What is code auditing?
Code auditing is the systematic examination of source code to identify security vulnerabilities, bugs, performance issues, and compliance violations. This means security experts and automated tools analyze your code to find problems before attackers can exploit them.
A code security audit goes deeper than regular code reviews. While typical reviews focus on whether code works correctly and follows style guidelines, security audits specifically hunt for ways attackers could break into your systems or steal data.
Here's how code auditing differs from related security practices:
| Practice | Primary Goal | Timing | Methods | Typical Findings |
|---|---|---|---|---|
| Code Review | Verify correctness, maintainability, style | Every commit/PR | Peer review, linting | Logic errors, style violations, performance issues |
| Code Audit | Identify security flaws and systemic risks | Pre-release, quarterly | SAST, manual review, threat modeling | SQL injection, broken authentication, insecure design |
| Penetration Test | Validate exploitability in running systems | Post-deployment | Black-box testing, exploit attempts | Configuration flaws, runtime vulnerabilities, access control bypasses |
Secure Coding Cheat Sheet
Want to build security into your code from the start? This cheat sheet provides quick reference patterns for preventing common vulnerabilities.
Code reviews happen continuously during development. Code audits occur before major releases or after significant changes. Penetration tests validate security in production-like environments after deployment.
The audit examines everything your code touches. This includes the code itself, all the libraries and packages it depends on, configuration files, and how it will actually run in production. For modern applications, this means checking infrastructure as code templates, container setups, API endpoints, and third-party components.
Code auditing evaluates your security decisions. Auditors look at how you handle authentication, process user data, validate inputs, and manage errors. They're searching for weak spots in your defenses before hackers find them first.
Why code auditing matters in cloud-native environments
Cloud-native applications introduce unique security challenges that traditional auditing can’t fully address. Containers, serverless functions, and ephemeral infrastructure change constantly, creating a large and dynamic attack surface.
Code interacts with numerous cloud services, APIs, and microservices, so a single misconfiguration—like an overly permissive IAM role or unencrypted storage—can expose critical data. With teams deploying multiple times per day, vulnerabilities can reach production within hours if not caught early.
To stay secure, auditing must be integrated into development pipelines. By combining automated scanning, manual review, and context-aware tools, organizations can detect and remediate risks continuously, ensuring security keeps pace with rapid cloud-native development.
Types of code auditing
Different auditing approaches serve different purposes in your security strategy. Understanding each type helps you build comprehensive protection.
Manual code auditing means security experts review your code line by line. Humans excel at spotting complex vulnerabilities that tools miss, like business logic flaws or sophisticated attack patterns that require understanding context and intent.
Automated code auditing uses specialized tools to scan large codebases quickly. These tools identify common vulnerability patterns, insecure coding practices, and known issues in your dependencies. Automation is essential when you're shipping code multiple times per day.
Hybrid code auditing combines both approaches. Automated tools do the initial heavy lifting, then security experts investigate and validate the findings. This approach gives you broad coverage without overwhelming your security team.
Security-focused code auditing specifically targets security vulnerabilities and compliance violations. Auditors examine authentication mechanisms, encryption implementations, and data handling procedures. This requires deep security expertise and knowledge of current threats.
Compliance-focused auditing helps demonstrate alignment with requirements in frameworks like PCI DSS, HIPAA, ISO 27001, or SOC 2 by validating secure SDLC practices. For example:
PCI DSS Requirement 6.5 mandates secure coding practices to prevent common vulnerabilities like injection and broken authentication—satisfied through SAST scanning and manual code review
ISO/IEC 27001 Control A.8.28 requires secure coding principles in software development—demonstrated through documented code review processes and security testing
SOC 2 CC8.1 (Change Management) requires security testing before production deployment—fulfilled through automated scanning in CI/CD pipelines
NIST SSDF (SP 800-218) Practice PW.8 requires identifying and confirming vulnerabilities in software components—achieved through SCA and SBOM generation
HIPAA 164.312(a)(1) requires technical safeguards to protect ePHI—validated through code audits that verify encryption, access controls, and audit logging
This validates your development processes and security controls rather than certifying the code itself.
The code auditing process
Code auditing follows a structured approach to ensure nothing gets missed. Each step builds on the previous one to create thorough security coverage.
You start with preparation and planning. This means defining what you're auditing, setting clear objectives, and deciding what success looks like. Teams identify which codebases need auditing and prioritize based on risk.
Next comes code analysis and review. Auditors use both automated tools and manual techniques to examine your code. They analyze application logic, review security controls, and evaluate infrastructure configurations to find vulnerabilities.
Documentation review examines your technical specs, API documentation, and architecture diagrams. Auditors compare what you intended to build with what actually exists, looking for gaps that could indicate security problems. In parallel, perform threat modeling and secure design reviews using frameworks like STRIDE or PASTA to identify abuse cases, trust boundaries, and systemic risks that code scanning alone can't reveal. For example, threat modeling might uncover that your microservices architecture lacks authentication between internal services, creating lateral movement risks.
Reporting findings consolidates everything discovered into prioritized, actionable reports. Each finding includes severity ratings, exploitation scenarios, and specific remediation guidance so developers know exactly what to fix.
Review meetings bring security teams and developers together to discuss findings. These sessions clarify remediation approaches, establish timelines, and ensure everyone understands what needs to happen next.
Implementing changes is when developers actually fix the vulnerabilities. This includes modifying code, updating configurations, and implementing new security controls based on the audit recommendations.
Follow-up auditing validates that fixes worked. Auditors verify that remediation efforts successfully addressed issues without introducing new vulnerabilities, closing the loop on the entire process.
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Code audit checklist: 12 steps to comprehensive security
Use this checklist to ensure your code audits cover all critical security areas:
Planning & Scope (Steps 1-3)
Define audit scope – Identify applications, repositories, and infrastructure components to audit based on business criticality and data sensitivity
Perform threat modeling – Use STRIDE or PASTA frameworks to identify abuse cases, trust boundaries, and attack vectors specific to your architecture
Establish success criteria – Set measurable goals like "zero critical vulnerabilities in production code" or "all secrets removed from repositories"
Automated Scanning (Steps 4-7)
Run SAST and SCA scans – Analyze source code for vulnerabilities and inventory all dependencies with known CVEs
Scan IaC templates – Check Terraform, CloudFormation, and Kubernetes manifests for misconfigurations like overly permissive IAM or unencrypted storage
Perform container image scanning – Analyze base images and layers for OS vulnerabilities and malware
Execute secret scanning – Search repositories, commit history, and configuration files for hardcoded credentials and API keys
Manual Review & Validation (Steps 8-10)
Conduct high-risk manual review – Focus human expertise on authentication logic, authorization checks, cryptographic implementations, and business-critical workflows
Validate dependency security – Review third-party library licenses, maintainer activity, and upgrade paths for components with known vulnerabilities
Implement policy-as-code gates – Enforce security standards using OPA or similar tools to block non-compliant deployments automatically
Remediation & Evidence (Steps 11-12)
Fix and verify issues – Remediate findings by severity, validate fixes with re-scans, and track MTTR (Mean Time To Remediate) metrics
Capture audit evidence – Generate SBOMs, store build attestations, document findings and fixes, and maintain compliance artifacts for frameworks like SOC 2 or ISO 27001
Track key metrics: percentage of critical findings past SLA, MTTR by severity, and scan coverage across repositories.
CI/CD Cheat Sheet
Need to integrate security checks into your pipeline without slowing down releases? This guide covers practical patterns for embedding code auditing into CI/CD workflows.
Code auditing techniques and tools
Modern code auditing uses multiple techniques to catch vulnerabilities across your entire application stack. Each technique addresses specific security concerns.
Static Application Security Testing (SAST) analyzes source code without running it. SAST tools examine code structure, identify insecure patterns, and trace data flows to find injection vulnerabilities and other security issues.
Dynamic Application Security Testing (DAST) tests running applications by simulating attacks. This black-box approach identifies runtime vulnerabilities, authentication issues, and configuration problems that only show up during execution.
Software Composition Analysis (SCA) examines third-party libraries and dependencies for known vulnerabilities. Modern applications use hundreds of open-source components, making SCA essential for managing supply chain security risks.
Infrastructure as Code scanning analyzes IaC templates for security misconfigurations before deployment. These tools understand cloud provider-specific constructs and identify issues like overly permissive access policies or unencrypted storage.
Container image scanning examines container images for vulnerabilities in base images, application dependencies, and system packages. This is crucial for containerized applications where vulnerable images can affect multiple deployments.
Secret scanning identifies hardcoded credentials, API keys, and sensitive information in code repositories. This prevents accidental exposure of authentication credentials that could give attackers privileged access.
Policy as code enforces security and compliance rules across IaC, CI/CD pipelines, and cloud configurations using version-controlled, testable policies. Tools like Open Policy Agent (OPA) with Rego policy language let you define rules such as 'all S3 buckets must have encryption enabled' or 'container images must come from approved registries.' These policies run automatically in your pipelines, blocking deployments that violate standards before they reach production.
Dependency analysis maps relationships across first-party and third-party software components, including transitive dependencies, to assess supply chain risk. Tools generate Software Bills of Materials (SBOMs) that inventory all components and their known vulnerabilities. Separately, architecture and service dependency mapping shows how vulnerable components could impact connected services and data—for example, tracing how a vulnerable logging library in a payment service could expose customer data through API connections to the customer database.
Unified, context-aware risk view connects code findings to runtime infrastructure, identities, and data to prioritize based on actual attack paths. Traditional tools generate isolated alerts—SAST finds a SQL injection, SCA flags a vulnerable library, IaC scanning detects an overly permissive IAM role—but don't show how these risks combine. A context-aware platform correlates findings across domains: for example, showing that a deserialization vulnerability exists in a service that's internet-exposed, runs with admin privileges, and connects to a customer database. This transforms thousands of isolated findings into a prioritized list of exploitable attack paths, cutting noise by 90% and focusing teams on risks that actually matter.
Common findings in code audits
Code audits consistently uncover high-impact security issues across applications and infrastructure. Common findings include:
Authentication & Authorization Flaws: Weak passwords, missing multi-factor authentication, broken session management, or privilege escalation risks.
Injection Vulnerabilities: SQL, command, LDAP, or NoSQL injection where unvalidated input allows unauthorized access or code execution.
Insecure Data Handling: Hardcoded secrets, unencrypted data, or excessive API data exposure.
Infrastructure Misconfigurations: Overly permissive IAM roles, publicly accessible storage, unsecured databases, and missing network segmentation.
Dependency Vulnerabilities: Outdated libraries with known CVEs, abandoned packages, or license compliance issues.
API Security Issues: Missing rate limiting, broken object-level authorization, or insufficient input validation.
Insecure Deserialization: Unsafe handling of user-controlled data leading to remote code execution.
Server-Side Request Forgery (SSRF): Applications that make unsafe HTTP requests based on user input, exposing internal services.
Prioritize remediation based on exploitability, data sensitivity, and exposure rather than treating all findings equally. This ensures critical risks are fixed first while maintaining development velocity.
Common code auditing challenges and solutions
Organizations face several obstacles when implementing code auditing programs. Understanding these challenges helps you build more effective security practices.
Alert fatigue happens when teams receive thousands of security findings without clear prioritization. The solution is implementing risk-based filtering that considers exploitability, exposure, and business impact to surface only actionable issues that warrant immediate attention.
False positives waste time investigating non-issues and erode trust in security tools. Address this by tuning scanning rules to match your environment, implementing validation workflows to confirm findings, and maintaining feedback loops to improve detection accuracy over time. For example, configure SAST tools to understand your framework-specific security controls so they don't flag protected endpoints as vulnerable.
Tool sprawl creates operational overhead when you deploy multiple disconnected security tools—separate scanners for SAST, SCA, IaC, containers, and cloud resources, each with its own console, policies, and alert formats. This forces teams to maintain duplicate policies: defining 'no public S3 buckets' in your IaC scanner, cloud security tool, and compliance checker.
A single policy engine across code, CI/CD, and cloud avoids this sprawl by enforcing consistent standards throughout the development lifecycle. For example, define one policy that 'all data stores must use encryption at rest' and enforce it across:
IaC templates (Terraform, CloudFormation) before deployment
Container configurations (Kubernetes manifests) in CI/CD pipelines
Running cloud resources (RDS, S3, EBS) in production
Application code that creates or configures storage
This prevents configuration drift, reduces policy maintenance overhead by 70%, and ensures security standards remain consistent as applications evolve from code to cloud.
Lack of security expertise among developers can slow remediation efforts. Bridge this gap by providing security training, creating secure coding guidelines, offering pre-approved code patterns, and embedding security champions within development teams.
Legacy code technical debt accumulates when older applications lack modern security controls. Approach this systematically by prioritizing high-risk components, implementing compensating controls where code changes aren't feasible, and gradually modernizing critical systems.
Rapid development pace can make comprehensive auditing seem impossible. Adapt by automating routine checks, focusing manual review on high-risk changes, and implementing incremental security improvements rather than demanding perfection.
Cross-team coordination challenges arise when security, development, and operations teams work in silos. Foster collaboration through shared tools, common metrics, regular security reviews, and aligned incentives that reward secure development practices.
How Wiz Code connects code security to cloud risk
Wiz Code transforms traditional code auditing by connecting security findings directly to their potential impact in cloud environments. This approach moves beyond isolated vulnerability detection to provide contextualized risk assessment that reflects real-world attack scenarios.
The platform scans source code, infrastructure as code templates, and CI/CD pipelines to identify vulnerabilities, secrets, and misconfigurations before they reach production. Unlike traditional tools that generate lists of findings without context, Wiz correlates these issues with runtime data to show which vulnerabilities actually create exploitable attack paths.
The Wiz Security Graph maps relationships between code repositories, build pipelines, container images, and running workloads. This comprehensive view reveals how a vulnerability in source code could enable an attacker to move laterally through cloud infrastructure or access sensitive data.
Wiz provides bidirectional traceability between code and cloud, real-time security feedback in IDEs and pull requests, unified policy enforcement across your entire development lifecycle, and hardened base images that cut vulnerability noise by 95%. Book a demo today!
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