The Era of “Move Fast and Break Things” Is Coming to an End
Not long ago, “move fast and break things” was celebrated for its positive impact on the innovation process. Fast-forward to today, AI-assisted software development is reshaping our software development workflows, and “move fast without breaking things” is the new updated motto where organizations need to exercise high levels of due diligence to maintain trust, security, and fulfill legal obligations.
For the past two years, generative AI tools have emerged as a multiplier for developers’ productivity, however, it has introduced three distinct risks: (a) obscured code provenance, (b) introduced potential license compliance issues, and (c) surfaced the threat of hidden security vulnerabilities. As a result, organizations must evolve their Software Composition Analysis (SCA) strategies to embrace the granular precision of snippet-level detection, integrate it into their developer workflow (shifting left), and exercise compliance and security checks as early as possible in the development lifecycle.
Questions to Address with AI-Generated Code
Unlike open source packages and files, which typically come with licensing information, AI-generated snippets lack attribution or details about their origin and licensing information. This situation raises several uncertainties:
- What’s the origin of the code?
- Is this code snippet a direct copy of some of the model’s training data?
- What licenses govern this AI-suggested code?
- Do these licenses impose obligations, such as the stringent copyleft requirements?
- Has the original code been modified in ways that inadvertently introduce or obscure vulnerabilities?
These are not abstract questions. AI models are trained on vast datasets of publicly available code, including open source software governed by a diverse range of licenses. When these models regenerate similar code patterns, functions, or even short blocks of code, they can easily reintroduce license obligations and potentially security vulnerabilities.
The Importance of Snippet-Level Detection
Organizations are shifting their SCA strategies beyond traditional component or file-level scanning to the granular precision of snippet-level detection. While conventional SCA tools focus on managing whole open source components and their dependencies, they prove inadequate when confronted with AI-generated code. This is where snippet detection becomes indispensable. Snippet detection engines analyze fragments of code, some are capable of precision levels down to just a few lines, comparing them against an extensive reference database (also called a knowledge base) of open source code. This level of scrutiny is essential for identifying three core elements:
- License obligations: AI-generated code may incorporate snippets from open source projects, triggering compliance obligations.
- Security vulnerabilities: AI-generated code may reproduce vulnerable code present in open source software.
- Code provenance: Snippet detection provides insights into the origins of code fragments, supporting the effort to provide a complete and accurate Software Bill of Materials (SBOM).
Without this level of granularity, organizations are operating with blind spots, leaving themselves exposed to unseen potential legal and compliance issues and security vulnerabilities.
Key Traits of a High-Caliber Snippet Detection Engine
As snippet detection becomes a cornerstone of responsible AI-assisted development, it is essential for developers, OSPO teams, and compliance professionals to evaluate SCA solutions and their snippet-level capabilities. We would like to highlight 5 criteria for evaluation:
- High-precision matching: The scanning engine must employ advanced algorithms capable of identifying partial matches and snippets accurately, even with variations in variable names, comments, and minor structural changes.
- Comprehensive and curated knowledge base: A well-maintained and up-to-date database of open source code is fundamental for effective detection.
- License intelligence: Detection capabilities must be tightly coupled with precise license metadata, including robust handling of dual-licensing scenarios and adherence to SPDX identifiers.
- Scalability and integration: Effective tools should integrate into modern CI/CD pipelines, IDEs, and other developer tools, ensuring that compliance and security checks are performed early and often within the development lifecycle.
- Security awareness: The ability to identify snippets associated with known vulnerabilities or deprecated patterns. Such capabilities enable developers to know the exact lines of vulnerable code.
By prioritizing SCA solutions with these capabilities, organizations can align their compliance and security posture with the evolving realities of AI-assisted development.
Moving Forward with Confidence
Organizations are already adapting their open source compliance strategies, investing in SCA tools with snippet detection, establishing Generative AI use policies, and educating their teams to adopt AI-assisted coding at a large scale.
The future of software development isn’t a trade-off between speed, compliance, and safety. It’s about architecting systems, processes, and developers workflows that empower us to achieve all. Snippet detection is a vital component of this architecture and a must-have feature in any modern SCA tool.
So, by all means, move fast! Just ensure you bring your snippet-enabled SCA tool along for the journey.
Happy hacking!
What’s In Your Code?
Interested to learn how FossID can work for your team?
Get in touch with a solution advisor and schedule a live demo. We will be happy to discuss your needs and explore how FossID can support your shift-left goals, improve engineering efficiency, and help your team manage open source compliance and security risks.
