“Basics don’t change regardless of who or what wrote the code” – Aaditya Uthappa, Co-Founder & COO ||
Generative AI (GenAI) has redefined the way businesses work today. It fuels innovation, automates tasks, and simplifies the work itself. With over 55% of companies using GenAI, its adoption is rapidly increasing. However, this progress comes with potential risks. Data security breaches, privacy violations, and the generation of inaccurate or biased outputs remain key concerns. Recent studies in 2023 regarding the security of code that GenAI helps generate surveyed developers and found that over 56% encountered security vulnerabilities in code suggestions from AI tools frequently or sometimes. This highlights a significant risk, considering the widespread adoption of GenAI for code generation.
Gen AI Coding Assistants: Efficiency & Risk
In today’s fast-paced development environment, AI coding is a valuable tool to stretch your development budget further. They offer undeniable advantages: speed, efficiency, and convenience. However, these benefits come with inherent risks, particularly regarding data leakage and the potential for incorporating malicious code. Below are the merits and risks:
Merits
Risks
Are Developers Writing Less Secure Codes with GenAI Tools?
A recent study by Schneier Security suggests that developers who had access to an AI assistance or, Gen AI tool wrote significantly less secure code than those without access.
Schneier Security – “Participants with access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities.”
How Do We Mitigate AI-Generated Code Risks?
Auto-remediated AI assistants like GitHub Autopilot etc. pose a greater challenge, making it difficult to identify and address potential vulnerabilities. Here’s how you can mitigate AI-generated coding risks:
1. Reapplying Basic Security Hygiene
2. AI Coding Usage Guidelines
3. Governance Policy Controls
Indemnity Support in AI-Generated Code
The potential of AI-powered coding assistants is undeniable, but concerns regarding intellectual property (IP) infringement remain a critical consideration. Some vendors, including AWS, IBM, Microsoft, and GitHub, offer indemnity support as a safety net. However, to navigate this landscape, it’s essential to understand both indemnity’s limitations and the importance of proactive strategies.
Self-Hosted AI Models: Balancing Control and Complexity
Self-hosted AI models offer an attractive proposition for organizations concerned about intellectual property (IP) ownership and data privacy. While cloud-based solutions provide convenience, self-hosting allows you to retain complete control over your model and the data it’s trained on. Here’s a detailed breakdown of the merits and demerits to aid your decision:
Merits of Self-Hosting:
Demerits of Self-Hosting:
Recommendations for Secure and Responsible AI-Assisted Development
1. Maintain Traditional Security Measures
2. Prioritize Secure AI Tools
Browser-based DLP for Leakage Protection
Indemnity and IP Protection
5. Developer Training and Code Review
Finding the Balance
AI coding assistants can be powerful tools, but they require a cautious approach. By being aware of the risks and implementing appropriate mitigation strategies you can harness the benefits of AI-powered development while keeping your business secure.
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