As a savvy developer, you know that generative AI is a must to boost your productivity, speed and efficiency.
But, you’re also cautious.
You read about the Samsung data breach (where employees unwittingly input confidential source code into ChatGPT and faced disciplinary action as a result), and you don’t quite know how to reap the rewards of generative AI while avoiding the risks.
We’re here to help. Below, we’ll explore all there is to know about generative AI in the software development life cycle (SDLC), and equip you with the insights you need to unleash the benefits of generative AI securely.
The rewards of using generative AI in software development
While Samsung might have banned generative AI after the data leakage incident(s) got out, we don’t think that’s a wise move. Generative AI, after all, is a powerful force, one that is set to revolutionize software development for the better.
Here are just some of the ways in which developers stand to benefit:
Productivity enhancements
Generative AI’s proficiency in pattern matching and synthesis opens up a wealth of new possibilities in software development, from translating natural language into code to adapting different forms of codes or converting change logs into release descriptions.
Reduced time-to-market
Generative AI tools also play a pivotal role in diminishing the time investment needed for the development and delivery of software products.
They facilitate swift prototyping, iteration, and refinement of code, all of which enhance agility in the development process.
Cost savings
Besides the speed boost, integrating generative AI tools can seriously cut down on your costs. These tools act as effective aids in optimizing your existing codebases. They can suggest relevant code snippets and encourage the reuse of proven patterns, minimizing the need for redundant coding efforts.
In simpler terms, you won’t have to spend resources rewriting what’s already in good shape.
The risks of using generative AI in software development
Now, let’s delve into the less enticing aspects of introducing generative AI into the software development life cycle. Here are the potential pitfalls that should be taken into consideration.
Intellectual property breaches
Entrusting AI with code generation can lead to significant intellectual property concerns. The abundance of usable code produced by generative AI is often attributed to extensive source material gathered from public repositories, some of which lack permissive licensing.
This means that, if you’re not careful, the AI-generated code you divide to use could end up in a lawsuit.
Data leakage
Another notable risk associated with generative AI tools is the potential for employees to inadvertently feed confidential data into training models, as exemplified by the Samsung incident. In fact, according to a recent Gartner survey, 57% of company leaders are concerned about the possibility of leaked secrets in AI-generated code.
While the act of posing a question to a tool like ChatGPT may appear harmless, the inclusion of company secrets within queries, such as credentials or keys in code, would be an immediate data breach and potential compliance fine.
Vulnerable code
Another risk in the realm of generative AI is the potential for platforms to reproduce flawed code circulating in the open-source community. An example of this challenge is the issue of “package hallucinations.”
Package hallucinations refer to situations where platforms like ChatGPT or GitHub’s Copilot propose non-existent packages or generate source code that includes dependencies that either do not exist or could be intentionally malicious, creating huge security risks.
Software supply chain incidents
Even if your company bans generative AI for internal projects, the risks above can still creep into your code as a result of open-source libraries usage. These libraries might not be as picky with their contributions as your internal code review team.
Looking ahead & looking left
Just as developers have embraced a longstanding philosophy of enhancing performance and security, the integration of generative AI demands a “shift left” approach. This entails incorporating security practices such as data loss protection, redaction, continuous testing, and validation as early as possible in the development life cycle, with heightened vigilance in areas where generative AI is applied.
Of course with generative AI generating possibilities so quickly, development teams will need to find a way to test their code, discover vulnerabilities, and limit data loss at lightning speed. Otherwise, they risk undermining the very benefits they wish to unlock.
How Polymer can help reduce the risks of using generative AI
To shift left, boost security, and reap the rewards of generative AI, developers cannot do what they have always done. Novel development tools require a new approach to security. And that’s where Polymer comes in.
Harnessing the power of AI and machine learning, Polymer has joined forces with the software supply chain security Phylum. Together, we offer customers best-in-class data loss prevention (DLP) and software development life cycle (SDLC) security to minimize the risks of generative AI so you can focus on the rewards.
With our tool, you can address the internal concerns of generative AI use in combination with AI-driven software supply chain capabilities that proactively identify and block third-party risks. The result? A SDLC that combines speed and security.
Ready to get started? Reach out to the Polymer or Phylum team to learn more.
