Static code analysis provides critical guardrails for AI-generated code. Left unchecked, AI code can introduce data security vulnerabilities through malfunctioning software updates.
Why It Matters: Artificial intelligence is entering just about every industry. But when it comes to Salesforce DevOps, these tools need oversight to confidently introduce new applications.
- Tools like ChatGPT, Microsoft Copilot, and Agentforce are changing the way workers approach their daily tasks.
- Given a single prompt, these tools will provide new results every time. And while this is interesting for general questions, it proves problematic for software development.
Here are four things you need to know about AI-generated code with Salesforce static code analysis:
1. Why You Need to Test AI-Generated Code
The main reason for adopting generative AI is to increase the velocity at which traditional IT infrastructure is created. Tools like GitHub Copilot and ChatGPT streamline code generation, enabling more people to create software, including those without extensive programming expertise.
With faster prototyping and development cycles, the sheer volume of traditional software (including Salesforce customizations) will grow, leading to an increased need for security and performance controls. An increase in complexity is unavoidable with this influx of new code.
AI-assisted development may produce code with complex interactions, making traditional static and dynamic analysis tools indispensable for identifying vulnerabilities and optimizing performance.
Currently, safety in IT systems is ensured because the rate at which they are inspected is similar to the rate they’re created. As the speed of creating IT components increases, the speed of inspecting them needs to increase correspondingly. Safety controls like Salesforce static code analysis need to be integrated to introduce guardrails and avoid letting things get out of hand.
2. How AI-Generated Code Will Grow
Growth in generative AI will primarily be driven by companies with financial incentives to harness it. It’s a fundamentally expensive computing technology, thus the revenue impact will need to exceed the costs, and anyone experiencing net gains will encounter a strong positive feedback loop to increase activities.
One of the biggest cost savings from AI will either be to reduce head count or leverage existing human workers more thoroughly.
The majority of employees will find a way to appease their supervisors and reduce the risk of losing their jobs. The inaccuracies injected by AI may go unnoticed because you can’t question a tool you don’t understand.
Implicit assumptions baked into the AI engines are likely to be strongly reinforced as there will arise a plurality of AI providers competing with each other.
3. What the Common Security Risks Are
Unchecked AI-generated code is likely to introduce security vulnerabilities. This is why a Salesforce static code analysis tool is crucial for safely using generative AI.
Here are three main ways generative AI creates security issues:
- Need for human review and scanning: The volume and complexity of AI-generated code will necessitate robust traditional controls to mitigate risks.
- Biases in AI models: AI-generated code may inadvertently include security vulnerabilities, performance issues, or compliance oversights inherited from its training data.
- Limited contextual understanding: While AI excels in specific tasks, it often lacks a holistic understanding of the broader application context, increasing the likelihood of subtle bugs or security risks.
4. How Salesforce Static Code Analysis Addresses These Concerns
Generative AI creates a dynamic component that needs a static component to counterbalance it. As the speed of a software application increases, there are static rules you need to be testing for and enforcing to make sure you’re truly operating in a reliable way.
CodeScan functions by producing stable, static, and reliable tests of an application’s security and performance.
Salesforce static code analysis tests will always produce the same results. They will not suddenly start failing components that previously passed. They will not randomly start passing components that previously failed or neglect to create tests.
These kinds of stable patterns of enforcement allow organizations to sign off with confidence that you’re consistently meeting security requirements, even as the pace of development is increasing.
Next Step…
Static code analysis goes a long way toward improving the quality of data in your Salesforce environment, but it isn’t the only DevOps tool that ensures the integrity of your information.
Read our blog, Are You Protecting Your Data with Salesforce DevOps Tools?, to learn more about how these tools keep your IT environment secure.