An Overview of the 
Implementation of 
SAST Tools on AWS

Did you know that only 30% of developers have rated its application security tool a 9 or a solid 10? The other 66% said its existing security tools could only protect 75% of its codebase. What we described are the key findings from a recent ESG Modern Application Development Security Survey. But if you think that these security loopholes are due to a lack of consulting or technological integrations, then you are mistaken. There are over 3500 security partners. What is seriously lacking is the integration of DevSecOps culture into the CI/CD process. To make this DevSecOps process effective and sustainable, you need to integrate the SAST tools. Adopting SAST tools is a crucial part of the early stage of DevSecOps for enabling consistency, efficiency, consistency, and quick detection. After all, the key to maximizing business value while reducing security risks lies in determining security protocols and collaborating with the development team.

Why is a SAST tool Suited for Developers?

It's not very uncommon to hear developers say things like “Why is debugging taking time?” “The scanning is so slow! This is why developer-first security tools came into being. At its core, it's about empowering developers. In this new concept, the developers oversee implementing security tools so that activities like security scanning, testing, and remediation can take place in the Dev's integrated development environment (IDE).

A SAST Tool takes care of the following things:

• Conduct assessment to resolve complex security challenges
• Automate security in the CI/CD pipeline
• Enable fast automated scans

Integrating SAST Tools with AWS Environment: A Quick Guide

Normally security testing is conducted towards the final stage of the Software Development Lifecycle (SDLC). This is why DevSecOps came into being as a shared responsibility between both security and development teams. SAST is an integral component of the SDLC. Interestingly, development teams require SAST tools to configure seamlessly into their DevOps infrastructure. Leveraging Amazon CodeGuru Reviewer Command Line Interface (CLI) can help in embedding CodeGuru Reviewer into your Continuous Integration and Continuous Delivery Pipeline. You can integrate this AWS tool at any part of the SDLC for whitebox testing. For instance, CodeGuru Reviewer CLI can be run on a Dev machine before establishing code.

CodeGuru Reviewer leverages Machine Learning to detect security flaws, poor usage of AWS SDKs and APIs and common coding anomalies. We can enable native integration with Source Code Management (SCM) systems such as AWS CodeCommit, GitHub, and BitBucket.

Amazon CloudWatch gathers data from databases to facilitate auto-scaling actions. This helps DevOps experts assess and debug the production of applications.

By enabling Static Code Analysis rules, the SonarQube SAST Tool identifies vulnerabilities and bugs in apps. PHPStan detects mistakes in code before running it. With OWASP Zap DAST tool, it automates identifying security threats in your applications when you develop or test them.

Integrating Snyk cloud can help in identifying vulnerabilities in applications that deploy open-source, container, and serverless solutions. For enhanced security and agility, Kubseal can be integrated into HashiCorp and AWS KMS.

Demystifying Opensource SAST Tools for Cloud-Native Applications

Typically, an average Cloud Native Application Stack is made of

• REST and GraphQL APIs
• Infrastructure as Code
• Configuration Code
• Microservices in different languages such as PHP, Golang, and Python

For building cloud-native applications, you need at most 5 SAST tools to review each code or artifact. However, this section will focus more on three leading SAST tools.

SonarQube

Being an open-source platform, SonarQube analyzes the source code quality. Performing code quality analysis enhances the reliability and readability of the code. SonarQube takes in files and assesses them on multiple factors such as bugs, coding, standards, code segments, complexity, and comments. After the assessment, the platform stores it in the form of metrics in a database and reflects them in a dashboard.

Image source: linkedin/mosuleiman

Powering SonarQube with AI/ML can offer the following benefits:

• Creating Custom Rules with Machine Learning: In traditional static code analysis, it becomes challenging to find security issues in the code. For this reason, making custom rules in SonarQube can help in leveraging machine learning models to detect specific patterns or anomalies in the code.
• Embedding External AI/ML Tools: If developers are relying on external AI/ML tools for code analysis, they can integrate the findings into SonarQube. The insights offered by these third-party tools can further improve and enhance the threat and vulnerability detection processes within a code.
• Enhancing Issue Prioritization: Not every security issue is critical. What we mean by this is that it's important to prioritize security issues that SonarQube identifies. Implementing an ML model can define the priority levels of each security issue based on the impact on system behavior, plausibility of causing security breaches, and frequency of occurrence.
• Enabling Predictive Analysis: Based on the data gathered by SonarQube, ML can predict potential issues or vulnerabilities within the code.
• Code Completion and Recommendations: Leveraging AI/ML models can generate code completion suggestions and recommendations.

Snyk

Snyk offers real-time insights into security vulnerabilities within the code along with suggesting remediation measures for threat mitigation. By integrating AI/ML, Snyk can help in:

• Monitoring Social and Community Channels: Utilizing AI helps Snyk monitor social and community forums to detect any future security threats.
• Natural Language Processing (NLP) for Vulnerability Detection: Through NLP techniques, developers can identify security flaws and loopholes in frameworks and open-source packages.
• Semantic Code Analysis: By offering critical information about data flaws and remediation of similar security issues, AI helps Snyx with quick and thorough semantic code analysis.
• Developing a SAST Knowledge Base: Using ML models can help in creating Snyx's SAST Knowledge Base by leveraging information from multiple open-source repositories per language.
• Snyk Cloud for Security Posture Modeling: With Snyk Cloud gathering real-time data sources, developers can build models to strengthen the security posture of an application right from the original code to the cloud.

Sealed Secrets with Kubeseal

As a Kubernetes tool for managing secrets in GitOps format, Sealed Secrets assists in the secured distribution and storage of highly sensitive data. This includes passwords, certificates, and API Keys. When compared to conventional Kubernetes Secrets, Sealed Secrets can be encrypted and decrypted by authorized parties, preventing unauthorized access to sensitive data.

However, the problem with Secrets is that they encode critical data in base64 that makes it easy for anyone to decode it but Kubeseal helps in encrypting the original secret resource and decrypt

This decryption is via a controller only which makes it safe to keep in a public repository.

Here are the three main features of Kubeseal:

• External Key Management: Integrating sealed secrets with external key management systems such as AWS KMS or HashiCorp Vault can enhance flexibility, and security, and leverage granular access controls.
• Custom Controllers: Creating and enabling custom controllers can automate and manage secrets across various clusters or can be embedded into external systems.
• Enhanced Security and Compliance: Secrets can be stored in a non-encrypted format in the etcd datastore. Sealed secrets are safe to be shared publicly and uploaded to git repositories.

How Cloud4C Assists in SAST Integration with AWS?

Thanks to DevOps, developing and deploying applications at a much quicker rate has become a possibility. However, security does come as a stumbling block to quick software development. This is where Cloud4C's DevSecOps services play an important role. As a leading cloud managed services provider our DevSecOps solutions enable continuous assessment, analysis, and monitoring to fix vulnerabilities at an early stage of the development process.

In addition, our professionals help in deploying AWS security tools and solutions like Amazon Cognito, AWS CloudTrail, Amazon CloudFront Security, and Inspector to build secured, in-compliant SaaS enterprise applications.

Do you want to know more about our DevSecOps services? Visit our website and get in touch with our representative today!