7 Tips for Safely Using Einstein GPT
Static code analysis is a non-negotiable aspect of securely leveraging generative AI tools like Salesforce’s Einstein GPT for writing new lines of code.
Why It Matters: Generative AI is continuously growing in popularity and sophistication across every industry and function. However, the speed of innovation is surpassing our understanding of the data security implications of tools like Einstein GPT.
- 55% of workers have used unapproved generative AI tools while at work.
- Proper guardrails and training are critical to ensure these workers don’t introduce quality or security issues into your Salesforce environment.
Here are seven tips for safely using Einstein GPT for AI-generated code:
- Leverage Static Code Analysis
- Limit Access
- Stay Current with Updates
- Provide Ample Training
- Work in Developer Sandboxes
- Implement Strong User Authentication Mechanisms
- Consider Compliance
1. Leverage Static Code Analysis
Everything that comes out of generative AI needs to be verified. This is especially true for code because bad code has the capacity to introduce functionality issues and data security vulnerabilities into your Salesforce environment.
Leverage a static code analysis tool to review the code generated by the AI model before integrating it into your project.
These automated changes give your developers the information they need to quickly review AI-generated code and fix mistakes before they have a chance to infect your platform.
2. Limit Access
Generative AI tools require a degree of expertise to accurately and safely use them. A Salesforce static code analysis tool won’t be much help if the person using it doesn’t understand what the results mean.
Control who has access to the AI model and restrict permissions to only those who need it.
Limiting access allows streamlined oversight and proper allocation of resources.
3. Stay Current with Updates
New technologies will often need to release updates and patches to support continued performance and to address emerging data security vulnerabilities. It is up to the user to download and integrate these patches to enjoy the benefits.
Keep the AI model and its dependencies up-to-date with the latest security patches.
It’s important to be proactive about these matters, so monitor its usage and performance for any unusual activity that could indicate a security breach.
4. Provide Ample Training
Offering the support of DevOps tools like static code analysis will go a long way toward helping your team safely use tools like Einstein GPT, but it won’t do everything.
Mandate security training to developers and users who interact with the AI model to raise awareness of potential security risks and best practices.
A unified approach and adherence to internal standards will keep everyone on the same page and eliminate confusion.
5. Work in Developer Sandboxes
Letting a generative AI tool loose in your development environment has the potential to release damaging lines of code into your updates.
Run the AI model in a sandbox environment to limit its access to system resources and prevent it from executing malicious code.
This layer of protection creates a firewall between your system and the unchecked lines of code from Einstein GPT. Code should be thoroughly tested before being integrated with the update.
6. Implement Strong User Authentication Mechanisms
We discussed limiting access to development environments with AI tools, but this can be taken even further by adding a layer of verification before entry is approved.
Implement strong user authentication and authorization mechanisms to control access to the AI model and the code it generates.
A method like two-factor authentication is a great way to verify users who have access to your AI tools.
7. Consider Compliance
Regulated industries like finance, healthcare, and insurance need to keep data security regulations top of mind when incorporating new DevOps tools.
Ensure your usage of the AI model complies with relevant security standards and regulations.
The specific regulations are determined by location and industry.
Next Step…
Artificial intelligence is only increasing in popularity. This technology is at the point where users either need to work toward implementing it themselves or be left behind. Static code analysis tools are critical for leveraging AI tools in Salesforce DevOps, but there’s more to know than that.
Read our ebook, Advantage or Liability? AI in Salesforce DevOps, to dive deep into the dangers and solutions for implementing artificial intelligence into your DevOps pipeline.
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.
How are developers using AI-generated code?
Generative AI tools are being used by workers across just about every industry. Their flexibility and speed have increased productivity in a variety of roles, and developers are no exception. Salesforce tried to find out exactly how popular these tools are with teams through a study released last year. They found that 28% of workers are utilizing generative AI tools. The most surprising insight from this group of workers, however, is that about half of them are doing so without formal approval from their superiors. This means that a large section of the workforce is using generative AI without proper guidance or a unified set of best practices throughout their organization.
What are the main risks of AI-generated code?
The code produced by Einstein is based on what it has learned through its models, but there’s no guarantee those models are correct. Internal coding and architectural standards guide your developers as they build new applications and updates. AI tools aren’t going to follow these standards, making AI-generated code stand out from the rest of your coding updates. There’s simply no way around the fact that Einstein’s coding models aren’t 100% reliable. Generative AI tools are built on public information, so if a pattern is recognized as the solution to a particular query, it’s going to be used whenever prompted. But if that pattern contain data security vulnerabilities, anybody who uses a similar prompt will be open to the same attack.
