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While tools like GitHub Copilot, Amazon CodeWhisperer, and similar AI assistants promise to accelerate development velocity, they have simultaneously introduced urgent challenges around code transparency, licence compliance, and intellectual property risk. Yet most development teams struggle to answer critical questions: Does our AI-generated code contain undeclared open source? What licence obligations have we inadvertently assumed? Are we exposed to copyright infringement claims? How do we maintain compliance without slowing development?

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The Challenge of AI-Generated Components

AI models are trained on vast repositories of public code, which means they frequently produce code that closely resembles, or directly replicates, existing open source implementations. These similarities are often invisible to developers who assume the generated code is original. The problem is structural: AI models learn patterns from their training data and naturally reproduce those patterns when prompted for similar functionality. Research demonstrates the scope of this challenge. Studies analysing LLM-generated code found that between 0.8% and 5.3% exhibits notable similarity to existing open source implementations (similarity strong enough to indicate copying rather than independent creation). When measured more permissively, approximately 30% of AI-generated code shows at least some degree of overlap with open source codebases. These aren’t edge cases, they represent mainstream AI coding tool outputs. The implications are severe:
  • Hidden in plain sight: AI-generated snippets don’t appear in dependency manifests, bypassing traditional Software Composition Analysis (SCA) tools that only scan declared dependencies.
  • Scattered across codebases: Rather than concentrated in a few files, AI code fragments appear throughout projects, embedded in functions developers wrote themselves.
  • Licence obligations without awareness: When an AI model generates code matching GPL-licenced source code, developers inherit those licence obligations, even though they never consciously copied anything.
  • Undetectable by conventional means: Traditional package-level scanners miss these snippet-level similarities entirely, creating blind spots in compliance programmes.
Consider this scenario: a developer uses GitHub Copilot to implement authentication middleware. The AI generates a function that closely matches an existing open source implementation licenced under GPL-2.0. The developer commits it without realising the code isn’t original. The organisation now has GPL obligations it doesn’t know about, embedded in proprietary software. The fundamental problem remains: you cannot fix what you cannot see.

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Why AI Code Transparency Matters Now

Multiple converging factors have made AI code transparency urgent: Adoption Velocity: GitHub reports that in files where Copilot is enabled, nearly 40% of the code is written by the AI tool itself, particularly in languages like Python. This isn’t experimental usage, AI coding assistants have become essential productivity tools for millions of developers. The volume of AI-generated code entering codebases has exploded. Licence Compliance Risk: Most AI models were trained on open source code under various licences (MIT, Apache, GPL, BSD, and more). When AI outputs resemble that training data, the original licences may apply. Organisations face potential licence violations without knowing which open source components they’re actually using. Legal teams cannot assess risk they cannot see. Copyright and IP Exposure: Beyond licensing, verbatim or near-verbatim reproduction of copyrighted code creates copyright infringement risk. The Software Transparency Foundation’s research found that some AI outputs maintain substantial similarity even at stringent 30% thresholds, indicating potential copyright concerns that traditional due diligence processes miss entirely. Regulatory Pressure: Emerging regulations like the EU AI Act and enhanced cybersecurity requirements increasingly demand transparency into software composition. Organisations must demonstrate they understand what code they’re shipping, including AI-generated components and their provenance. Supply Chain Accountability: Customers, auditors, and business partners increasingly require Software Bills of Materials (SBOMs) that accurately reflect all components, including snippet-level open source. AI-generated code creates SBOM gaps that undermine supply chain transparency. M&A Due Diligence: Acquisitions and investments require comprehensive IP assessments. Undisclosed AI-generated code containing open source creates undisclosed liabilities that can derail transactions or trigger post-close disputes.

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Building a Strategy for Transparency

Managing AI code risk requires visibility beyond traditional dependency scanning. Modern DevSecOps teams need intelligent snippet detection systems capable of identifying code similarities at the fragment level, not just complete packages or files. SCANOSS provides this intelligence through multiple complementary approaches: Snippet-Level Detection: Identify open source code fragments within AI-generated outputs, regardless of whether they appear in dependency declarations. Detection works at the function and code block level where AI copying actually occurs. Winnowing Fingerprinting: Use advanced fingerprinting algorithms that detect structural code similarity even when surface-level changes (formatting, variable renaming, comment modifications) obscure the relationship. Research validates that Winnowing effectively serves as a preliminary indicator for deeper analysis. Comprehensive Knowledge Base: Compare generated code against SCANOSS’s knowledge base containing 27+ terabytes of unique open source software from 250+ million URLs (approximately 35 times larger than typical AI training datasets), providing broader detection coverage. Licence Intelligence: When matches are detected, immediately surface the licence obligations, copyright information, and compliance requirements associated with the matched open source code.

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SCANOSS Solutions for AI Code Detection

SCANOSS has developed a comprehensive suite of tools and workflows specifically designed to provide transparency into AI-generated code:

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SCANOSS Engine

The core SCANOSS Engine implements Winnowing-based fingerprinting optimised for detecting code fragments. Fast Winnowing provides up to 15x performance improvement, enabling real-time scanning of large codebases without blocking development workflows.

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SCANOSS-PY

The SCANOSS-PY command-line scanner integrates directly into developer workflows.

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SBOM Workbench

SBOM Workbench provides visual analysis.

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SCANOSS-CC

SCANOSS-CC enables detailed visual inspection for granular control over compliance decisions, allowing developers to examine matches line-by-line.

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Pre-Commit Hooks

Automatically scan code before commits reach the repository using Pre-Commit Hooks.

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GitHub Actions

Embed snippet detection directly into continuous integration pipelines using SCANOSS Code Scan Action.

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The Path Forward

AI coding tools are not experimental, they are mainstream developer productivity infrastructure. Organisations that fail to implement AI code transparency face growing risk, licence violations they cannot detect, IP exposure they cannot assess, and compliance gaps they cannot close. SCANOSS provides the detection capabilities, knowledge base coverage, and integration workflows needed to achieve and maintain AI code transparency. By identifying open source similarities at the snippet level where AI copying occurs, SCANOSS enables organisations to harness AI productivity gains whilst managing IP and compliance risk responsibly. The goal is straightforward: help organisations see, understand, and manage open source in AI-generated code, turning blind spots into visibility, compliance risk into controlled process, and AI adoption into sustainable competitive advantage.

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Getting Started with AI Code Transparency

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