There’s no doubt that data breaches are expensive, with the global average cost reaching US$4.4M according to IBM’s report. The cost rises if vulnerabilities are only fixed after production, yet teams continue to rely almost entirely on automated tools and see security reviews as a last-minute step before release.
Secure code review should be done as early as possible in the software lifecycle to help teams catch vulnerabilities when they’re less costly and easier to fix. There’s no doubt that automated tools are beneficial, but they should be combined with human expertise.
What Is Secure Code Review?
Secure code review is the practice of manually examining application source code to identify security vulnerabilities and weaknesses.
As a formal process within the secure development lifecycle, it aims to ensure secure coding practices and to thoroughly examine code for vulnerabilities.
It’s different from a general code review, which focuses more on functionality. When developers perform secure code review, they are specifically looking for security vulnerabilities, such as:
-
Injection flaws
-
Broken authentication and session handling
-
Missing or incorrect authorization checks
-
Sensitive data exposure
-
Security misconfiguration
It’s true that automated tools, such as static analysis, are helpful for scanning known patterns, but secure code review should still use human insight to understand context and assess risk. That’s because there are subtle security issues that tools can’t find, such as vulnerabilities tied to logic, design, or intended behavior.
Why Secure Code Review Is Vital
If there’s a vulnerability caught during code review, it might take an hour to fix, but that same issue in production could take much longer. Why? It might require emergency patches, security incident response, customer notifications, and potential regulatory reporting, all of which could cost thousands of times more.
Aside from cost, secure code review has several other benefits:
-
Reduced breach risk: If security issues are identified and fixed before deployment, then the attack surface and overall security risks are reduced. Potential attack pathways are removed as each vulnerability is mitigated.
-
Fewer false positives: SAST tools often report issues that aren’t really exploitable. In these cases, it’s easier for human reviewers to distinguish real vulnerabilities from tool noise, saving time and preventing alert fatigue.
-
Knowledge transfer: When experienced developers review code with security in mind, they can then teach secure coding practices to the entire team. Developers can then begin to see vulnerability patterns and write safer code by default.
-
Improved code quality: Secure code reviews also catch logic errors, performance issues, and design problems that affect long-term code maintenance.
-
Compliance requirements: Many frameworks and regulations require secure development practices, including code review. Standards such as PCI DSS and HIPAA specifically reference secure code review as part of the SDLC.
Typical Security Issues Found During Code Review
Vulnerabilities can exist anywhere in the entire codebase, which is why secure code review typically combines broad automated coverage with focused manual review of high-risk areas. These include vulnerability categories such as:
Injection Vulnerabilities
These remain the most common and dangerous security flaws, and include SQL injection, command injection, LDAP injection, and XML injection.
They all come from user input that hasn’t been validated or sanitized, and is then passed directly into a database query or a system command.
Reviewers look for dynamic query construction, string concatenation in database calls, and missing input validation. There are also path-traversal vulnerabilities that can occur when user input constructs file paths without proper validation.
Authentication and Session Management Issues
If applications use weak passwords, store credentials insecurely, generate predictable session tokens, don’t expire sessions, or use incomplete logout logic, the system is more likely to be compromised.
Broken Access Control
Users who can access resources or perform actions they shouldn’t be authorized for lead to broken access control. In this case, reviewers tend to check for missing authorization checks and insecure direct object references, as well as privilege escalation vulnerabilities.
Sensitive Data Exposure
This happens when applications fail to properly protect confidential information. It includes managing sensitive data without encryption, storing passwords in plain text, or leaking API keys or tokens in client-side code, and logging sensitive information.
Security Misconfiguration
Misconfigurations remain a priority and are listed second in the OWASP Top 10. They show up in all sorts of ways, whether it’s leaving default credentials active or leaking internal details through error messages, both of which attackers can exploit.
Using Data Flow Analysis During Code Review
Reviewers can identify vulnerabilities through data flow analysis, which is an important part of secure code review. It looks at how data flows through an application to catch potential security vulnerabilities.
Essentially, it traces data from input through storage and transmission, making sure that sensitive data is properly validated and not exposed to unauthorized parties.
Through data flow analysis, reviewers can identify vulnerabilities such as SQL injection and cross-site scripting (XSS), as well as weaknesses in data protection methods. Code reviewers can catch potential security issues early by examining how user input is handled and stored, and then transmitted.
Secure Code Review in Pull Requests
For many development teams, secure code review takes place within pull requests. The reason is that reviews integrated into continuous integration pipelines help detect security issues early.
Pull requests provide a convenient checkpoint for security because they combine code changes, discussion, and shared context in one place. Security questions that are asked during review are easier to address before the code is merged.
It works best when:
-
Pull requests are small enough to review meaningfully.
-
High-risk changes are clearly identified or labeled.
-
Security questions are seen as part of normal review, not obstacles.
Some teams reinforce this process by automating security checks in pull requests, so developers see relevant guidance during reviews (rather than after code is deployed).
Automated vs. Manual Secure Code Review
When it comes to automated or manual secure code reviews, it’s not a question of which approach is best, but rather how to combine them.
Automated
The main benefit of automated static analysis tools is that they can scan large codebases much more quickly, and they also integrate well into CI/CD pipelines. They look for vulnerability patterns such as hardcoded secrets, unsafe query construction, and missing input validation.
However, automated tools also have well-known limitations, such as generating false positives that require manual triage, struggling with business logic and authorization flaws, and lacking the context needed to properly assess impact.
Manual
Manual secure code review adds human insight to the process, because experienced reviewers better understand context and assess risk based on how the code is being used. Developers can also find subtle logic flaws and race conditions, and see whether automated tool findings are real security issues or false alarms.
It’s best to use both processes as automated tools provide broad coverage and catch obvious issues, whereas manual review focuses on high-risk code, complex logic, authentication and authorization, and triaging automated findings.
AI-Assisted Secure Code Review
AI-powered secure code review builds on traditional automated tools by analyzing code context and patterns to help developers identify potential vulnerabilities.
These days, AI-assisted tools are being used during secure code review embedded in IDEs or integrated into pull request processes. AI can perform an initial scan during review, so developers can focus their attention on areas more likely to introduce security risks rather than manually scanning every change.
However, AI-powered secure code review has limitations and should not be treated as a replacement for human review:
-
Limited understanding of the application context: AI tools typically lack awareness of business logic and developer intent, which can lead to risks being missed or not properly assessed.
-
Difficulty identifying design and architectural flaws: Issues such as insecure workflows, broken trust boundaries, or unsafe integrations often sit above the code level and are hard to detect through automated analysis alone.
-
False positives and false negatives: Automated findings still require human validation to determine whether an issue is exploitable and how it impacts the application in practice.
-
Reduced effectiveness with custom or uncommon stacks: The further the code deviates from well-known frameworks and patterns, the less reliable the AI-based analysis tends to be.
If it’s used correctly, AI can make secure code review faster and more scalable. However, security decisions still depend on developers’ understanding of how vulnerabilities arise and on their ability to see risk in the context of the system as a whole.
Building a Secure Code Review Process
To work in practice, secure code review needs structure and training. Start by defining when security-focused reviews are required. Be sure to include common security vulnerabilities and best security practices to enhance code security.
Many teams do security reviews before changes are merged to the main branch, focusing on:
-
Authentication and authorization code.
-
Features that manage sensitive data.
-
Changes to security controls.
-
Third-party integrations and new features.
Next, create a security-focused review list based on the team’s tech stack and vulnerability patterns, including:
-
Input validation and output encoding.
-
Authorization and access control.
-
Sensitive data handling.
-
Error handling and logging.
-
Cryptography usage.
-
Third-party dependencies.
It’s always a good idea to invest in developer security training. Developers need to understand common vulnerability classes and how to identify security issues in code. Apart from that, they also need to know when automated tool findings are false positives.
Creating a Security-First Development Culture
For secure code review programs to succeed, organizations should try to embed security awareness into their development culture.
Developers should be trained to think like attackers, ideally through threat modeling in the design stage of the SDLC. Then they need to learn to write secure code so that it can’t be exploited. An environment that welcomes security questions rather than dismisses them as obstacles is most beneficial.
It’s ideal to give developers hands-on practice identifying real vulnerabilities in code, which is much more effective than only reading about vulnerabilities.
Measuring Secure Code Review Effectiveness
It’s also worth tracking a few metrics to see if your process is working:
-
Monitor the number of security issues found during code review compared to those found in later stages. A worthwhile review process should catch most issues before code reaches QA or production.
-
Measure the time to resolve security findings, because as your team’s skills improve, they should identify and fix issues more quickly.
-
Track false positive rates from automated tools to reduce time wasted investigating non-issues.
-
Survey developers about their security skills. Teams that regularly conduct security-focused code reviews should show increasing confidence in their ability to identify and fix security issues.
Documenting past code review findings can help prevent the same mistakes from happening again and improve the overall security posture.
Challenges in Secure Code Review
The most well-intentioned secure code reviews can still not work properly if there are mistakes, such as:
-
Relying only on automated tools.
-
Seeing security review as something done at the end of development.
-
Expecting developers to conduct security reviews without training.
-
Reviewing all code equally instead of focusing on high-risk areas.
Train Your Team for More Effective Secure Code Reviews
SecureFlag’s Code Review Labs provide developers with hands-on practice identifying real vulnerabilities in code. They learn to identify genuine security issues, including filtering out false positives, using the tools they are already familiar with.
For teams with specific coding patterns or security concerns, SecureFlag’s Custom Labs allow you to create private training based on your own code and risk profile.
If you want secure code review to be faster and more accurate, practical training is what makes the difference.
