Einstein GPT Moves Fast—Too Fast: Why You Need an AI Airbag

Code scanning tools are a critical aspect of ensuring AI-generated code doesn’t introduce quality and security vulnerabilities into your Salesforce environment.

Why It Matters: AI is quickly becoming a non-negotiable aspect of writing code. These tools are growing more evolved by the day, and those that don’t use them will quickly be left behind. However, the data security implications can’t be ignored.

• New AI tools are consistently introduced with new features and capabilities.
• The majority of workers—55%—have used generative AI tools at work without the permission of their organization.

Here are six reasons code scanning tools need to accompany AI-generated code:

1. Unreliable Quality
2. Potential for Security Vulnerabilities
3. Inability to Adhere to Best Practices
4. Inefficient Code Performance
5. Unpredictable Dependability
6. A Full System of Support is Necessary

1. Unreliable Quality

Large language models (LLMs) are constantly being refined with new inputs and data. They are a continuous work in progress, which means they aren’t perfect, and all code generated with a tool like Einstein GPT is unreliable.

AI-generated code responds to queries with information fed into it. And if this information is faulty, the results it spits out will be equally flawed.

Code scanning tools need to be used in conjunction with Einstein GPT to ensure the code it produces meets your organization’s quality standards.

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2. Potential for Security Vulnerabilities

The most damaging coding vulnerabilities are the ones you don’t see. They have more time to infect your platform and expose or damage sensitive data. And when using AI-generated code, you simply don’t know what’s hidden in there if you don’t investigate it.

AI-generated code has the capability to introduce security vulnerabilities such as SQL injections, cross-site scripting (XSS), or buffer overflows.

Scanning code for these vulnerabilities is the only way to be sure you aren’t doing more harm than good when integrating AI-generated code.

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3. Inability to Adhere to Best Practices

Your team of developers can be trained to adhere to coding best practices for quality, structure, and security; however, this requires an effort on their part to learn these standards and have the ability to communicate these practices to each other. Einstein GPT can’t connect with team members in the same way.

Code scanning tools can be used to ensure AI-generated code adheres to internal standards and coding best practices and eliminate inconsistencies between developers.

Teams that use Einstein GPT need to verify coding structures before integrating it with the main repository.

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4. Inefficient Code Performance

One of the main perceived benefits of utilizing a tool like Einstein GPT is that it expedites the code-writing process. And while this is true, it doesn’t take the entire application lifecycle into account. Time saved up front creates a backlog of processes that will need to be accounted for in later stages.

Rigorous testing is crucial for confident deployment of AI-generated code.

These tests can create bottlenecks in later DevOps stages. And once the code is deployed, any unchecked errors will slow down the performance of the ultimate product.

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5. Unpredictable Dependability

Consistency is key to maintaining a successful Salesforce DevOps strategy. It helps in the early planning stages and sets expectations for end users when new applications and updates are introduced. However, the unreliability of AI-generated code makes it much more difficult to be consistent across releases.

LLMs have the capability to respond to a singular prompt in a variety of ways, which makes predicting the results impossible.

Code scanning tools check this code in real time against predefined rules, which can be used to craft these responses into reliable results.

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6. A Full System of Support is Necessary

All these considerations add up to an unreliable system that has the capacity to introduce data security vulnerabilities into your system. And if you don’t have the proper guardrails in place, you could end up degrading the quality and security of your entire platform.

A full Salesforce security posture management solution provides multiple layers of support to both prevent security vulnerabilities and minimize any threats that slip through.

Einstein GPT has the capacity to greatly increase the rate at which you can produce code, but this can end up harming your Salesforce environment if your security and quality tests can’t keep up. These tools are still in their infancy and need to be examined to be used safely.

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Next Step…

Code scanning tools are an essential aspect of safeguarding your system when using AI-generated code. However, this isn’t the only way these tools support a strong data security strategy.

Check out our ebook, Preventing Salesforce Security Risks Through Code Quality, to learn more about how strong code protects your Salesforce environment.

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FAQs

What is Einstein GPT?

Einstein GPT refers to a suite of AI tools within Salesforce. It’s built with OpenAI’s ChatGPT and Salesforce’s own AI models. It can also be combined with a user’s own external AI models. This creates a highly adaptable interface that is continuously being updated with new data. Users can enter prompts to engage with the software to create AI-generated content within their Salesforce environment. This can take the form of email copy, code, lead qualification, sales cycle summaries, and much more.

What is Einstein for Developers?

Einstein for Developers is a generative AI tool that produces code through prompts. It connects to your environment as a Visual Studio (VS) Code extension and relies on the information stored in the source models. Along with giving non-developers the ability to write code, it enables development teams to quickly generate lines of code. Einstein for Developers is built right into your Salesforce environment. This gives it access to your metadata, so it can use your existing code to make recommendations.

How advanced are AI code generating tools?

AI code-generating tools have made significant strides in recent years, leveraging advanced machine learning models. These tools can assist developers by generating code snippets, suggesting completions, and even writing entire functions based on natural language descriptions. They are often used to automate repetitive tasks, accelerate prototyping, and provide code examples in various programming languages. However, while impressive, these tools are not flawless. They still require human oversight to ensure accuracy, security, and adherence to best practices. Their capabilities continue to improve, but they currently function best as a tool to help you get more done instead of a replacement for human developers.

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