Static Application Security Testing (SAST): SAST Security Explained

What is Static Application Security Testing (SAST)?

SAST (Static Application Security Testing) is a white-box testing method that analyzes source code, bytecode, or binaries for security vulnerabilities without executing the program. Integrated early in the development lifecycle, it helps developers spot and fix flaws, like SQL injection and hardcoded secrets, before code is compiled.

How SAST works:

• Static analysis: It evaluates the application from the “inside out” by reviewing the structure and logic of the code without running it.
• Shift-left security: Scans are often integrated directly into CI/CD pipelines and IDEs to catch issues early, making them cheaper and easier to fix.
• Techniques: Uses methods like dataflow analysis and taint tracking to trace how untrusted data moves through the codebase.

How it compares:

• SAST analyzes static source code.
• DAST (Dynamic Application Security Testing) acts like an external attacker and scans a running web application to find vulnerabilities.
• SCA (Software Composition Analysis) scans third-party open-source libraries and dependencies for known vulnerabilities and license issues.

Why Is SAST a Crucial Element of Application Security?

SAST is just one component of a comprehensive application security program. SAST can help identify potential vulnerabilities in an application’s source code, and should be implemented as part of a holistic application security platform that also identifies other vulnerabilities, such as those that may arise from misconfigured servers, insecure network connections, or vulnerabilities in third-party software components.

Here are some of the important reasons to implement SAST: 

• It can be integrated into the software development process early on, giving real-time feedback, and allowing developers to address security issues before they become more difficult and expensive to fix. 
• SAST can help organizations comply with industry regulations and standards, such as the Payment Card Industry Data Security Standard (PCI DSS).
• SAST scans all of your codebase, so your AppSec teams don’t need to decide what to scan. 
• SAST is easy to automate, and teams can set up scans to run at any stage of the SLDC.

Common Vulnerabilities Detected by SAST

SAST tools can detect a wide range of security issues directly in application source code, helping teams identify and remediate vulnerabilities before the application is deployed. While exact coverage depends on the programming language, framework, and tool configuration, common vulnerabilities detected by SAST include:

Injection and Input Handling Vulnerabilities

• SQL injection (SQLi): SAST can detect cases where untrusted user input is passed into SQL queries without proper validation, sanitization, or parameterization. These vulnerabilities may allow attackers to manipulate database queries, access sensitive data, modify records, or execute unauthorized database operations.
• Cross-site scripting (XSS): SAST tools can identify situations where user-controlled input is rendered in a web page without proper output encoding or sanitization. XSS vulnerabilities can allow attackers to inject malicious scripts into trusted pages, potentially stealing session tokens, redirecting users, or performing actions on behalf of victims.
• Command injection: SAST tools can detect cases where user-controlled input is passed into operating system commands without proper validation or escaping. This may allow attackers to execute arbitrary commands on the host system.
• Path traversal: SAST can identify file access patterns where untrusted input is used to construct file paths. If not properly validated, attackers may be able to access unauthorized files or directories outside the intended application scope.
• Server-side request forgery (SSRF): SAST can identify code paths where user-controlled URLs or network destinations are used in server-side requests. SSRF vulnerabilities may allow attackers to access internal services, cloud metadata endpoints, or restricted network resources.
• Missing or improper input validation: SAST can flag areas where external input is accepted without sufficient validation. This can contribute to several vulnerability types, including injection attacks, business logic flaws, and unsafe processing of malformed data.

Sensitive Data and Access Control Risks

• Hardcoded credentials or API keys: SAST tools can identify secrets embedded directly in source code, such as passwords, API keys, access tokens, private keys, database credentials, and cloud service credentials. Hardcoded secrets are risky because they can be exposed through source code repositories, logs, build artifacts, or accidental leaks.
• Weak cryptography: SAST tools can flag the use of outdated or insecure cryptographic algorithms, weak cipher modes, insufficient key lengths, insecure random number generators, or improper encryption implementations. Examples may include MD5, SHA-1, hardcoded encryption keys, or encryption without proper initialization vectors.
• Insecure data handling: SAST can identify unsafe handling of sensitive data, such as storing passwords in plaintext, logging personally identifiable information, exposing tokens in error messages, or transmitting sensitive data without adequate protection. These issues can increase the risk of data leakage and compliance violations.
• Authentication and authorization flaws: SAST can help identify missing authentication checks, weak password handling, insecure session management, improper role validation, or authorization bypass risks in application logic.
• Insecure error handling: SAST tools can detect error messages, stack traces, or exception handling patterns that may expose sensitive implementation details. Attackers can use this information to better understand the application and plan further attacks.

Memory Safety and Application Logic Vulnerabilities

• Buffer overflows: In languages such as C and C++, SAST can detect unsafe memory operations that may allow data to exceed the allocated size of a buffer. Buffer overflows can lead to application crashes, data corruption, privilege escalation, or remote code execution.
• Insecure deserialization: SAST tools may detect unsafe deserialization of untrusted data, which can lead to object injection, application logic abuse, privilege escalation, or remote code execution depending on the language and framework.
• Unsafe file uploads: SAST tools can detect insecure file upload logic, such as missing file type validation, unsafe file storage paths, or insufficient checks on uploaded content. These issues can lead to malware uploads, web shell deployment, or unauthorized file access.

How SAST Works

Static Analysis

SAST works by performing static analysis on an application’s source code, bytecode, or binaries without executing the program. Instead of testing the application while it is running, the tool inspects the code itself to detect patterns, logic flaws, insecure coding practices, and violations of predefined security rules.

During a scan, the SAST tool parses the codebase and builds an internal representation of the application. This may include syntax trees, control flow graphs, data flow models, and dependency relationships. By analyzing how data moves through the application, the tool can identify whether sensitive or untrusted input reaches dangerous functions without proper validation, sanitization, or access controls.

For example, a SAST tool may detect that user-supplied input is passed directly into a database query, creating a possible SQL injection risk. It may also identify insecure cryptographic implementations, hardcoded credentials, weak authentication logic, unsafe file handling, cross-site scripting risks, or missing input validation.

Because the analysis is performed before the application is deployed, SAST gives developers visibility into security weaknesses while the code is still being written. This makes it especially useful for identifying vulnerabilities that originate in the application’s logic, architecture, or coding practices.

Shift-Left Security

SAST is strongly associated with the concept of shift-left security, which means moving security testing earlier in the Software Development Life Cycle. Rather than waiting until late-stage testing, pre-production reviews, or post-release assessments, SAST enables teams to detect and remediate vulnerabilities during development.

This approach helps reduce remediation costs and development delays. When a vulnerability is discovered while a developer is actively working on the relevant code, it is usually easier to understand, fix, and retest. By contrast, vulnerabilities discovered late in the release cycle may require extensive rework, delay deployment, or create pressure to accept security risk in order to meet business deadlines.

SAST tools can be integrated directly into developer workflows, including Integrated Development Environments, source code repositories, pull requests, build pipelines, and CI/CD processes. This allows developers to receive feedback in near real time and correct issues before insecure code is merged into the main branch.

Shift-left security also helps build a stronger security culture across engineering teams. Developers become more aware of secure coding practices, while AppSec teams can focus on higher-risk issues rather than manually reviewing every code change. Over time, this can reduce the number of vulnerabilities introduced into the codebase and improve the overall maturity of the organization’s security program.

SAST Techniques

SAST tools use several techniques to identify vulnerabilities, insecure coding patterns, and compliance risks within source code. These techniques often work together to provide a deeper understanding of how the application behaves and where security weaknesses may exist.

Code Pattern and Structural Analysis

• Rule-based scanning: Compares code against predefined security rules to detect insecure patterns such as hardcoded credentials, weak cryptography, and missing input validation.
• Semantic analysis: Examines the meaning and context of code to distinguish between potentially risky code and exploitable vulnerabilities, helping reduce false positives.
• Framework-aware analysis: Understands framework-specific behavior, such as routing, authentication, and templating, to improve detection accuracy.
• Configuration and dependency-aware analysis: Reviews configuration files and dependencies for insecure settings, exposed secrets, and risky framework configurations.

Data and Execution Flow Analysis

• Data flow analysis: Tracks how data moves through the application to identify cases where untrusted input reaches sensitive operations without proper validation.
• Control flow analysis: Examines execution paths to verify that security checks are applied before sensitive actions occur.
• Taint analysis: Tracks untrusted input through the codebase to identify injection risks, unsafe deserialization, and similar vulnerabilities.
• Path analysis: Determines whether vulnerable code is realistically reachable, helping teams prioritize exploitable findings.

Risk Prioritization and Developer Guidance

• Remediation guidance and prioritization: Provides severity ratings, explanations, and fix recommendations to help developers resolve the most important vulnerabilities first.

SAST Pros and Cons

SAST provides several important advantages for development and security teams because it analyzes code early in the Software Development Life Cycle, before vulnerabilities reach production.

SAST pros include:

• Finds vulnerabilities early: SAST helps identify security issues while developers are still writing or reviewing code, making vulnerabilities easier and less expensive to fix.
• Supports shift-left security: By integrating security checks into IDEs, repositories, pull requests, and CI/CD pipelines, SAST allows teams to address risks earlier instead of waiting for late-stage testing.
• Does not require a running application: Since SAST analyzes source code, bytecode, or binaries, teams can scan applications before they are fully built, deployed, or configured in a test environment.
• Provides broad code coverage: SAST can scan the entire codebase, including branches, functions, and code paths that may not be reached during runtime testing.
• Easy to automate: SAST tools can be configured to run automatically during commits, pull requests, scheduled scans, or build processes, making security testing part of the normal development workflow.
• Helps developers learn secure coding practices: Many SAST tools provide explanations, severity ratings, and remediation guidance, helping developers understand why an issue matters and how to fix it.
• Supports compliance requirements: SAST can help organizations demonstrate secure development practices and identify issues related to standards such as PCI DSS, HIPAA, SOC 2, ISO 27001, and OWASP guidance.
• Reduces downstream remediation costs: Fixing a vulnerability during development is typically faster and less disruptive than fixing it after deployment, during a security audit, or following an incident.

Although SAST is a valuable part of an application security program, it also has limitations. It works best when combined with other testing methods, manual review, and runtime security validation.

• Can produce false positives: SAST tools may flag code as vulnerable even when the risk is mitigated elsewhere in the application. This can create noise and require AppSec teams or developers to manually review findings.
• May miss runtime-specific vulnerabilities: Because SAST does not execute the application, it may not detect issues that only appear during runtime, such as authentication flow problems, environment-specific misconfigurations, business logic flaws, or vulnerabilities caused by live application behavior.
• Requires proper configuration: To be effective, SAST tools often need tuning based on the organization’s languages, frameworks, coding standards, and risk priorities. Poor configuration can lead to missed vulnerabilities or excessive alerts.
• Can slow down development workflows: Large codebases or overly strict scan policies may increase build times, delay pull requests, or frustrate developers if findings are not prioritized properly.
• Limited context for exploitability: SAST can identify potentially vulnerable code patterns, but it may not always determine whether a vulnerability is truly reachable, exploitable, or exposed in production.
• Language and framework support varies: Not all SAST tools support every programming language, framework, or custom architecture equally well. Coverage and accuracy may vary depending on the technology stack.

SAST cons include:

• Can produce false positives: SAST tools may flag code as vulnerable even when the risk is mitigated elsewhere in the application. This can create noise and require AppSec teams or developers to manually review findings.
• May miss runtime-specific vulnerabilities: Because SAST does not execute the application, it may not detect issues that only appear during runtime, such as authentication flow problems, environment-specific misconfigurations, business logic flaws, or vulnerabilities caused by live application behavior.
• Requires proper configuration: To be effective, SAST tools often need tuning based on the organization’s languages, frameworks, coding standards, and risk priorities. Poor configuration can lead to missed vulnerabilities or excessive alerts.
• Can slow down development workflows: Large codebases or overly strict scan policies may increase build times, delay pull requests, or frustrate developers if findings are not prioritized properly.
• Limited context for exploitability: SAST can identify potentially vulnerable code patterns, but it may not always determine whether a vulnerability is truly reachable, exploitable, or exposed in production.
• Language and framework support varies: Not all SAST tools support every programming language, framework, or custom architecture equally well. Coverage and accuracy may vary depending on the technology stack.

How AI Is Transforming SAST

AI is making SAST more developer-friendly and actionable by helping developers understand not only what vulnerability was found, but also why it matters and how it can be fixed. Traditional SAST identifies vulnerabilities in source code, but developers still need to interpret the finding, assess the risk, and decide on the right remediation approach. 

AI can support developer security workflows by:

• Explaining vulnerabilities in clearer, more practical language.
• Summarizing the potential security impact of a finding.
• Providing remediation guidance that is easier for developers to apply.
• Making SAST results more useful inside IDEs, pull requests, repositories, and CI/CD pipelines.

AI can help reduce remediation time: 

• Identifying a vulnerability is only the first step; developers still need to research the issue, understand the vulnerable code path, and implement a secure fix. 
• AI-assisted SAST can shorten this process by providing suggested fixes, secure coding recommendations, or replacement code tailored to the specific issue. 
• AI can reduce context switching for developers and help security teams move from simply reporting vulnerabilities to helping teams resolve them faster.

AI assisted prioritization:

SAST tools may generate many findings, including false positives, duplicate issues, or low-risk vulnerabilities. AI can help analyze the context around each finding, such as where the vulnerable code appears, how data flows through the application, and whether the issue is likely to be exploitable. This can help teams:

• Focus first on the vulnerabilities that present the greatest risk.
• Reduce alert fatigue among developers.
• Spend less time reviewing low-value findings.
• Improve the overall efficiency of AppSec workflows.

Securing AI-generated code and agentic development workflows

AI makes SAST more important in the age of AI-generated code. As more developers use AI coding assistants, organizations can produce code faster, but they may also introduce insecure patterns at greater scale. 

AI-generated code can contain flaws like injection risks, weak authentication logic, insecure data handling, exposed secrets, or unsafe dependency usage. SAST provides an important control layer by scanning both human-written and AI-generated code before it reaches production.

SAST vs DAST vs IAST vs SCA

You’ve probably heard of SAST mentioned alongside terms like DAST and IAST. Let’s break down the differences and how they compare.

Static vs Dynamic Application Security Testing (DAST)

SAST analyzes the source code of an application.

It’s performed early in the development lifecycle, and often integrated directly into the development environment.

This allows for early detection, comprehensive coverage and makes it a developer-friendly solution.

However, SAST can produce false positives or negatives since it does not analyze vulnerabilities at runtime.

This is where DAST comes in.

DAST (Dynamic Application Security Testing) is a set of security technologies that analyzes the application from the outside while it’s running. It simulates an attacker’s approach to discover vulnerabilities. DAST is typically performed later in the development lifecycle, once a runnable version of the application is available.

The DAST approach allows identifying vulnerabilities that only become apparent when the application is running, such as issues with user sessions, authentication, and server configurations.

DAST can also test any application accessible via a network, regardless of the language or technology used to build it.

However, relying solely on DAST has its limitations.

Late detection of vulnerabilities makes them more expensive and time-consuming to fix.

In addition, DAST potentially misses vulnerabilities in unexecuted code.

Analyzing the results is also less developer-friendly, because it typically requires more security-specific knowledge to interpret and act on the results.

SAST vs. DAST – Comparison Table

Static vs Interactive Application Security Testing (IAST)

Interactive Application Security Testing (IAST) is a security testing approach that operates within the application during runtime, identifying vulnerabilities in real-time as the application processes data. Unlike traditional static testing, IAST requires the application to be actively used, often relying on functional testing to provide realistic user interactions. This dependency means that the quality and depth of IAST results are directly influenced by the thoroughness of these functional tests. In other words, if the functional tests do not cover all aspects of the application, IAST may miss critical vulnerabilities that could otherwise be exposed in live environments. 

As a result, for IAST to deliver valuable insights, organizations need to maintain extensive and up-to-date functional testing suites, which many development teams find to be challenging. The ongoing creation, maintenance, and updating of functional tests can be prohibitively time-consuming and resource-intensive, particularly as applications evolve rapidly in agile and CI/CD environments. As a result, teams often struggle to keep their functional testing aligned with the application’s current state, which can lead to gaps in IAST coverage and less reliable security findings. Furthermore, teams may find it challenging to ensure their functional tests cover all parts of the application, potentially leaving security blind spots.

Due to these challenges, many organizations are opting for alternative approaches, such as DAST combined with API security as part of a complete and comprehensive application security approach. Like IAST, DAST analyzes live applications, and does not rely on functional tests, as it operates from the outside-in, assessing the application’s behavior in response to simulated attacks and identifying vulnerabilities that would be accessible to external threats. Additionally, API security testing focuses on securing API endpoints, which are increasingly targeted in modern applications. By incorporating DAST and API security testing into their application security programs, organizations can maintain a flexible, lower-maintenance solution that adapts easily to changes in code and infrastructure.

When comparing SAST to IAST, teams should recognize it’s like comparing apples and oranges. SAST is ideal for detecting and remediating vulnerabilities in the coding and development process, allowing developers to address security issues before the application is fully built or deployed.

SAST vs. IAST – Comparison Table

SAST vs. SCA: How to Combine SAST with SCA

Software Composition Analysis (SCA) is a set of technologies used to identify and manage the risks associated with using open-source and third-party components.

SCA tools evaluate third-party components for security vulnerabilities, licensing issues, and outdated versions to ensure the safety and compliance of the software product.

With the right SCA tools, organizations can boost productivity while remaining secure and compliant.

SAST and SCA complement each other to provide a layered defense against a wide range of vulnerabilities, covering both the internal and external code.

While SAST helps write secure code from the start, SCA ensures that open-source third-party components don’t introduce new vulnerabilities or violate licenses.

Both SAST and SCA can be integrated early in the development process and into the CI/CD pipeline, providing continuous, automated security feedback.

How to Measure SAST Accuracy

SAST accuracy refers to how reliably a SAST tool identifies real security vulnerabilities while minimizing noise, missed issues, and low-value findings. Accuracy is important because even a powerful SAST tool can create friction if it overwhelms developers with false positives or fails to detect meaningful risks. Measuring SAST accuracy helps security teams evaluate tool performance, tune rules, improve developer trust, and prioritize the findings that matter most.

SAST accuracy is usually evaluated with a combination of detection metrics, triage outcomes, and developer workflow indicators. No single metric tells the full story, so organizations should assess both how well the tool finds vulnerabilities and how useful its findings are in practice.

Key SAST Accuracy Metrics

SAST accuracy is best measured through a combination of metrics that show both detection quality and practical usefulness. These metrics help teams understand whether a tool is finding real vulnerabilities, how much noise it creates, and how effectively developers can act on its findings. 

• True positives: True positives are findings that correctly identify real vulnerabilities in the code. A high number of true positives indicates that the tool is successfully detecting valid security issues that require attention.
• False positives: False positives are findings that the tool flags as vulnerabilities but are not actually exploitable or relevant in the application’s context. A high false positive rate can lead to alert fatigue, reduce developer trust, and increase the time AppSec teams spend on manual triage.
• False negatives: False negatives are vulnerabilities that exist in the code but are missed by the SAST tool. These are especially important because they create a false sense of security and may allow real risks to reach production.
• Precision: Precision measures how many of the tool’s reported findings are actually valid. In SAST, high precision means developers are less likely to waste time reviewing inaccurate or low-value alerts.
• Recall: Recall measures how many real vulnerabilities the tool successfully detects. High recall means the tool is effective at finding a large portion of the actual security issues in the codebase.
• F1 score: The F1 score combines precision and recall into a single metric. It is useful when teams want to balance finding as many real vulnerabilities as possible with reducing false positives. A higher F1 score generally indicates a better balance between detection coverage and finding quality.
  Continue Reading on F1 Score SAST Accuracy Metric >>
• False positive rate: This metric measures how often the tool incorrectly flags safe code as vulnerable. Reducing the false positive rate is critical for maintaining developer adoption and making SAST findings actionable.
• False negative rate: This metric measures how often the tool misses real vulnerabilities. A lower false negative rate improves confidence that the tool is providing meaningful security coverage.
• Mean time to remediate: Mean time to remediate, or MTTR, measures how long it takes teams to fix confirmed SAST findings. While not a pure detection metric, it helps show whether SAST results are clear, prioritized, and actionable enough for developers to resolve quickly.
• Finding acceptance rate: This measures how often developers or security teams accept SAST findings as valid after triage. A low acceptance rate may indicate excessive noise, poor rule tuning, or weak context around findings.

Improving SAST Accuracy

SAST accuracy can often be improved through proper configuration, rule tuning, framework-aware analysis, and regular review of triage results. Teams should customize rules based on their languages, frameworks, architecture, and risk tolerance. They should also suppress or refine noisy rules, validate findings against real application context, and use developer feedback to improve future scan quality.

Modern SAST tools increasingly use contextual analysis, data flow analysis, reachability analysis, and AI-assisted prioritization to improve accuracy. These capabilities help distinguish between theoretical code issues and vulnerabilities that are more likely to be exploitable in practice.

Ultimately, the goal of SAST accuracy is not simply to produce more findings. The goal is to produce the right findings: vulnerabilities that are valid, relevant, prioritized, and clear enough for developers to fix efficiently.

How Can SAST Tools Be Effectively Implemented?

Effective implementation of Static Application Security Testing (SAST) involves several crucial steps. Here are some of the key steps that can help ensure successful SAST implementation:

Selection of appropriate SAST tool

Choosing the right SAST tool is important to ensure accurate and effective analysis of an application’s source code. Factors such as language support, scalability, and reporting capabilities should be considered when selecting a tool.

Integration into the development process

SAST should be integrated into the SLDC as early as possible to identify security vulnerabilities early on. This involves setting up SAST as part of the development pipeline and ensuring that it is regularly executed as part of the build process.

Configure the tool accurately

Once the SAST tool is selected, it needs to be properly configured to ensure accurate analysis of the code. This includes defining the scope of the analysis, setting up rules and policies, and customizing the analysis settings based on the specific needs of the organization.

Analyze and triage results

The SAST tool will generate a large number of results, so it is important to have a process in place to analyze and triage the findings. This involves prioritizing the results based on their exploitability, severity and relevance, assigning them to the appropriate developer or team, and tracking the status of the remediation efforts.

Remediate vulnerabilities

Once the vulnerabilities have been identified and prioritized, developers need to remediate them in a timely manner. This involves fixing the code and testing the changes to validate that they do not introduce any new vulnerabilities. Robust SAST tools will offer advanced features, such as finding the best fix location for developers.

Implement continuous improvement

SAST is an iterative process, and there is always room for improvement. Organizations should regularly review their SAST process to identify areas for improvement, such as refining the analysis rules, optimizing the workflow, or updating the tool.

Key Features of a Robust SAST Security Solution

To help you to choose the right SAST solution, you need to know more than simply answering the question, what does SAST stand for?

Here are nine questions to ask your SAST vendor:

• Can I perform deep and wide scans? Sometimes teams will want to know every potential vulnerability including zero-days, and other times they will want to scan widely – uncovering a high level view of the most critical vulnerabilities. Can your tool do both?
• What presets do you offer? SAST tools should have template rulesets that come out of the box, to meet common use cases such as compliance with PCI DSS or FISMA, or to cover the most common threats. 
• Do you include auto-remediation? AI is a powerful tool in application security testing, and robust SAST solutions will include auto-remediation features that automatically suggest code to fix vulnerabilities. 
• Do you use Application Security Posture Management? ASPM allows you to ingest all of your data from all of your tools into a single dashboard, viewing apps by risk level and providing guidance on mitigation and risk reduction. 
• Are you application-centric? Does your SAST tool scan in silos, or can you see how application flows interact and build connections? Look for features such as data-flow analysis and symbolic execution. 
• How do you reduce false positives and negatives? This is a common issue with SAST, and the problem can be reduced with the help of a customizable query language and the ability to fine-tune scanning. 
• Do you scan uncompiled code? Ask about the ability to scan new or changed code incrementally to save time and resources, as well as the option to scan at the repository level, which reduces the reliance on rebuilds. 
• Can you direct developers to best-fix locations? By looking at the application holistically, developers can be empowered to make the fix themselves, often solving multiple vulnerabilities with a single fix, dramatically reducing MTTR. 
• Will you be able to scan in real-time? Real-time scanning in the IDE provides instant feedback, and protects against vulnerabilities introduced by human-generated code, as well as any issues with AI-generated code written by tools such as GitHub Copilot. 
• How do you hero developers? How will this SAST tool help developers to learn as they work, empowering them with added security skills to add to their bow without taking them from their flow of work? 
• How many languages do you support? SAST is limited by its language support, with a strong code dependency. The more languages and frameworks your SAST tool supports, the less of an issue this will be for your AppSec teams. 
• Is the SAST tool integrated into development? The success of your solution hinges on developer adoption. If your SAST lives where they work, this is far more likely. Think about integrating with the IDE, SCM solutions, CI/CD tools, and more. 

SAST Tool Considerations Summary Table

By asking these key questions during discovery, organizations can effectively implement SAST and improve the security of their applications. Checkmarx provides an eBook to help you find a SAST tool that works for you. Check out these 10 considerations when choosing a SAST solution for your AppSec program.