What Is Application Security Testing and How To Get It Done

| Harness Blog

• Automated, policy-driven application security testing (AST) embedded in CI/CD pipelines eliminates manual bottlenecks and ensures consistent protection without slowing down developer workflows.
• AI-powered triage and intelligent scanning significantly reduce false positives and alert fatigue, enabling teams to focus on real, exploitable vulnerabilities and accelerate remediation.
• Scalable governance is achieved through risk-tiered controls, flexible pipeline templates, and policy-as-code, empowering organizations to balance developer velocity with enterprise security and compliance requirements.

If you’re building modern software today, security isn’t a final step. It’s an ongoing process woven into every stage of development. And that’s exactly where Application Security Testing (AST) comes in.

AST helps teams identify and fix vulnerabilities before they can be exploited. But with rapid release cycles, complex architectures, and growing reliance on open-source and AI, traditional security approaches often fall short.

That’s why modern platforms like Harness are embedding security directly into CI/CD pipelines, making it easier to test, prioritize, and remediate issues without slowing down delivery. So how do we actually implement AST in a way that keeps up with development speed?

What is Application Security Testing?

Application Security Testing (AST) is the process of analyzing software applications to identify, understand, and remediate security vulnerabilities throughout the entire software development lifecycle (SDLC).

Unlike traditional security methods that often focus only on infrastructure or perimeter defenses, AST is specifically concerned with the application layer: the code, logic, and components that directly interact with users and data.

This includes:

• Custom-written application code
• APIs and integrations
• Open-source libraries and dependencies
• Runtime behavior and configurations
• CI/CD pipelines and build artifacts

The purpose of AST is not just to “find bugs,” but to uncover security weaknesses that could lead to real-world exploitation, such as:

• Data breaches
• Unauthorized access
• System manipulation
• Service disruptions

What makes AST unique is that it doesn’t rely on a single method. Instead, it combines multiple testing approaches, each designed to analyze different parts of an application in different ways.

In other words, AST is not a tool, but is an ecosystem of practices and technologies working together to secure modern applications.

Why Application Security Testing Matters More Than Ever

If we look at how software development has evolved over the past decade, it becomes clear why AST has become so critical.

The Explosion of Software Complexity

Applications today are far more complex than they used to be. A single app might include:

• Microservices running across multiple environments
• Third-party APIs and integrations
• Dozens (or hundreds) of open-source dependencies
• Cloud infrastructure and containerized workloads

Each of these components introduces potential vulnerabilities. The more moving parts you have, the more opportunities there are for something to go wrong.

The Speed of Modern Development

With DevOps and CI/CD practices, teams are deploying code faster than ever, sometimes multiple times a day.

While this speed is great for innovation, it creates a challenge for security:

• There’s less time for manual review
• Vulnerabilities can be introduced and deployed quickly
• Traditional “end-of-cycle” testing becomes ineffective

Without continuous security testing, risks can slip through unnoticed.

The Expanding Attack Surface

Attackers are no longer limited to exploiting obvious weaknesses. Today’s threat landscape includes:

• API vulnerabilities
• Supply chain attacks
• Misconfigured cloud services
• AI-specific threats like prompt injection

This means security teams must think beyond just code. They need to secure the entire ecosystem surrounding the application.

The Cost of Getting It Wrong

Security breaches are not just technical issues; they are business risks. A single vulnerability can lead to:

• Financial loss
• Regulatory penalties
• Brand damage
• Loss of customer trust

AST helps mitigate these risks by identifying issues early, when they are easier and cheaper to fix.

Types of Application Security Tests

To truly understand AST, we need to break down the different types of testing that fall under it. Each one plays a specific role in identifying vulnerabilities.

‍Static Application Security Testing (SAST) 

Static Application Security Testing (SAST) focuses on analyzing the application’s source code, bytecode, or binaries without executing the program.

This approach works by scanning code for patterns that are known to be insecure, such as:

• Unsanitized user inputs (which can lead to injection attacks)
• Hardcoded credentials
• Insecure cryptographic practices

One of the biggest advantages of SAST is that it can be applied very early in the development process, even while developers are still writing code.

This early detection is powerful because:

• Issues are easier to fix before code is deployed
• Developers receive immediate feedback
• Security becomes part of the coding process, not a separate step

However, SAST can sometimes generate false positives and may lack context about how the application behaves at runtime.

‍Software Composition Analysis (SCA)

Modern applications rely heavily on open-source components. In many cases, the majority of an application’s codebase comes from third-party libraries.

Software Composition Analysis (SCA) focuses on identifying risks within these dependencies.

It answers critical questions like:

• Are we using libraries with known vulnerabilities?
• Are there outdated or unsupported packages?
• Are there licensing issues that could create legal risks?

This is especially important because attackers often target known vulnerabilities in widely used libraries.

Without SCA, teams may unknowingly introduce serious risks into their applications.

‍Secret Detection

Secret Detection focuses on identifying sensitive information that has been accidentally exposed within your codebase or development workflows.

This includes:

• API keys
• Passwords
• Tokens
• Private certificates

These secrets are often unintentionally committed to source control, embedded in configuration files, or exposed in logs. The risk is significant. If attackers gain access to these credentials, they can bypass traditional security controls entirely and gain direct access to systems or data.

‍Container Scanning

As organizations adopt containerized environments (like Docker and Kubernetes), container security has become a key part of AST. Container Scanning analyzes container images to identify vulnerabilities before they are deployed.

This includes:

• Vulnerabilities in base images
• Outdated or insecure packages
• Misconfigurations in container settings

Containers are often built using layered images, which means vulnerabilities can be inherited from upstream sources. Without proper scanning, vulnerabilities can propagate across environments and insecure containers can reach production unnoticed.

Dynamic Application Security Testing (DAST) 

Dynamic Application Security Testing (DAST) takes a completely different approach. Instead of analyzing code, it tests the application while it’s running.

DAST works like simulating an attacker interacting with your application from the outside.

It looks for vulnerabilities such as:

• Authentication flaws
• Misconfigured security settings
• Input validation issues

Because DAST operates in a live environment, it provides insights into how the application behaves in real-world conditions.

The tradeoff is that DAST:

• Requires a deployed application
• May not pinpoint the exact location of vulnerabilities in code

Still, it’s an essential layer of testing because it validates actual exploitability.

How To Get Application Security Testing Done

To make Application Security Testing effective, it needs to be integrated into your workflows, intelligently managed, and easy for developers to adopt. Here’s how to do it step by step:

1. Make Security Pipeline-Native – Start by embedding security directly into your CI/CD pipelines. This ensures every code change is automatically tested, policies are enforced consistently, and security scales with development speed instead of slowing it down.
2. Centralize Visibility – Use a unified platform to bring all security findings into one place. This helps eliminate duplicate issues, reduce noise, and gives teams a clear, holistic view of risks across the entire application.
3. Prioritize What Matters – Focus on vulnerabilities that are actually exploitable, reachable in runtime, and high impact. This allows teams to spend time fixing the most critical issues instead of getting overwhelmed by low-risk alerts.
4. Improve Developer Experience – Make security easy for developers to adopt by providing fast feedback, clear remediation steps, and seamless integrations into their existing tools. The smoother the experience, the higher the adoption.
5. Leverage Automation and AI – Use automation to reduce manual work and AI to improve detection, prioritization, and remediation. This helps teams move faster while maintaining strong security coverage.
6. Commit to Continuous Improvement – Treat security as an ongoing process. Continuously monitor for new vulnerabilities, update policies, and refine your approach as your application and threat landscape evolve.

Application Security Testing: Best Practices

Shift Application Security Testing Left

As part of an effective DevSecOps program, it is critical to run application security tests as early on in the software development life cycle as possible, which enables developers and security engineers to detect and fix security vulnerabilities with the least amount of disruption and toil.

Automate Application Security Testing

Reduce the amount of manual operations and automate application security testing where possible. Integrating security scanners with a software development platform and automatically running them within build and deploy pipelines is highly recommended.

Feed Clear And Actionable Vulnerability Information To Developers

In order for shift left security to be effective, developers need to be able to act fast and remediate vulnerabilities with as little toil as possible. The use of multiple security scanners often produces a mountain of vulnerability data which must be deduplicated and prioritized in order for developers to immediately begin remediating them.

Take Application Security Testing to the Next Level With Harness AST

Platform teams that embed intelligent application security testing in CI/CD pipelines stop playing defense against vulnerabilities. Your three-step path: standardize pipeline templates with required security gates, turn on PR-time scans with risk-tiered OPA policies, then add runtime verification with automated rollback when issues arise.

The payoff is immediate. No more manual compliance tracking and guesswork about security posture across services. Scorecards track progress across hundreds of teams while immutable audit trails prove governance without slowing releases. Teams get AI-driven remediation suggestions, policy violations fail fast, and production stays protected through automated verification.

Ready to eliminate security ticket-ops while proving ROI to leadership? See how Harness combines AI-powered pipelines, automated verification, and Policy as Code in one platform that scales with your teams.

Application Security Testing FAQ

Platform engineering leaders face the same questions when rolling out application security testing at scale. These answers provide practical guidance based on what actually works in production environments.

How can application security testing be automated within CI/CD pipelines without slowing down developer workflows?

Run lightweight checks like SAST and SCA on pull requests with fast fail thresholds. Reserve heavier DAST and fuzzing for nightly builds or specific deployment stages. Use policy-as-code to define risk-appropriate gates rather than blanket blocking. Security testing orchestration platforms can parallelize scans and provide unified results without pipeline bottlenecks.

What are the best practices for scaling application security testing across multiple engineering teams?

Start with standardized pipeline templates that embed required security checks by default. Onboard high-risk services first to prove value, then expand through self-service patterns. Use centralized policies with team-specific customization points. Provide reference implementations and sample configurations so teams can adopt secure patterns without starting from scratch.

How do you balance developer velocity with enterprise governance in application security testing?

Implement risk-tiered policies where internet-facing services get stricter controls than internal tools. Use advisory-only checks for low-risk applications to avoid unnecessary friction. Automate approvals and exception handling within CI/CD pipelines so governance decisions are logged without manual tickets. Application security frameworks should enforce compliance through automation, not human gatekeepers.

How do you measure success when implementing application security testing at scale?

Track median time to fix security findings, policy compliance rates by team, and developer adoption metrics. Monitor pipeline performance impact to ensure security checks don't slow delivery cadence. Use scorecards to incentivize security posture improvements and provide visibility into which teams need support versus recognition.

What role does AI play in modern application security testing and reducing false positives?

AI-driven triage prioritizes exploitable findings over noise, often reducing alert volume by 60-70%. Machine learning models can correlate findings across tools, suppress duplicates, and suggest remediation based on code context. Intelligent scanning focuses on changed code paths rather than full repository scans, reducing scan times while maintaining coverage.