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How to Protect Your Code
in the Age of GenAI
Why an LLM-first security approach is the key to preventing vulnerabilities
Welcome to the coding superhighway
It seems like every developer is doing it these days. Well, just about. According to Gartner, 75% of enterprise software engineers will be using these tools by 2028.
With GenAI permeating almost every aspect of our personal and professional lives, it may not come as a big surprise that Large Language Model (LLM)-based generative AI tools such as GitHUb’s Copilot, Google’s Due AI, and Amazon’s CodeWhisperer, have become a routine component of the coding toolkit.
But here’s a fact that should make security, risk, and compliance leadership sit up and take notice: around 80% of them are also bypassing security policies when using these tools, even though they know that GenAI coding assistants regularly create code that is not secure.
And this is the case even when using the most highly regarded tools out there.
A New Era of AI Code Assistants
GitHub Copilot
Its integration into popular IDEs like Visual Studio Code amplifies its appeal, making Copilot an indispensable tool for many developers.
Amazon CodeWhisperer
Integrated into AWS's ecosystem, CodeWhisperer stands out with its emphasis on security and compliance, appealing to enterprises focused on code quality and regulatory standards
Google Duet AI
This integration directly into Google Cloud operations and products offers developers a crucial advantage to streamline work and enhance productivity. By leveraging Google's extensive cloud infrastructure, developers can expect a seamless and efficient coding experience that boosts overall project efficiency.
Key Features and Benefits of Secure Code Assistant
Productivity
In studies conducted by the Nielsen Norman Group, programmers who used AI tools said that they could code 126% more projects every week.
Quality
According to research by GitHub about its Copilot Chat tool, 85% of developers say that they feel more confident in the quality of their code using GenAI.
Consistency
In addition to making better code in less time, developers using GenAI code assistants have reported gains in the consistency of their code.
Efficiency
Automating routine coding tasks, suggesting code improvements, and providing debugging support are transforming the programmer’s day-to-day experience.
Speed
Time-consuming manual searches, queries, and indexing are now a thing of the past and problem solving is accelerated to near real-time.
Compliance
Ensures adherence to industry standards and regulatory requirements such as ISO, SOC2, and AI global and regional laws and acts.
In fact, with GenAI’s unique ability to deliver code snippets, provide instant suggestions, simplify development workflows, and much more, so much of coding has completely changed.
So, AI code assistants can make code production faster, more efficient, more productive, and less complex. That’s the upside. But there’s also a downside. AI coding is also a lot less secure.
The Risks of AI-Generated Code
With all the benefits that GenAI code assistants bring to developers, it is critical to make sure that the
team is also keenly aware of the risks that are involved.
When data poisoning, data leaks, or other attacks occur as a result of vulnerable code produced by GenAI, the organization is at a greater risk than ever of falling victim to the malicious attacks of threat actors.
And the associated damage can be great, entailing disruptions to operations, loss of intellectual property, compromised competitiveness, compliance transgressions, reputational damage, and more.
Popular approaches can’t rope in the LLM cyberthreat
- Enhancing code review processes
- Expanding automations
- Providing training and awareness programs to employees
Thorough processes, automations, and vigilance alone are not sufficient to ensure that the code that is generated is reliable and doesn’t introduce security vulnerabilities. But blocking access to GenAI tools is also not an option.
However, Generative AI is here and it’s here to stay. The benefits are too great. When it comes to code assistants, it’s not a matter of ‘yay’ or ‘nay.’ It’s a matter of enabling development teams to reap all the benefits while avoiding the risk.
The answer is – to go beyond securing code with LLMspecific protection that actually secures the very use of GenAI tools, as they are being used, but without disruption to developers, of course.
This is where the Secure Code Assistant from Lasso Security comes into play.
How Lasso Security Can Secure Your Code Assistant from LLM Risks?
Lasso Security empowers developers to unlock the potential of AI-assisted coding without compromising security. Lasso has an in deep knowledge of the attack surfaces, providing users with much more than just a secure code.
With a dedicated security solution like Lasso for Code Assistant, an intelligent LLM-first solution, they can ensure that every interaction with AI code assistants is secure, private, and compliant, and brings no disruption to their workflows.
The solution is an easy-to-install IDE plugin that seamlessly integrates into their environment, requiring zero coding and data science expertise.
It operates between LLMs and developers, observing all data movements, and detecting dangerous inputs and unauthorized outputs.
With advanced code scanning, it ensures that incoming code suggestions align with the organization’s security standards.
And by dynamically masking sensitive data and scrutinizing incoming code in real time, it prevents sensitive elements, such as credentials and proprietary logic from reaching AI assistants, sending alerts to users in the event that a threat is detected.
The Lasso Secure Code Assistant
Protects
intellectual property
Prevents
the introduction of insecure code
Enhances
compliance with security policies
Real-Time Data Masking
Monitor Every Data Movement in Minutes
The bottom line
With the frequency and sophistication of attacks increasing, no organization can afford the tradeoff between developer productivity and code security. With dedicated LLM security, Lasso Secure Code Assistant eliminates the need for that tradeoff altogether. Developers can now have the freedom to capture all the benefits of large language models for their coding, and to do so securely, responsibly, and disruption-free.