What is shadow AI?
Shadow AI is an unauthorized use of any artificial intelligence tools for work purposes that is outside the organization’s AI policy, AI governance, oversight, and/or knowledge. Simply put, when employees use generative AI tools like ChatGPT to perform any work duties without IT's permission, it compromises the security of their entire company.
Security risks associated with large language models (LLMs) are an unfortunate but unavoidable consequence of rapid AI adoption and hype. Teams rush to implement new AI tools for efficiency, but end up using unapproved shadow AI systems, exposing security vulnerabilities.
Examples of shadow AI
The use of unauthorized AI tools isn’t exclusive to just one team, task, or business function. Shadow AI happens across the board, with different levels of security risks. Let’s review a few key examples and the AI risks associated with each.
Data
It’s no wonder AI, including shadow AI, is rampantly used in data analysis and reporting – it drastically reduces data processing time and has the potential to uncover hidden patterns and insights, otherwise undetected by the human eye.
However, data analysis is also one of the most high-risk generative AI use cases. It can directly lead to exposing company data to LLM training algorithms, violating regulatory compliance, and data exposure of internal and customer records to malicious actors.
Communications
Shadow AI tools pose significant risks even when used for something as seemingly minor as drafting an email, an internal message to a colleague, or a client newsletter. To generate text, AI systems process input – details you tell AI to interpret.
Incremental pieces of sensitive data from various employees or chats can be compiled together to track patterns and compile a bigger picture of sensitive data. The use of AI tools for communications poses security risks, particularly because it’s used across teams, from engineering to HR to marketing.
Coding
Using shadow AI for coding is one of the most straightforward, but also widely used data security risks. Engineering employees can potentially leak proprietary code to large language model training algorithms or expose it to hackers.
Using AI-generated code without proper review and quality assurance can also make the entire codebase more susceptible to security risks and data leaks.
HR and Talent Acquisition
Unauthorized AI use by human resources and talent acquisition teams is one of the core risks of shadow AI. HR might process sensitive data of employees or use AI tools to review resumes and make hiring decisions based on LLM feedback.
This directly violates regulatory compliance and the legal framework around AI. Proper use of AI technologies in the hiring and people management processes requires a thorough AI security assessment.
Main risks of shadow AI
Risks of shadow AI continue to grow as AI adoption accelerates and organizations implement machine learning and generative AI into almost every aspect of their business. The main risks of unauthorized AI tools are reputational damage, data breaches, compliance risks, security vulnerabilities, and misinformation. Let’s explore each of these below.
Data leaks and breaches
Primarily, AI tools used without IT teams’ permission and oversight endanger company data and potentially lead to data leaks. Unapproved AI tools are, ultimately, Shadow AI and pose the same risks as any other violation of security protocols: data leakage, data breaches, and other emerging threats.
Security vulnerabilities
Using AI tools outside the governance framework ultimately makes the entire tech stack more vulnerable to attacks, hacking, and exploits. Whether proprietary code is leaked or vulnerabilities are introduced to the product via AI-generated code, both put the entire organization at risk.
Compliance risks
Specifically for compliance-heavy industries like finance, healthcare, and tech dealing with customers’ personal information, adhering to AI regulations is crucial. Employees using unapproved AI tools compromise customer data and can even be illegal and violate AI ethics.
Misinformation
Using unsanctioned AI tools also holds an indirect threat of misinformation. Unapproved and unverified LLM tools are more likely to produce AI hallucinations, especially if they’re not fully grounded in company data.
How to spot shadow AI
At the general level, there are two main ways for security teams to manage shadow AI and spot AI models used without authorization: technical and strategic.
From a technical standpoint, security teams can use special software and enterprise AI governance solutions to flag data security breaches and AI systems connecting to internal tools without permission. Enterprise AI platforms, like nexos.ai, offer specialized features to automatically detect and manage shadow AI use and to automatically detect AI tools used without employer permission. Security teams can restrict access to these AI tools to prevent exposure of sensitive data.
However, simply tracking and restricting employee access to AI tools is not a remedy. Shadow AI might be a signal that your teams desperately need AI models for work, but there’s simply no AI governance or internal AI tools to meet this demand. Potentially, your tech stack might be missing critical AI tools for innovation, but the approval processes are so complicated that your team members skip the red tape and go straight to the AI models they need via personal accounts.
To address this, use shadow AI as free internal research. Conduct team-wide confidential surveys and interviews to identify unauthorized AI use, question why employees don’t request these AI models via official channels, and let these insights guide your AI governance strategy.
How can enterprises avoid shadow AI
Minimizing shadow AI use in your organization comes down to several new strategies working together to both prevent security incidents from happening in the first place and reduce security risks after the damage is done.
Develop internal AI governance policy
Most importantly, it’s essential to address shadow AI head-on and develop a clear, comprehensive AI governance policy. Companies that implement AI with no strategy or rules see pilots fail and more shadow AI instances endanger their data. Make both rules and AI accesses clear and readily available to your entire organization.
Implement AI guardrails
As a part of the AI governance policy, invest in developing granular AI guardrails. Guardrail AI services will help you automatically monitor, track, and prevent sensitive information from being input to LLMs, or for certain outputs to reach your employees.
Implement AI monitoring and observability tools
To effectively manage shadow AI and mitigate the security risks, organizations must implement full LLM observability across their tech stack. This is a crucial defense mechanism that tracks every user query, model response, and API call, providing full transparency into AI usage, performance, and associated costs.
Dedicated enterprise AI governance solutions, such as nexos.ai, offer features that help security teams automatically detect unapproved model use, enforce granular guardrails, and secure the data pipeline. By gaining this comprehensive visibility, enterprises can turn the shadow AI challenge into a structured, manageable risk.