The mobile application we used in 2017 required only permission for contacts and text messages. Nowadays, we allow permission for contacts, gallery, location, bank details, health records, certificates, fitness data, etc. We are allowing our personal information access to applications today, and ultimately, we must think of security for our information. The software development company also considers this situation and works to give the users peace of mind.
Security checks are being followed severely throughout the software development life cycle in DevOps to improve the security of the applications. The increased number of users requires enhanced support to improve se eager to kcurity. AI can play a major role in safeguarding security in DevSecOps. If you arenow how AI can be useful, then you are at the right place, learn about the role of AI in DevSecOps, its benefits, and much more in this blog.
Raising Security Threats for Applications
The day-to-day applications we use have numerous threats, and here are some major threats.
1. Supply Chain Attacks
When an insecure third-party application or vendor is included in the process, they can lose the data. There are chances of losing more valuable data through such compromised, insecure third-party applications.
You may see a notification blinking while installing the third-party application on your devices, saying the process involves a threat.
SolarWinds attack, Kaseya attack, and Target data breach are some major supply chain attacks that have occurred in the past decades.
2. Container Vulnerabilities
When an application uses insecure privileged containers, it is at risk of cyber attacks. The chances of cyber attacks are also high when there are vulnerable container images, misconfigured containers, improper container isolation, insecure networking, insecure API, and Logging and monitoring failures in the application.
A compromised Docker Hub repository could lead to the deployment of malicious container images. Kubernetes API Server Exploit and RunC Vulnerability are some examples of container vulnerabilities.
3. Phishing Attacks
This is a type of cyber attack in which fraudulent emails, messages, links, or websites are used to deceive a single user or an organization. The attacker attacks the device or application to collect data or ransom from the victims.
Examples of phishing attacks include the PayPal phishing campaign, Sony Pictures Hack, and Google and Facebook phishing scams.
4. Code Injection
Code injection is a process in which hackers send malicious code through a form of user input. The malicious code attacks the original code of the application. This process includes SQL injection, LDAP injection, and command injection. The attackers can get access to the system or application through this attack.
Foxtons Group faced an SQL injection attack, 7-Eleven faced an SQL injection attack to breach users' credit card details, and Fortnite faced an SQL injection attack to compromise accounts. These are real-world examples of Code injection attacks.
5. Complex Nature of DevOps Process
The complex nature of DevOps can also compromise the application’s security. DevOps came into use to reduce development time by automating the process involved, which leads to loose ends that are vulnerable to attacks. Containers, CI/CD processes, and cloud environments are common attack points.
Things to Know About DevSecOps
DevSecOps refers to the integration of Security in the DevOps process. Since the security risks in the software development process are increasing daily, Security is also integrated throughout the DevOps lifecycle to form DevSecOps. This process also increases collaboration between the development, operational, and security teams.
The integration of security enhances the application’s security. In this DevSecOps process, the security check is either automated or integrated into the development process to avoid any security issues. This process is better than handling or treating the application's situation after the security breach.
Many software development companies are using this approach. Apps like Airbnb, Bloomberg, and Gitlab are famous for integrating DevSecOps to make their applications safe and secure.
Role of AI in DevSecOps
What is AI in Devsecops? AI can be used in DevSecOps to increase the security of the applications. AI increases the security of applications by automating certain processes, continuously monitoring certain processes, and generating the resources to improve the security of applications in the DevSecOps process. Here are some important roles of AI in DevSecOps:
1. Early Detection of Vulnerability:
AI-powered tools can be used to scan code repositories automatically and repeatedly to find vulnerabilities like SQL injections and XSS attacks. These prior deduction tools can help reduce vulnerabilities and severity. The AI can also provide the action for remedies to rectify problems.
2. Prediction of Threats
AI can be trained with ML based on previous security breaches. Using this learning, AI can predict potential threats during the process based on previous patterns.
AI can be used to monitor every process involved in application development in real time after the training. This will help detect unusual threats and alert users.
3. Using AI for Automation
AI can be used for automation of repetitive tasks in DevSecOps. This process can reduce the time involved in repetitive tasks and to reduce the possibilities of manual errors. The process like vulnerability scanning, compliance checks, and incident response can be automated by using AI.
Automation, with the help of AI, can reduce the time spent on software development and the threats involved by automating processes like code analysis and testing.
4. Improving the Quality of Code with AI
Generative AI can be used to create quality code that can be safe for malware attacks. The generated code can be safe from bugs, potential security issues, and meet the standards of the industries.
AI can be used to generate repetitive compliance checks throughout the development lifecycle. This will ensure the consistency of every process involved in the development.
5. Intelligent Analysis of User Behavior
AI can be used to detect unusual activities. AI can identify unauthorized access requests or unusual activities that can affect the security of the applications. This can reduce the chances of using the personal information of an individual access from the login page or from the data.
6. Incident Response and Recovery
AI can automatically detect and respond to security incidents, such as ransomware attacks, reducing the time it takes to contain and resolve them.
AI can also assist in incident recovery by identifying the root cause of the issue and suggesting appropriate solutions.
Tools Integrated with AI for Enhancing Security
App development companies are currently using certain tools to increase the security of applications. Here are the list of tools with AI functionalities:
1. Static Analysis Tools (SAST)
A tool for analysing code without executing it will reduce potential vulnerabilities at the coding stage. These tools are known as SAST.
- Checkmarx
This SAST tool integrates AI to improve the static analysis of codes and for identification potential vulnerabilities before it's executed.
- DeepCode
DeepCode is another AI-powered code reviewing tool, the AI undergoes machine learning to identify the weak spot in the codes.
- Snyk
Snyk helps detect and resolve security flaws in open-source libraries, which are often used in app development.
2. Dynamic Analysis Tools (DAST)
This DAST is used to analyse the application running and find potential vulnerabilities and threats from external sources.
- Palo Alto Networks Cortex XDR
This tool uses machine learning to find threats and anomalies in the application. It also uses data from different sources to provide a single security approach.
- Vectra AI
This tool also uses machine learning and AI for network detection and response to enhance security. The tool is used to detect and prioritize cyber security threats based on the effects and environments.
- Cybereason XDR
Cybereason XDR uses AI for threat-blocking purposes and NGAV to prevent and detect every possible threat.
3. AI-Powered Code Assistants
An AI-powered coding assistant is a special tool to help in writing codes for development, automation code writing, and maintaining code.
- GitHub Copilot
An AI coding assistant that can suggest secure code, identify potential vulnerabilities, and even assist with fixing them.
- CodeAI
CodeAI aims to prevent vulnerable code by automatically allocating security measures.
- Tabnine
Tabnine offers code completion and suggestions, including secure code practices.
Benefits of Using AI in DevSecOps
AI is supporting multiple processes from different industries, likewise it has capacity to support the DevSecOps. When we use AI in DevSecOps we can expect the following benefits in the software development process and application’s security.
1. Improved Threat Detection Capacity
AI integrated tools can help in finding the threats even before execution. This process reduces the chance of missing any threats. Traditional methods take more time to cover huge data checks but AI can help the process to be simple.
2. Efficient Anomaly Detection
AI integrated tools can be trained to find the unusual patterns that occur in codes, application behavior, user behavior. These tools create an alert if there are any unusual patterns. With this approach it can be rectified before it becomes a worse case.
3. Automating the Repetitive Tasks
The manual repetitive tasks during the inspection can lead to manual errors or missing any potential threats. To eradicate it, AI integrated tools can be used to automate all hectic tasks.
4. Cost Savings
Since major tasks can be automated to improve the efficiency, this can reduce the manual involvement and result in reducing the cost savings for DevSecOps.
5. Resource Optimization
AI tools are capable of identifying the issues in the initial stage and they can prioritize the issues based on severity. Through prioritizing the issues the resource allocation and assigning the task based on resource expertise is possible.
6. Risk Management
AI tools can be trained with basic security standards that will help maintain the application's regulations. Automating compliance checks and generating reports through AI helps in effective risk management.
These are current trends of AI in Devsecops uses it may increase as the technology grows.
Using AI in DevSecOps will Help in Securing Future
AI has a huge scope in application development industries. It is capable of increasing the security of software as its main conduct. DevSecOps is a collaboration of development, security, and IT operations teams to bring software with more safe features for users.
I have shared only a few uses of AI in Devsecops; there are a lot more to come. If you join the DevOps Course, you can learn more about using AI in application development processes. Join us to empower yourself with the skills to protect the future app-some world!
