COMMENTARY

The advent of artificial intelligence (AI) coding tools undoubtedly signifies a new chapter in modern software development. With 63% of organizations currently piloting or deploying AI coding assistants into their development workflows, the genie is well and truly out of the bottle, and the industry must now make careful moves to integrate it as safely and efficiently as possible.

The Rise of AI Coding Assistants

The OWASP Foundation has long been a champion of secure coding best practices, providing extensive coverage on how developers can best defend their codebases from exploitable vulnerabilities. Its recent update to the OWASP Top 10 highlights the disastrous data exposure, or pave the way for serious data poisoning and embedding inversion attacks.

Security Considerations for AI Coding Assistants

A comprehensive understanding of both core business logic and least-privilege access control should be considered a security skills baseline for developers working on internal models. However, realistically, the best-case scenario would involve utilizing the highest-performing, security-skilled developers and their AppSec counterparts to perform comprehensive threat modeling and ensure sufficient logging and monitoring.

The Importance of Security Knowledge

As with all LLM technology, while this is a fascinating emerging space, it should be crafted and used with a high level of security knowledge and care. This list is a powerful, up-to-date foundation for the current threat landscape, but the environment will inevitably grow and change quickly. The way in which developers create applications is sure to be augmented in the next few years, but ultimately, there is no replacement for an intuitive, security-focused developer working with the critical thinking required to drive down the risk of both AI and human error.