Automated Security Reviews: A Practical CI/CD Guide

In today’s fast paced development world, shipping code quickly is the name of the game. But speed can’t come at the cost of security. This is where automated security reviews come in, acting as your tireless, vigilant partner to catch vulnerabilities before they ever reach production. Instead of treating security as a final, manual gate, automation weaves it directly into the development lifecycle, making everyone’s job easier.

This guide breaks down everything you need to know about setting up and mastering automated security reviews, from integrating them into your pipeline to leveraging the latest in AI to find and fix flaws.

What is an Automated Security Review Feature?

At its core, an automated security review feature is a capability within a development platform that continuously scans code for risks. It operates with minimal manual effort, surfacing potential issues directly in pull requests (PRs) and enforcing security policies automatically. These systems can warn developers, suggest fixes, or even block risky merges.

The goal is to make security a seamless part of the developer workflow. This approach is strongly encouraged by standards like NIST’s Secure Software Development Framework (SSDF), which recommends integrating automated analysis to reduce vulnerabilities. Platforms like GitHub use this model, allowing multiple tools to feed into a single, unified view of a project’s security posture.

If your team also fields security questionnaires, here’s a quick primer on what a Trust Center is and why it matters.

Integrating Scans into Your CI/CD Pipeline

The true power of automated security reviews is unlocked when they are part of your Continuous Integration and Continuous Deployment (CI/CD) pipeline. This means every single commit and pull request gets analyzed, ensuring no code merges without a security checkup.

You can configure these scans to run on specific triggers, such as when a pull request is opened or when code is pushed to a main branch. By using branch protection rules, you can require that all security checks pass before a merge is allowed, making it a powerful gatekeeper for your codebase. Many platforms even offer a default setup that automatically enables scans for supported languages, making the initial configuration surprisingly simple.

Automated Pull Request Reviews with GitHub Actions

A popular way to implement this is through GitHub Actions. When a developer opens a pull request, a workflow can automatically trigger a code scan. The results then appear directly in the PR as checks, comments, or annotations.

To avoid overwhelming developers, these systems are smart about how they present findings. For instance, GitHub’s code scanning is optimized to only show alerts on the lines of code that have actually changed in the pull request, keeping the feedback highly relevant. For teams with very busy repositories, advanced configurations can ensure these checks run efficiently even in high throughput merge queues.

Pro tip: pair PR scanning with a buyer-facing Security Portal so customers can self-serve your security collateral and reduce follow-up questionnaires.

Inline Comments and Automated Fixes

Getting a report is one thing, but getting actionable feedback right where you work is another. Modern automated security reviews excel at this.

Inline Comments with Fix Recommendations

Tools can post comments directly on the line of code with a vulnerability, explaining the issue and often suggesting a fix. GitHub even allows developers to “Apply suggested changes” with a single click, batching multiple suggestions into one commit to streamline the process.

Automated Fix Implementation

Taking it a step further, some tools can automatically generate and apply the code changes needed to fix a vulnerability. This can be a deterministic, rule based fix or an AI generated suggestion.

• GitHub Copilot Autofix generates fixes for alerts from its CodeQL scanner, providing a natural language explanation alongside the code.

• Semgrep uses a deterministic “autofix” feature that has shown over 96% correctness in internal tests for Python.

• Dependabot automatically creates pull requests to update vulnerable dependencies, which can even be auto merged if they meet predefined policy conditions.

Getting Started: Installation and First Steps

Jumping into automated security reviews is easier than you might think. Most tools are built for quick adoption.

• CodeQL: GitHub offers a “default setup” option right in your repository settings for a fast start.

• Semgrep: Installation is a simple brew or pip command, followed by semgrep login and semgrep ci to get running.

• Snyk: After installing the CLI, you can run snyk code test to find vulnerabilities in your source code.

• Trivy: You can scan a local repository for vulnerabilities, misconfigurations, and secrets with a single command like trivy repo.

Running Scans from Your Terminal

For quick feedback without waiting on a CI pipeline, developers can run security analysis commands directly from their terminal. This local first approach helps catch issues early and is great for scripting. Common commands include:

• snyk code test: Scans your source code for vulnerabilities.

• semgrep scan: Runs local checks using Semgrep’s rules.

• gosec ./...: Scans Go code and can output results in a format compatible with GitHub.

• bandit -r PATH: Recursively finds common security issues in Python code.

Don’t stop at code scanning. Centralize evidence and FAQs in a Trust Center to cut inbound requests and speed up assessments.

Customizing Rules and Reducing False Positives

Out of the box, scanners can sometimes be noisy. The key to long term success with automated security reviews is tuning the rules to fit your team’s needs, which helps reduce false positives and maintain developer trust.

Most tools offer different levels of strictness. For example, CodeQL has a “default” suite focused on high precision alerts and a “security-extended” suite that offers broader coverage at the cost of more potential false positives. The best practice is to start with the high precision rules and expand coverage as your team gets comfortable with triaging alerts.

Recent research has even shown that AI, specifically LLMs, can be used to help adjudicate and filter out static analysis false positives with impressive accuracy.

Aligning Scans with Security Policies

Your automated security reviews should reflect your organization’s risk appetite and compliance requirements. This involves aligning scanner rules and severity levels with your internal security policies and external frameworks like CVSS or NIST.

You can configure protected branches in GitHub to require status checks to pass and conversations to be resolved before merging, which is a practical way to enforce your policies. The selection of a query suite, like CodeQL’s Default versus Extended, also acts as a sensitivity dial, allowing you to balance signal with noise according to your policy.

While engineering teams focus on code-level security, many organizations face another review challenge: customer security questionnaires. Targhee Security complements your internal efforts by automating responses and by providing a centralized Trust Center to accelerate sales without distracting engineers.

What Vulnerabilities Should You Cover?

A good automated security review setup should cover a wide range of common and critical vulnerability types. Key areas of focus include:

• SQL Injection and Cross Site Scripting (XSS): These remain top threats, with XSS and SQL injection ranking #1 and #3 respectively in the 2024 CWE Top 25 list.

• Authentication and Authorization Flaws: Broken Access Control was identified as the top risk in the OWASP Top 10 for 2021, covering issues where users can access things they shouldn’t.

• Insecure Data Handling: This includes everything from using weak cryptography to accidentally exposing sensitive data in plaintext, a category OWASP now calls “Cryptographic Failures”.

• Dependency Vulnerabilities: Your code is only as secure as its dependencies. A 2024 report found that 74% of codebases contained high risk vulnerabilities from open source components.

• Server Side Request Forgery (SSRF): This vulnerability allows an attacker to make the server perform requests on their behalf, often to internal resources. It was famously involved in the 2019 Capital One breach and remains a top concern.

Advanced Concepts in Automated Security Reviews

The field is constantly evolving. Here are a couple of advanced topics worth knowing about.

Agentic Code Scanning Architecture

The next frontier is agentic AI systems. Instead of just finding a flaw, these multi agent systems can autonomously plan and run security checks, triage findings, propose fixes, and even update pull requests. This approach blends the reasoning power of LLMs with traditional static and dynamic analysis tools. Early research is promising, with one multi agent system called “VulAgent” improving vulnerability detection accuracy by 6.6% and cutting false positives by nearly 36% compared to a single LLM.

Internal Validation on a Real Codebase

How do you know if your automated security review process is actually effective? By testing it against real world, complex vulnerabilities. This means validating your scanners and response playbooks against bugs like DNS rebinding, which can bypass network policies to reach internal services. Running these drills helps ensure your automated defenses work as expected when faced with a sophisticated threat.

Best Practices for Success

Ready to roll out automated security reviews? Here’s a simple checklist to guide you:

1. Start with the Basics: Enable default PR scanning first. Get your team comfortable with the process before expanding.

2. Unify Your Tooling: Use tools that can output results in a standard format like SARIF. This gives developers a consistent experience across all your scanners.

3. Protect Your Branches: Use required status checks to prevent risky code from being merged.

4. Human in the Loop for Fixes: For AI generated fixes, always require a human developer to review and approve the changes before merging.

5. Validate and Tune: Regularly test your setup against real vulnerability patterns and use the results to tune your rules and reduce noise.

On the customer-review side, you can also simplify your security questionnaire process to reduce back-and-forth.

Automating engineering reviews is a huge step forward for security. But don’t forget the other side of the coin, security reviews from customers and partners. For SaaS companies managing SOC 2 or ISO 27001 compliance, the constant barrage of security questionnaires can be a major drag on sales and engineering. Targhee Security helps by publishing your security docs in a Trust Center and using AI to pre-draft answers with its Security Questionnaire Automation, so your team can stay focused on building.

Frequently Asked Questions

What are automated security reviews?
Automated security reviews are processes that use specialized tools to automatically scan source code for security vulnerabilities within the software development lifecycle, typically integrated into CI/CD pipelines and pull requests.

Why are automated security reviews important?
They help teams find and fix security flaws early in the development process, which is far cheaper and more effective than finding them in production. This practice improves security posture without slowing down development velocity.

How do I get started with automated security reviews?
A great starting point is to enable the default code scanning feature in your source code management platform, like GitHub Code Scanning. From there, you can explore other tools like Semgrep or Snyk and integrate them into your GitHub Actions or other CI/CD workflows. For customer-facing reviews, stand up a Trust Center and use AI Questionnaire Automation to draft accurate responses from your docs.

Can these tools automatically fix vulnerabilities?
Yes, many modern tools offer automated fix implementations. This can range from automatically creating a pull request to update a vulnerable dependency (like Dependabot) to using AI to generate and suggest a code patch for a detected vulnerability (like GitHub Copilot Autofix).

How do you handle false positives in automated security reviews?
Handling false positives involves tuning the scanner’s ruleset. You can often start with a high precision set of rules and gradually expand coverage. Most tools also allow you to suppress or mark specific findings as false positives to prevent them from appearing in future scans.

What types of vulnerabilities can be found?
Automated tools can detect a wide range of issues, including common web application flaws like SQL injection and cross site scripting (XSS), insecure data handling, broken access control, and vulnerabilities in open source dependencies.

Do automated security reviews replace manual penetration testing?
No, they are complementary. Automated tools are excellent for catching a broad range of common vulnerabilities quickly and consistently. Manual penetration testing provides deep, creative analysis by a security expert to find complex business logic flaws and other issues that automated tools might miss.

How can I align security scans with my company’s policies?
You can configure the tools to match your organization’s risk tolerance. This includes customizing which rules to run, setting severity thresholds for what constitutes a build failure, and integrating the checks as required steps in your branch protection rules.