IAST vs SAST vs DAST: The Complete 2026 Comparison Guide for Application Security

If you work in application security, you have encountered the acronyms SAST, DAST, and IAST hundreds of times. Yet the question "which one should we use?" continues to generate confusion in engineering teams, security departments, and boardrooms. The confusion is understandable: each technology solves a real problem, each has genuine limitations, and vendors have strong incentives to present their approach as the only one that matters.

This guide cuts through the marketing noise. We examine each testing methodology on its technical merits — how it works under the hood, what classes of vulnerabilities it excels at finding, where it falls short, and where it belongs in a modern software development lifecycle. By the end, you will have a clear framework for deciding not which approach is "best," but how to combine all three into a security testing strategy that leaves minimal gaps.

The Three Pillars of Application Security Testing

Application security testing (AST) encompasses a family of techniques designed to identify vulnerabilities in software before attackers do. While the field includes additional approaches like Software Composition Analysis (SCA) and Runtime Application Self-Protection (RASP), the three foundational testing methodologies — SAST, DAST, and IAST — represent fundamentally different strategies for the same goal: finding exploitable weaknesses in application code.

The Core Distinction

The simplest way to understand the three approaches is by when they analyze code and what they have access to:

• SAST (Static Application Security Testing) analyzes source code, bytecode, or binary code without executing the application. It reads the code the way a human reviewer would — tracing data flows, identifying dangerous patterns, and flagging paths where untrusted input reaches sensitive operations.
• DAST (Dynamic Application Security Testing) tests a running application from the outside. It sends crafted HTTP requests and examines the responses, treating the application as a black box. It has no access to source code, internal state, or architecture.
• IAST (Interactive Application Security Testing) instruments the application at runtime with agents that observe code execution from the inside. It combines the runtime context of DAST with the code-level visibility of SAST, monitoring data as it flows through the actual running application.

Each metaphor carries an important truth: no single perspective gives you the full picture. The architect catches design flaws that the inspector would never notice. The inspector finds problems that only manifest in the real, constructed building. And X-ray vision reveals the relationship between what you see on the surface and what is happening underneath. Together, they provide comprehensive coverage. Individually, they leave blind spots.

SAST: Static Application Security Testing

Static Application Security Testing analyzes an application's source code, intermediate representation (such as Java bytecode or .NET IL), or compiled binary to identify security vulnerabilities without ever running the application. SAST operates on the principle that many categories of vulnerabilities have recognizable code-level patterns that can be detected through automated analysis of program structure and data flow.

How SAST Works

A modern SAST engine performs several analysis passes over the codebase, each building on the results of the previous one:

1. Lexical and syntactic analysis: The tool parses source code into an Abstract Syntax Tree (AST), resolving language-specific constructs, imports, class hierarchies, and method signatures. This phase establishes the structural foundation for all subsequent analysis.
2. Semantic analysis: The tool resolves types, method overloads, variable scoping, and inheritance chains. For strongly typed languages like Java or C#, this phase is highly accurate. For dynamically typed languages like Python or JavaScript, the tool must use heuristic type inference, which introduces uncertainty.
3. Control flow analysis: The tool constructs a control flow graph (CFG) that represents every possible execution path through the program, including branches, loops, exception handlers, and early returns. This graph determines which code paths are reachable under which conditions.
4. Data flow and taint analysis: This is the core of SAST vulnerability detection. The engine identifies sources (entry points where untrusted data enters the application), sinks (dangerous operations like SQL execution, file system access, or command execution), and sanitizers (functions that neutralize dangerous data). It then traces every path from every source to every sink. If tainted data reaches a sink without passing through a recognized sanitizer, the tool reports a vulnerability.
5. Pattern matching: In addition to taint analysis, SAST tools maintain libraries of known vulnerable code patterns — hardcoded credentials, weak cryptographic algorithms, insecure random number generators, missing authentication checks — that can be detected through structural pattern matching on the AST.

What SAST Finds Best

• Injection vulnerabilities (SQL injection, command injection, XPath injection, LDAP injection) where taint analysis can trace untrusted input to a dangerous sink
• Cross-site scripting (XSS) where user input flows to HTML output without encoding
• Hardcoded secrets (API keys, passwords, tokens embedded in source code)
• Weak cryptography (MD5 hashing, DES encryption, insufficient key lengths, insecure random number generation)
• Path traversal where user input controls file system paths without validation
• Insecure deserialization patterns in languages that support native serialization
• Missing security controls (absent CSRF tokens, missing authentication annotations, unvalidated redirects)

Example: SAST Detecting a SQL Injection

// C# - SAST taint analysis traces 'username' from source to sink
[HttpPost("login")]
public IActionResult Login(string username, string password)
{
    // SOURCE: 'username' comes from HTTP request parameter (untrusted)
    // PROPAGATOR: string interpolation propagates taint
    string query = $"SELECT * FROM Users WHERE Username = '{username}'";

    // SINK: ExecuteSqlRaw executes raw SQL
    var user = _context.Users.FromSqlRaw(query).FirstOrDefault();

    // SAST finding: CWE-89 SQL Injection
    // Tainted data flows: username (source) -> query (propagation) -> FromSqlRaw (sink)
    // No sanitizer detected on the path
    return user != null ? Ok() : Unauthorized();
}

// C# - SAST recognizes parameterized query as a sanitizer
[HttpPost("login")]
public IActionResult Login(string username, string password)
{
    // SAST traces 'username' but finds parameterized binding (sanitizer)
    var user = _context.Users
        .FromSqlRaw("SELECT * FROM Users WHERE Username = {0}", username)
        .FirstOrDefault();

    // Better yet: use LINQ (no raw SQL, inherently safe)
    var user = _context.Users
        .FirstOrDefault(u => u.Username == username);

    return user != null ? Ok() : Unauthorized();
}

SAST Strengths

• Shift-left integration: SAST runs on source code, meaning it can be integrated into IDEs, pre-commit hooks, and the earliest stages of CI/CD pipelines. Vulnerabilities are found before the code is even compiled or deployed.
• Full code coverage: SAST analyzes every code path in the repository, including error handlers, admin panels, debug endpoints, and rarely-executed branches that DAST may never reach through crawling.
• No running environment required: No servers, no databases, no test data, no deployment infrastructure. SAST analyzes code directly, making it the simplest tool to run in terms of environment setup.
• Precise remediation guidance: Because SAST operates on source code, it can point to the exact file, line number, and variable that causes the vulnerability, making remediation straightforward.
• Language-specific depth: Good SAST tools understand framework conventions (Spring Security annotations, ASP.NET authorization attributes, Django middleware) and can detect framework-specific misconfigurations.

SAST Limitations

• False positives: SAST's conservative analysis philosophy means it reports anything that might be vulnerable. Custom sanitization functions, application-specific validation logic, and framework behaviors the tool does not model all generate false positives. Industry benchmarks show false positive rates between 20% and 60% depending on the tool and codebase.
• No runtime context: SAST cannot verify whether a vulnerability is actually exploitable. A code path may be theoretically vulnerable but protected by a WAF, network segmentation, or authentication middleware that SAST cannot see.
• Configuration blindness: SAST analyzes code, not deployment configuration. Server misconfigurations, exposed admin interfaces, weak TLS settings, and missing security headers are invisible to static analysis.
• Dynamic language challenges: In Python, Ruby, JavaScript, and other dynamically typed languages, SAST must infer types at analysis time. Metaprogramming, dynamic dispatch, eval() usage, and monkey-patching create analysis paths that are difficult or impossible to resolve statically.
• Build complexity: Large enterprise codebases with complex build systems, multiple frameworks, and extensive use of code generation can be difficult to configure for accurate SAST analysis.

DAST: Dynamic Application Security Testing

Dynamic Application Security Testing takes the opposite approach from SAST. Instead of reading source code, DAST interacts with a running application from the outside — exactly the way an attacker would. It crawls the application to discover endpoints, injects attack payloads into every input it finds, and analyzes the responses to determine whether the application is vulnerable.

How DAST Works

A DAST scan proceeds through several distinct phases:

1. Discovery and crawling: The scanner starts from a set of seed URLs and systematically explores the application. It follows links, submits forms, discovers API endpoints, and builds a site map of all reachable input points. Modern DAST tools include JavaScript execution engines that can crawl single-page applications (SPAs) rendered client-side.
2. Attack surface mapping: For each discovered page or endpoint, the scanner catalogs all input vectors: URL parameters, form fields, JSON body properties, HTTP headers, cookies, file upload fields, and WebSocket messages. Each input vector becomes a test target.
3. Payload injection: The scanner sends carefully crafted payloads to each input vector. Payloads are organized by vulnerability type (SQL injection, XSS, command injection, path traversal, etc.) and database type (MySQL, PostgreSQL, SQL Server, Oracle). Each input may receive hundreds of different payloads.
4. Response analysis: The scanner compares each response to the baseline (the response received with a normal, non-malicious input). It looks for indicators of vulnerability: error messages containing database details, reflected payloads in HTML output, response time anomalies suggesting blind injection, status code changes, and content length differences.
5. Verification and confidence scoring: Advanced DAST tools perform secondary verification to reduce false positives. If a SQL injection is suspected based on an error message, the scanner may send a known-safe payload and a known-exploit payload to confirm the differential behavior.

What DAST Finds Best

• Server and infrastructure misconfigurations: Missing security headers (HSTS, CSP, X-Frame-Options), exposed admin panels, directory listing enabled, verbose error pages, default credentials on management interfaces
• Authentication and session management flaws: Weak session tokens, missing session expiration, session fixation, insecure cookie attributes (missing HttpOnly, Secure, SameSite flags)
• Runtime injection vulnerabilities: SQL injection, XSS, command injection, and other injection types that are actually exploitable in the deployed environment (not just theoretically vulnerable in code)
• TLS/SSL configuration issues: Weak cipher suites, expired certificates, missing HSTS, protocol downgrade vulnerabilities
• CORS misconfigurations: Overly permissive cross-origin resource sharing policies that allow unauthorized cross-domain access
• Business logic flaws: Price manipulation, privilege escalation through parameter tampering, IDOR (Insecure Direct Object Reference) vulnerabilities

Example: DAST Detecting Reflected XSS

Step 1: Crawl discovers search page
  GET /search?q=test HTTP/1.1
  Response: 200 OK
  Body contains: <p>Results for: test</p>

Step 2: Scanner identifies 'q' parameter reflects in response body
  Baseline response length: 4,832 bytes

Step 3: Inject XSS probe payload
  GET /search?q=<script>alert(1)</script> HTTP/1.1
  Response: 200 OK
  Body contains: <p>Results for: <script>alert(1)</script></p>

Step 4: Scanner detects unencoded script tag in response
  Finding: CWE-79 Reflected Cross-Site Scripting
  Confidence: HIGH (payload reflected verbatim without encoding)
  Input: q parameter on /search endpoint
  Evidence: Response body contains injected <script> tag

Step 5: Verification with alternate payload
  GET /search?q=<img src=x onerror=alert(1)> HTTP/1.1
  Response confirms: tag reflected without sanitization
  Finding CONFIRMED

DAST Strengths

• Technology agnostic: DAST does not care what language, framework, or architecture the application uses. It tests the HTTP interface, making it equally effective against Java, .NET, Python, Go, PHP, or any other technology stack.
• Finds real exploitability: Because DAST tests the running application, its findings represent actually exploitable vulnerabilities, not theoretical code-level possibilities. If DAST reports a SQL injection, that injection works against the deployed application with all its middleware, WAFs, and runtime protections in place.
• No source code required: DAST is ideal for testing third-party applications, commercial off-the-shelf (COTS) software, APIs, and legacy applications where source code is unavailable.
• Catches configuration issues: Server misconfigurations, missing headers, weak TLS, exposed endpoints, and other deployment-specific issues that SAST cannot see are directly observable by DAST.
• Low false positive rate: Well-tuned DAST tools typically have lower false positive rates than SAST because they verify exploitability empirically rather than theoretically.

DAST Limitations

• Limited code coverage: DAST can only test what it can reach through the application's interface. Dead code, error handlers, admin-only features behind complex authentication, and code paths triggered only by specific server-side events are invisible to DAST.
• No line-of-code guidance: DAST reports that an endpoint is vulnerable, but it cannot tell developers which file, function, or variable to fix. Remediation requires manual investigation to map the external finding to internal code.
• Requires a running environment: DAST needs a deployed, functional application with realistic data. Setting up and maintaining staging environments, test accounts, and seed data adds operational overhead.
• Scan duration: Thorough DAST scans of large applications can take hours or days. Comprehensive payload injection across hundreds of endpoints with thousands of parameters is inherently time-consuming.
• SPA and API challenges: While modern DAST tools have improved significantly, JavaScript-heavy single-page applications and complex API workflows with chained requests still present crawling challenges.
• Cannot find source-level issues: Hardcoded secrets, weak cryptography choices, insecure random number generation, and other code-quality security issues are invisible to DAST because they do not manifest as observable HTTP behavior differences.

IAST: Interactive Application Security Testing

Interactive Application Security Testing represents the newest of the three methodologies and attempts to combine the strengths of both SAST and DAST while minimizing their respective weaknesses. IAST works by instrumenting the application at runtime with lightweight agents that observe code execution from the inside as the application handles real or test traffic.

How IAST Works

IAST deploys an agent — typically a language-specific library or framework module — into the application's runtime environment. This agent hooks into key framework and language-level operations to monitor data as it flows through the application in real time:

1. Instrumentation: The IAST agent instruments critical points in the application runtime: HTTP request handling, database query execution, file system operations, command execution, cryptographic operations, serialization, and output encoding. In Java, this typically uses bytecode instrumentation via java.lang.instrument. In .NET, it hooks into the CLR profiling API. In Node.js and Python, it wraps key library functions.
2. Taint tracking at runtime: As the application processes a request, the IAST agent tracks every piece of data that originated from an untrusted source (HTTP parameters, headers, body content). Unlike SAST's static approximation, IAST observes the actual data flow through the actual code path executed at runtime. It sees which variables hold tainted data, which transformations are applied, and where the data ultimately ends up.
3. Sink monitoring: When tainted data reaches a security-sensitive operation (a SQL query, an HTML response, a file path, a system command), the agent evaluates whether adequate sanitization was applied. Because the agent observes the actual runtime state, it can see the concrete value of the data and the specific sanitization function that was (or was not) called.
4. Contextual analysis: IAST agents have access to the full execution context: the call stack, the HTTP request that triggered the code path, the framework's routing configuration, authentication state, and session data. This context dramatically reduces false positives because the agent can verify conditions that SAST can only guess at.

Taint Flow Analysis in Action

REQUEST: POST /api/users/search
Content-Type: application/json
Body: { "name": "O'Brien" }

IAST AGENT TRACE:
  [1] SOURCE: Request body parsed -> name = "O'Brien" [TAINTED]
  [2] PROPAGATE: controller.SearchUsers(name) called
  [3] PROPAGATE: name assigned to local variable 'searchTerm' [TAINTED]
  [4] PROPAGATE: string.Format("SELECT * FROM Users WHERE Name = '{0}'", searchTerm)
      Result: "SELECT * FROM Users WHERE Name = 'O'Brien'" [TAINTED]
  [5] SINK: SqlCommand.ExecuteReader(query) called with tainted query string
  [6] SANITIZER CHECK: No parameterization detected
      - Not using SqlParameter binding
      - Not using ORM parameterized method
      - No allowlist validation on 'searchTerm'

FINDING GENERATED:
  Type: SQL Injection (CWE-89)
  Confidence: CONFIRMED (tainted data observed reaching SQL sink)
  Source: HTTP POST body, field "name"
  Sink: SqlCommand.ExecuteReader() at UserController.cs:47
  Data flow: 5 steps traced through runtime execution
  Actual value at sink: "SELECT * FROM Users WHERE Name = 'O'Brien'"

Notice the critical difference from SAST: IAST observed the actual data value, the actual code path executed, and the actual absence of sanitization at runtime. It did not need to approximate whether a custom validation function was effective — it saw whether the function was called and what the data looked like afterward. And unlike DAST, IAST can report the exact source file and line number where the vulnerability exists.

What IAST Finds Best

• Injection vulnerabilities with high confidence: Because IAST sees both the tainted data and the sink simultaneously at runtime, injection findings are confirmed with near-zero false positive rates
• Data flow vulnerabilities across framework boundaries: IAST follows data through middleware, interceptors, filters, and framework internals that SAST may not model accurately
• Vulnerabilities triggered by specific data patterns: Some vulnerabilities only manifest with certain input shapes (e.g., Unicode handling bugs, encoding edge cases). IAST detects these when triggered by test traffic
• Missing security controls in context: IAST can verify whether CSRF tokens are actually validated (not just present), whether authentication checks are enforced on specific routes, and whether output encoding is applied at the point of rendering
• Cryptographic issues at runtime: IAST agents can observe actual cryptographic operations (key lengths, algorithms, modes of operation) as they execute, catching weak crypto that is configured dynamically or loaded from external sources

IAST Strengths

• Lowest false positive rate: Because findings are based on observed runtime behavior rather than static approximation, IAST typically achieves false positive rates below 5%. This dramatically reduces triage burden on development teams.
• Line-of-code accuracy: Like SAST, IAST can report the exact file, method, and line number where the vulnerability exists, because the agent has access to the runtime call stack and source mapping.
• Zero additional scan time: IAST analyzes the application passively as it handles normal test traffic. There is no separate scanning phase — security testing happens as a byproduct of functional testing, QA testing, or even production traffic monitoring.
• Works with existing test suites: Any test that exercises the application — unit tests, integration tests, end-to-end tests, manual QA testing — generates security findings from IAST. The better your test coverage, the better your IAST coverage.
• Real data flow confirmation: IAST does not guess whether data might flow from point A to point B. It observes that data did flow from A to B during actual execution. This eliminates an entire category of static analysis uncertainty.

IAST Limitations

• Language and framework dependency: IAST agents are language-specific. Each supported language requires a separate agent implementation that understands the runtime, framework internals, and standard library. Coverage across polyglot architectures requires multiple agents.
• Coverage depends on test execution: IAST only analyzes code paths that are actually executed during testing. If a code path is never triggered by any test case, IAST cannot analyze it. This makes IAST coverage directly proportional to functional test coverage.
• Performance overhead: Runtime instrumentation adds overhead to the application, typically between 2% and 10% in latency and 5% to 15% in memory usage. While this is acceptable in staging and QA environments, it may be unacceptable in high-performance production systems.
• Deployment complexity: Installing an IAST agent requires modifying the application's runtime configuration (JVM flags, .NET profiler environment variables, Python imports). In containerized environments, this means modifying Docker images or Kubernetes pod specs.
• Not suitable for pre-build analysis: Unlike SAST, IAST cannot analyze code that has not been compiled and deployed. It does not provide shift-left coverage at the IDE or commit level.
• Limited for serverless and microservices: Short-lived function invocations in serverless architectures may not provide enough execution time for the IAST agent to complete its analysis. Tracing data flows across microservice boundaries requires distributed tracing integration.

Head-to-Head Comparison: SAST vs DAST vs IAST

The following table compares the three methodologies across the criteria that matter most when selecting and implementing application security testing tools. No single approach wins across all dimensions — which is precisely why a combined strategy is necessary.

When to Use Each: A Decision Framework

The right testing strategy depends on your organization's SDLC maturity, team size, application architecture, and compliance requirements. Here is a practical decision framework for determining where each methodology delivers the most value.

Use SAST When...

• You need the earliest possible feedback. SAST is the only methodology that works before the application is compiled or deployed. For shift-left security programs that want developers to see vulnerabilities in their IDE or during code review, SAST is the enabling technology.
• You have large codebases with many contributors. When dozens or hundreds of developers commit code daily, automated SAST in the CI pipeline ensures that every change is analyzed, regardless of whether a human reviewer catches security issues.
• Compliance requires code-level analysis. Standards like PCI DSS 4.0 (Requirement 6.2.4) explicitly mandate static analysis of custom application code. SAST provides the audit trail and evidence needed for compliance.
• You are developing in strongly typed languages. SAST delivers its highest accuracy in Java, C#, Go, and TypeScript, where type information enables precise data flow tracking.
• You need to audit for hardcoded secrets and crypto weaknesses. These vulnerability classes are invisible to DAST and only partially visible to IAST. SAST is the primary detection mechanism.

Use DAST When...

• You need to verify real-world exploitability. Before a penetration test or a compliance assessment, DAST provides an automated first pass that identifies actually exploitable vulnerabilities in the deployed application.
• You are testing third-party or COTS applications. When you do not have source code, DAST is the only automated testing option. This includes commercial software, SaaS applications you host, and vendor-provided APIs.
• You want to assess deployment configuration. DAST is the only methodology that can verify that security headers are present, TLS is properly configured, admin interfaces are not exposed, and error pages do not leak information.
• You have a mature staging environment. DAST requires a running application that closely mirrors production. Organizations with automated deployment pipelines and realistic staging environments get the most value from DAST.
• Your application is a REST or GraphQL API. Modern DAST tools with API schema import (OpenAPI, GraphQL introspection) can efficiently test API endpoints that traditional crawling might miss.

Use IAST When...

• False positive fatigue is killing developer adoption. If your team has stopped looking at SAST results because too many findings are false positives, IAST's sub-5% false positive rate can rebuild trust in automated security findings.
• You already have strong functional test coverage. IAST leverages existing tests. If your QA team runs comprehensive integration tests or your CI pipeline includes end-to-end test suites, IAST transforms that test effort into security coverage at near-zero additional cost.
• You need runtime data flow confirmation. For high-risk applications (financial services, healthcare, government), IAST provides evidence that specific data flows are (or are not) vulnerable based on actual observed runtime behavior, not static approximation.
• You are running a DevSecOps pipeline with automated testing. IAST slots naturally into the test execution phase of CI/CD. Every automated test run produces security findings without adding scan time.
• Your application uses complex middleware or framework magic. Applications with extensive dependency injection, AOP (aspect-oriented programming), runtime proxies, or dynamic routing are difficult for SAST to model accurately. IAST observes the actual runtime behavior, bypassing these analysis challenges.

Decision Matrix by Context

The Combined Approach: Why You Need All Three

The most important insight from comparing SAST, DAST, and IAST is that their strengths and weaknesses are almost perfectly complementary. Where one methodology is blind, another has clear visibility. Where one generates noise, another provides confirmation. Relying on any single methodology creates predictable, exploitable gaps in your security posture.

Complementary Coverage Map

Consider how the three methodologies cover the OWASP Top 10 differently:

No column in this table shows "Very Strong" or "Strong" for every row. Each methodology has categories where it provides weak or only partial coverage. Only the combination of all three approaches covers all ten categories with strong or better detection capability.

The Correlation Advantage

When multiple testing methodologies identify the same vulnerability, the correlation provides significant advantages beyond mere duplication:

• Confidence multiplier: A SQL injection found by SAST (code-level taint flow), confirmed by DAST (exploitable with crafted payload), and validated by IAST (observed at runtime) is a finding that no one in the organization can dismiss as a false positive. The evidence is overwhelming and multi-dimensional.
• Prioritization signal: A vulnerability that SAST finds in code but DAST cannot exploit may have a lower practical risk than one that both SAST and DAST flag. Correlation data helps security teams prioritize remediation based on actual exploitability, not just theoretical severity.
• Coverage gap identification: If DAST finds a vulnerability that SAST missed, it indicates a gap in SAST's rules, language support, or framework modeling. If SAST finds a vulnerability that IAST did not see, it indicates a gap in test coverage. These gap signals drive continuous improvement of the overall testing program.

Integration into CI/CD Pipelines

Each testing methodology has a natural home in the software development lifecycle. Placing each tool at its optimal pipeline stage maximizes detection effectiveness while minimizing developer friction and pipeline latency.

Where Each Fits in the Pipeline

Stage 1: Code and Commit (SAST)

SAST operates at the earliest stage. It requires only source code and can run before compilation. The optimal integration points are:

• IDE plugin: Developers see warnings in real time as they write code. This is the fastest possible feedback loop but is advisory only, not a gate.
• Pre-commit hook: A lightweight SAST scan on changed files can block commits that introduce known-dangerous patterns. Keep this fast (under 30 seconds) to avoid disrupting developer workflow.
• CI build stage: A full SAST scan runs on every pull request. Results are posted as code review comments, and critical findings can block the merge. This is the primary SAST enforcement point.

# Example: SAST in CI Pipeline (GitHub Actions)
security-sast:
  stage: build
  runs-on: ubuntu-latest
  steps:
    - uses: actions/checkout@v4
    - name: Run SAST Analysis
      run: |
        sf365 scan --type sast \
          --source ./src \
          --languages csharp,javascript \
          --severity-threshold high \
          --fail-on-findings true
    - name: Upload Results
      if: always()
      run: sf365 report --format sarif --output sast-results.sarif

Stage 2: Test Execution (IAST)

IAST activates during the test execution phase. The IAST agent is deployed alongside the application when integration tests, end-to-end tests, or QA tests run. It produces findings as a byproduct of test execution, requiring no additional scan time.

• Integration test phase: When the CI pipeline deploys the application to a test environment and runs integration tests, the IAST agent monitors every request and reports vulnerabilities found during test execution.
• QA environment: Manual QA testing in a staging environment with the IAST agent installed generates findings from exploratory testing that automated tests may not cover.
• Performance test phase: Load tests exercise the application under stress, and the IAST agent observes whether security controls degrade under load (a finding class unique to IAST).

# Example: IAST during Integration Tests
security-iast:
  stage: test
  needs: [deploy-staging]
  steps:
    - name: Deploy with IAST Agent
      run: |
        # Add IAST agent to application runtime
        sf365 iast install --app-id $APP_ID \
          --environment staging \
          --runtime dotnet
    - name: Run Integration Tests
      run: dotnet test --filter Category=Integration
    - name: Run E2E Tests
      run: npx playwright test
    - name: Collect IAST Findings
      run: |
        sf365 iast results --app-id $APP_ID \
          --format sarif --output iast-results.sarif
          --fail-on-severity critical

Stage 3: Pre-Release Validation (DAST)

DAST runs against the deployed staging environment after all functional tests pass. It serves as the final security gate before production release.

• Staging environment scan: A comprehensive DAST scan against the staging deployment tests the application as it will appear in production, including all server configuration, middleware, and network-level controls.
• API contract testing: DAST tools import OpenAPI or GraphQL schemas to ensure complete API coverage, testing every endpoint and parameter combination defined in the specification.
• Scheduled production monitoring: Lightweight DAST scans can run against production on a recurring schedule (weekly or monthly) to detect configuration drift, newly exposed endpoints, or regressions introduced by infrastructure changes.

# Example: DAST Pre-Release Gate
security-dast:
  stage: pre-release
  needs: [integration-tests, security-iast]
  steps:
    - name: Run DAST Scan
      run: |
        sf365 scan --type dast \
          --target https://staging.example.com \
          --api-spec ./openapi.yaml \
          --auth-config ./dast-auth.json \
          --scan-profile full \
          --severity-threshold medium \
          --fail-on-findings true
    - name: Upload Results
      if: always()
      run: sf365 report --format sarif --output dast-results.sarif

Pipeline Summary

This layered approach means that a vulnerability must evade three independent detection mechanisms to reach production. SAST catches it in code. If it slips through SAST (perhaps due to a dynamic language analysis limitation), IAST catches it during testing. If it slips through IAST (perhaps because no test exercised that code path), DAST catches it in the deployed environment. The probability of a vulnerability evading all three is orders of magnitude lower than the probability of evading any single one.

How Security Factor 365 Combines All Three

Most organizations cobble together their application security testing from multiple vendors: one tool for SAST, another for DAST, a third for IAST, and additional tools for SCA, secrets scanning, and infrastructure-as-code analysis. This multi-vendor approach creates integration headaches, duplicate dashboards, inconsistent severity scoring, and finding correlation that ranges from manual to impossible.

Security Factor 365 takes a fundamentally different approach. The platform includes ten security scanning engines in a single, unified solution — and three of those engines are purpose-built SAST, DAST, and IAST analyzers that run in parallel and share a common finding model.

Unified Engine Architecture

When you connect a repository and trigger a scan in SF365, the platform does not run a single analysis. It runs all applicable engines simultaneously:

• SAST Engine: Performs taint analysis across all supported languages in the repository, mapping data flows from untrusted sources to dangerous sinks. Findings include exact file, line, and variable with a full data flow trace.
• DAST Engine: Deploys and scans the application dynamically, injecting payloads against every discovered input point. Findings include the exact request, response, and payload that demonstrated the vulnerability.
• IAST Engine: Instruments the application runtime during automated and manual testing, monitoring actual data flows in real time. Findings include both the runtime trace and the source code location.
• SCA Engine: Analyzes all dependencies for known CVEs, license compliance violations, and end-of-life components.
• Secrets Scanner: Detects hardcoded credentials, API keys, tokens, and certificates across the entire repository history.
• IaC Scanner: Analyzes Terraform, CloudFormation, Kubernetes manifests, and Dockerfiles for security misconfigurations.
• Configuration Auditor: Validates application and server configurations against security benchmarks (CIS, DISA STIG).
• API Security Analyzer: Tests API specifications and runtime behavior for OWASP API Top 10 vulnerabilities.
• Container Scanner: Scans container images for vulnerable base images, exposed ports, and privilege escalation vectors.
• Compliance Mapper: Maps all findings to regulatory frameworks (PCI DSS, HIPAA, SOC 2, ISO 27001) for automated compliance reporting.

Automatic Finding Correlation

The real power of a unified platform emerges when findings from multiple engines are correlated automatically. When SF365's SAST engine identifies a SQL injection in UserController.cs:47 and the DAST engine confirms the same vulnerability is exploitable at POST /api/users/search, the platform automatically merges these into a single, correlated finding with a confirmed status.

This correlation provides three critical benefits:

• Automatic false positive elimination: SAST findings that are not confirmed by DAST or IAST are flagged as lower priority. Findings confirmed by multiple engines are escalated automatically.
• Complete remediation context: Developers see the source code location (from SAST), the runtime data flow (from IAST), and the exploit proof (from DAST) in a single finding view. They do not need to cross-reference three different tools.
• Unified risk scoring: SF365's AI-powered scoring engine considers findings from all ten engines to produce a single Security Score for each application. This score reflects the holistic security posture, not just the perspective of one testing methodology.

AI-Powered Triage and Remediation

SF365's built-in AI Copilot analyzes correlated findings and generates specific, context-aware remediation guidance. Instead of generic advice like "use parameterized queries," the Copilot provides code-level fix suggestions that reference the exact vulnerable code, the framework being used, and the project's coding patterns. The AI triage agent also automatically classifies findings as true positives, false positives, or requires-investigation based on the correlation evidence from all active engines.