What Is Shadow AI? Preventing and Managing AI Risks

The exploding popularity of generative artificial intelligence (GenAI) has brought about a new set of challenges for organizations. One of the most pressing issues is the unmonitored use of AI tools by employees, often without their employer's approval. A staggering 38% of workers admit to sharing sensitive work information with AI tools without authorization.

What is shadow AI?

Shadow AI refers to the use of AI tools without the knowledge or oversight of the IT department. In software development, this can manifest in various ways, such as using GenAI tools to write code snippets, debug issues, generate, or summarize documentation, or analyze data, all without formal review or approval.

Common AI tools in shadow AI

Shadow AI can take many forms, fueled by the powerful tools and platforms now readily available to today’s workers. Developers, in particular, have a wide range of AI resources at their fingertips, enabling them to build, automate, and deploy outside the visibility of IT. ome of the most common tools include: 

• Large language models (LLMs): Tools like OpenAI's ChatGPT, Anthropic's Claude, Google's PaLM, Meta's LLaMA, Mistral, and Cohere are used to generate or improve code.. 

• GenAI tools and platforms: Platforms like Hugging Face Transformers for pretrained models, ChatGPT via the OpenAI API for integration, and LangChain for orchestrating LLMs in complex workflows.

• Code and development tools: Tools such as GitHub CoPilot, Cursor, and Codex are used to generate code and complete tasks without IT oversight. 

• Task automation: Tools like Zapier and Make are used to automate workflows between AI tools, potentially linking multiple unapproved tools.

• Custom tools: Developers may fine-tune custom models on proprietary or sensitive data using local resources or open-source frameworks. 

While shadow AI can accelerate innovation, it also introduces significant risks.

Risks and concerns of shadow AI

Shadow AI skirts organizational guardrails, creating risks to data, systems, operations, and intellectual property. These risks include:

Creating complacency 

Developers often trust AI-generated code, which can be vulnerable. Despite the fact that a third of AI-generated code has common weaknesses across multiple languages, 75.8% of developers believe it is more secure than human-written code. As a result, developers often accept it without taking additional measures to identify bugs and vulnerabilities. 

Producing hallucinations and errors 

AI hallucinations, where unvetted models can produce misleading or outright false information without oversight, are a significant concern. Hallucination rates can range from around 1% up to 30%, depending on the LLM used. Alongside hallucinations, model reasoning and output errors continue to pose significant risks. 

Introducing data risks  

Developers can inadvertently compromise data privacy by inputting confidential data into LLMs, spinning up and abandoning data stores, oversizing AI data training sets, or transmitting data insecurely. For example, in 2023, Samsung’s semiconductor division experienced three data leaks after engineers used ChatGPT to check source code and perform other tasks. 

Violating compliance requirements  

Unauthorized use of data for model training or inputting unapproved tools can expose companies to regulatory violations. For example, mishandling sensitive personal data without proper consent or security measures could breach regulations like GDPR or CCPA, leading to substantial fines, legal action, and significant damage to the company's reputation.

Machine learning and shadow AI

As organizations increasingly develop and deploy their own AI and machine learning (ML) models, the risks associated with shadow AI are growing. ML models can expose corporate strategies, business priorities, and operational processes if compromised.

• Lack of monitoring: Unmonitored models may provide biased or substandard output, impacting performance. 

• Version control issues: Without proper version tracking, teams may unknowingly use outdated or inconsistent models, making it difficult to trace changes or justify decisions.

• Limited explainability: Poorly managed models often lack transparency or explainability, complicating efforts to understand, validate, or defend model-driven decisions—especially in regulated industries. 

Data management and shadow AI

As AI tools become more accessible, employees will continue to adopt new solutions to boost productivity, often without considering security risks. While training and policies can be put in place to reduce shadow AI, organizations should also focus on strengthening data management and security to mitigate risks.

Specifically, software and security leaders should establish a risk-based vulnerability management program. Steps include:

• Identifying assets: Creating an up-to-date record of all assets and dependencies to understand vulnerabilities and create a comprehensive view of the attack surface. 

• Assigning responsibilities: Ensuring asset owners proactively manage and report on assets, including changes in their risk status.

• Identifying compliance and regulatory requirements: Determining which regulations apply (such as GDPR, HIPAA, and PCI-DSS) and using developer-first security tools to integrate security and compliance into development workflows. 

• Classify assets and vulnerabilities: Performing risk assessments to evaluate the potential impact of vulnerabilities, developing risk profiles, and prioritizing remediation efforts.

• Creating a culture of continuous improvement: Educating developers on the risks of using unapproved GenAI tools and the importance of proactively searching for bugs and vulnerabilities in AI-generated code. 

How to limit the risks of shadow AI 

To combat the growing risks of shadow AI, leading organizations are putting guardrails in place to regain control. Some of the most effective actions that can be taken include: 

• Assessing risks: Evaluate security gaps, such as inadequate encryption, misconfigurations, and vulnerabilities that increase data exposure risks.

• Creating AI policies and controls: Collaborate with IT and security teams to develop usage policies, implement data-handling procedures, and conduct regular compliance audits for AI tools.

• Scanning for vulnerabilities: Use tools like Snyk Code, a developer-centric security tool, to scan both human- and AI-generated code and automatically identify issues with fix recommendations.

• Developing an incident response plan: Establish clear procedures to respond quickly to shadow AI incidents, minimizing the impact on data, systems, and business operations.

Prevent shadow AI with strong security processes 

Developers often self-provision resources when their needs aren’t met. To reduce these risks, security teams should collaborate closely with developers to provide approved tools, clear guidance, and guardrails that don’t impede productivity.

Snyk helps bridge this gap by integrating directly into developers’ daily workflows, making it easy to identify and fix vulnerabilities early, even in AI-generated code.

Book a demo to see the power of Snky for securing AI-generated code.