AI coding assistants can be an efficient way to improve the coding process. They allow developers to automate many repetitive tasks so they can focus on the more grueling, detail-oriented aspects of coding. However, developers must watch out for potential security risks common among AI coding assistants.
Cybersecurity Risks Posed by Using AI Coding Assistants
Coding is an intensive process that requires developers’ vigilance throughout. While AI coding assistants alleviate some of that burden, the risks can still be severe depending on the specific coding project. Coding is even used to create AI applications, but that does not mean AI-performed coding is foolproof.
1. Vulnerable Code
AI coding assistants can create vulnerable code in various ways. They are often trained on public domain code, which can be secure or insecure. The AI coding assistant is not able to identify which is which. It also rewrites code from these sources without noticing any logical issues that might exist.
AI is rewarded based on whether it completes a task, not if it is done well, so it might create code that is not secure or without necessary security controls. Weak, repetitive codes create vulnerabilities that can be exposed by cybersecurity attackers.
2. Privacy Issues
AI coding assistants might overlook security considerations. AI does not understand security intent, so it can produce code that appears to be correct, yet the quality of the code is often inadequate.
This is problematic because AI coding assistants often have access to sensitive data. If the code they create does not match security protocols, then the data is at greater risk of being breached and stolen by attackers, creating various new issues.
3. Dependency
AI coding assistants can develop a dependency on vulnerable or deprecated code and let these dependencies bleed into other projects they are working on. If this goes unchecked, repeated mistakes could continue to be implemented within layers of code that will inevitably need to be sifted through once the error is noticed.
Developers are also becoming more dependent on AI coding assistants, assuming their coding methods are tested and safe. The training of these assistants determines how good they can be. If they are trained within only certain procedures and prompting, then they cannot exceed that level of skill. This overreliance means AI tools could make mistakes without the developer’s knowledge because they trust them too much.
4. Bias and Ethics
It is no secret that AI learns biases and ethics and implements them into its answers. The same goes for AI coding assistants. Whatever original datasets were used to train the assistant provide the foundation for future coding endeavors. If the original dataset had biases, then everything the AI codes afterward will have that same bias.
Consequently, AI coding assistants trained only on data from the specific industry they were purchased for can develop a narrow process for coding practices. This contrasts with the broader grasp of coding that developers gain through formal education. The assistants might not be able to account for more efficient and secure options for coding, or they could perpetuate poor coding practices on all of the projects they work on.
Another ethical concern is the coding assistants’ tendency to use another company’s IP address or copyrighted code. AI does not understand the legal implications of this, so the company it works for must notice this mistake to prevent a potential lawsuit.
How to Address These Risks
AI is a powerful tool when used responsibly. Addressing the risks associated with using AI coding assistants can help organizations maximize efficiency while remaining secure and consistently produce trustworthy code.
Secure Coding Practices
Companies should create security guidelines for AI coding assistants and accompanying developers to adhere to. In fact, 45% of security teams have policies on using AI to limit the negative impacts of relying on it. Establishing best practices, reviewing AI-created code and performing audits periodically should help limit the risk of crucial mistakes.
Security Tools
When developing AI coding assistants and training them on company data, integrating security tools into the development process should help AI learn to fact-check itself as it codes. It must be taught how to fix certain mistakes. The security tool should also perform occasional checks and analyses to make sure everything is running smoothly.
To further secure confidential company data, access controls and encryption protocols should be applied to any code available to AI coding assistants. This can increase overall data security and limit the risk of interference from cyberattackers.
Human Intervention
Most AI applications, including coding assistants, are not ready to operate solely on their own. AI still relies on machine learning, so flaws in the machine can be reflected in the work itself. For example, if an assistant suggests a third-party library to store data, human developers should monitor that library and determine its legitimacy before throwing all the data in there.
Suggestions made by AI assistants should be carefully vetted by a real human to check for mistakes and to ensure all data is secure. This keeps performance within guidelines.
Monitoring AI For A Safer Cybersecurity Culture
While AI coding assistants can make coding easier, it is important to remain vigilant when using them. Installing failsafes and assigning humans to monitor and check on the assistants periodically will help identify and eliminate security risks. Secure practices can limit the perpetuation of rogue, incorrect or illegal code.
