cannot pinpoint the exact location in the source code where the problem exists.

SAST vs. DAST: The Key Differences 

1. Testing Approach

SAST uses a white-box testing approach, analyzing the application’s internal source code, configuration files, or binaries. It examines the logic and structure of the code without executing it, identifying vulnerabilities introduced by insecure coding practices. This method provides insight into how the application is built and can catch subtle flaws that are not apparent during runtime.

DAST uses a black-box approach, interacting with the application as an external user would, without knowledge of the underlying code. It sends requests and analyzes responses to discover vulnerabilities that appear during execution. DAST focuses on how the application behaves in a live environment, uncovering issues related to runtime configuration, authentication, and data exposure.

2. Stage in the SDLC

SAST is most effective when used early in the software development life cycle (SDLC), particularly during the coding and build phases. By integrating SAST into the development workflow, teams can identify and remediate vulnerabilities before they progress to later stages, reducing the cost and complexity of fixes. Early detection helps maintain secure coding standards and reduces the risk of introducing security flaws into production.

DAST is typically performed in the later stages of the SDLC, such as during testing, staging, or pre-production. Because DAST requires a running application, it is best suited for environments where the software is already deployed or near completion. This timing allows DAST to catch issues that may have been missed by earlier static analysis or that only become apparent when the application is operational.

3. Visibility into the Application

SAST provides visibility into the application’s internal logic, structure, and data flows. By analyzing the source code, SAST tools can trace how data moves through the application and uncover vulnerabilities such as insecure data handling or logic errors. This insight allows for targeted remediation and helps developers understand the root causes of security flaws.

DAST has limited visibility, as it only interacts with the application’s external interfaces. It cannot access or analyze the internal workings of the application, relying solely on observed behavior. While DAST detects issues that arise during execution, it may miss vulnerabilities deep within the code that do not appear in the application’s responses.

4. Types of Findings

SAST detects code-level vulnerabilities such as buffer overflows, SQL injection points, hardcoded secrets, and improper error handling. These findings often point directly to the affected lines of code and provide context for remediation. SAST’s focus on the source makes it suited for enforcing secure coding standards and catching issues before deployment.

DAST identifies vulnerabilities exposed during runtime, such as authentication flaws, insecure session management, and cross-site scripting. Its findings are based on observed application behavior and may include exploited endpoints, misconfigured security settings, or data leaks. While DAST can find issues missed by static analysis, it cannot always provide the exact location in the code where the vulnerability originates.

5. Accuracy and False Positives

SAST tools can generate false positives, flagging code as vulnerable when it may not be exploitable in practice. This is due to the static nature of the analysis, which cannot always account for runtime context or environmental factors. Developers may need to spend time triaging and validating reported issues to determine their true risk.

DAST tends to produce fewer false positives, as it identifies vulnerabilities based on application behavior. If a DAST tool can exploit a vulnerability, it is likely to be a genuine risk. However, DAST can miss issues that require knowledge of the internal code or specific execution paths not exercised during testing, leading to false negatives.

6. Remediation Context

SAST provides remediation guidance, often pinpointing the exact lines of code and offering suggestions for fixes. This detail enables efficient resolution of issues. The context provided by SAST reports supports code refactoring and secure coding practices.

DAST reports typically highlight the vulnerable endpoints or features but lack detail about the underlying code responsible for the issue. Remediation may require additional investigation to trace the vulnerability back to its source within the application. This can slow the fix process, especially in complex or poorly documented codebases.

SAST Pros and Cons 

SAST identifies security vulnerabilities before an application is deployed. By analyzing source code, it gives developers insight into potential weaknesses early in the software development life cycle (SDLC). While this makes it useful for secure development, it cannot detect every type of security issue on its own.

Pros

• Detects vulnerabilities early in development: Identifies security issues while developers are writing or reviewing code.
• Pinpoints the exact location of vulnerabilities: Reports include the affected file, function, and line of code.
• Integrates into development workflows: Integrates with IDEs, version control systems, and CI/CD pipelines for automated scans on commits or pull requests.
• Provides code coverage: Analyzes the entire codebase, including code paths that may be difficult to trigger during runtime testing.
• Supports secure coding practices: Encourages adherence to secure coding standards and helps identify recurring coding mistakes.
• Helps meet compliance requirements: Provides evidence that code has been reviewed for security issues.
  

Cons

• Cannot detect runtime-specific vulnerabilities: Cannot identify vulnerabilities caused by runtime configuration, server settings, authentication flows, or interactions between multiple systems.
• May generate false positives: May flag code that appears vulnerable but is not exploitable in practice.
• Requires access to source code: Cannot be performed without access to source code, bytecode, or binaries.
• Limited visibility into deployment environments: Does not assess infrastructure misconfigurations, web server settings, cloud services, or runtime dependencies.
• Language and framework support can vary: Effectiveness depends on support for the programming language, framework, and libraries used.
• Can slow development if not properly configured: Large codebases may require longer scan times, and excessive findings can overwhelm developers.

DAST Pros and Cons 

DAST evaluates the security of applications while they are running, identifying vulnerabilities exposed in real-world environments. It complements static analysis by validating how the application behaves from an external attacker’s perspective. However, because it has no visibility into the application’s source code, it has limitations.

Pros

• Identifies vulnerabilities in running applications: Detects security issues that appear during execution, including authentication flaws, session management problems, insecure configurations, and runtime input validation errors.
• Does not require source code access: Assesses proprietary, legacy, or third-party applications through their external interfaces.
• Produces fewer false positives: Findings are based on observable application behavior or successful exploitation.
• Tests applications from an attacker’s perspective: Simulates techniques used by external attackers to show exposed vulnerabilities.
• Works across different programming languages: Independent of the programming language or framework used to build the software.
• Validates deployed application security: Verifies security after deployment, accounting for infrastructure, web server configuration, APIs, authentication mechanisms, and other runtime components.
  

Cons

• Requires a running application: Requires a staging, QA, or production-like environment.
• Cannot identify the vulnerable source code: Reports the affected URL, API endpoint, or functionality but not the specific file or line of code.
• May miss untested functionality: Analyzes only application areas it can discover or access.
• Finds issues later in the SDLC: Vulnerabilities are discovered after the application is operational.
• Scanning can take significant time: Comprehensive scans may require extensive crawling and testing of application endpoints.
• Cannot detect all code-level vulnerabilities: Vulnerabilities not exposed through application behavior may remain undetected.

DAST vs. SAST: How to Choose 

Choosing between DAST and SAST depends on where the application is in the SDLC, what level of access the team has, and which risks need to be tested. Most teams use both methods because they find different types of vulnerabilities.

Key considerations

• Use SAST early in development: Helps catch insecure coding patterns before the application is built or deployed.
• Use DAST for running applications: Validates application behavior in test, staging, or production-like environments.
• Consider source code access: SAST requires access to source code, bytecode, or binaries. Without access, DAST is usually more practical.
• Match the tool to the type of risk: SAST targets code-level flaws such as insecure functions, hardcoded secrets, and unsafe data handling. DAST targets runtime issues such as authentication weaknesses, session problems, exposed endpoints, and configuration errors.
• Evaluate remediation needs: SAST points to affected code. DAST identifies vulnerable endpoints and may require additional investigation.
• Account for false positives and false negatives: SAST may produce more false positives. DAST may miss vulnerabilities in code paths not reached during testing.
• Use both for broader coverage: Combining SAST during development and DAST before release provides coverage across code-level and runtime risks.
• Plan for what happens after the scan: Neither method tells you whether a finding is exploitable in your specific environment. SAST flags code that may never be reachable, and DAST only exercises what it can discover. Factor in how findings get validated before they reach a developer.

Moving Beyond SAST and DAST with Maze Code

AST and DAST each surface important vulnerabilities, but both approaches tend to flood teams with findings and little way to tell which ones actually matter. Teams often reach for DAST to answer the exploitability question, since a successful attack is proof. Maze answers the same question by investigation, checking whether the conditions a vulnerability requires exist in your environment, without needing a running target.

Maze Code takes a different approach. It uses AI agents that understand your code, gather deep context, and investigate every finding the way an expert security engineer would. It traces whether a vulnerability is genuinely reached, survives the build, and is exploitable in your environment. It also does a full code review, reading each line of your application code one by one, surfacing novel vulnerabilities traditional scans miss. It combines AI-SAST and AI-SCA on a single platform, so the code your team writes and the third-party dependencies it relies on are covered in one place.

Key capabilities of Maze Code:

• AI-SAST that understands your code: Rather than matching code against known patterns, Maze agents reason over your code’s structure and data flow to understand what it actually does, surfacing novel vulnerabilities and business-logic flaws that pattern-based scanners miss — alongside standard issues like SQL injection, XSS, and hardcoded credentials.
• AI-SCA with full call-chain reachability: Maze finds every vulnerable dependency and uses AI-built call graphs to trace the full call chain, however many hops deep the vulnerability sits, determining exploitability with context from your code and cloud.
• Exploitability, not just reachability: Maze proves what is genuinely exploitable in your environment, prioritizes those findings by real risk, and closes reachable-but-not-exploitable findings before they ever reach your team.
• Automated, developer-ready remediation: When a vulnerability is exploitable, agents trace its root cause, identify the owner, and ship a verified fix as a pull request; when no fix exists, they recommend a mitigation instead.
• Fits where developers already work: Maze Code runs in your CI/CD pipeline (GitHub Actions, GitLab CI, CircleCI), surfaces findings right at the pull request, and pulls fixes into coding agents so teams don’t need another dashboard.
• Unified code and cloud context: Running on the same platform as Maze Cloud, agents mesh code investigations with cloud findings and leave you with one unified ticket per issue instead of duplicates.
• Works with your existing scanners: Maze Code can ingest and deduplicate findings from the scanners you already run, or use its own built-in scanner.

Ready to move from endless SAST and DAST findings to the handful that are actually exploitable? Explore Maze Code to see how AI agents investigate, prove, and fix code vulnerabilities the way your best security engineer would.