Shadow AI in the Enterprise: Managing Unsanctioned Models and Deep Learning Risk

What is Shadow AI? 

Shadow AI refers to the unsanctioned, unmonitored, and often unknown use of AI tools and services within an organization, such as ChatGPT. It’s similar to “Shadow IT,” which entails employees downloading unauthorized software or using personal devices for work. With AI, this phenomenon takes on a far more complex and potentially devastating form.

Shadow AI typically arises when employees leverage readily available AI tools, such as a generative AI chatbot for drafting emails, an online code assistant, or a public machine learning model for data analysis, without the IT department’s knowledge or approval. In their quest for efficiency, employees often seek quick fixes and shortcuts, unknowingly creating security blind spots that can compromise the organization

Defining the “Shadow” in Shadow AI

The “shadow” isn’t about secrecy in a malicious sense, but rather a lack of visibility and knowledge of an AI approval process. The “shadow” includes several characteristics:

• Unintentional blind spots: Employees often create unintentional security vulnerabilities by seeking quick fixes and shortcuts, inadvertently compromising the organization.
• Implicit Functionality: The AI is integrated so deeply into a product or service that its AI nature isn’t highlighted or even mentioned. It just works.
• Background Operation: It runs behind the scenes, processing data, making decisions, or automating tasks without requiring direct input from the user.
• Unacknowledged Influence: Its impact on our choices, information consumption, or digital experience isn’t always recognized or understood by the user.
• Pervasive Integration: Rather than being a standalone AI tool, it’s often a component within a larger system—a feature enhancing an existing product.

Consider how your home’s smart thermostat learns your schedule to ensure the house is the perfect temperature when you get back from work, or the predictive text feature on your phone. These are not marketed as “AI systems” per se, but their core functionality is driven by intelligent algorithms. The “shadow” concept comes from the fact that we rarely pause to consider the intricate AI mechanisms making these capabilities possible.

The Invisible Mechanics: How Shadow AI Operates

Shadow AI utilizes various AI techniques like machine learning, deep learning, NLP, computer vision, and recommendation engines to operate without explicit user awareness. It analyzes large datasets to identify patterns, make predictions, and implement actions. For instance, a recommendation engine might track your browsing history, purchase patterns, and even how long you hover over certain items, and use this data to predict what you may like next.

Why Should We Be Concerned About Shadow AI?

The invisible aspect of Shadow AI is a main concern for IT teams and overall organizational security. When AI tools and the use of AI are adopted without proper vetting, this can expose them to a range of AI risks, from minor inefficiencies to significant data leaks and legal liabilities.

Even with good intentions, employees may use powerful new online tools to handle sensitive company information, such as customer data. Without official oversight, the security, data handling, and accuracy of these tools cannot be verified, which can lead to numerous potential issues. The accessibility of modern AI tools, especially generative AI, makes them easy for almost anyone to use. This ease of access, combined with a lack of awareness about AI’s inherent risks, fuels the growth of “Shadow AI.” This makes Shadow AI a widespread and urgent concern for organizations across all sectors.

Top Security Risks Posed by Shadow AI

1. Let’s delve into the specific threats posed by Shadow AI, highlighting why each presents a critical area of concern.

1. Data Leakage and Privacy Breaches
   Arguably, the most significant threat associated with Shadow AI stems from employees inputting sensitive company data into unauthorized AI systems, such as customer lists or financial records. This data often bypasses the company’s secure network, as public AI services might utilize it for training purposes, potentially exposing confidential information. For example, using a public Large Language Model (LLM) to summarize a confidential client contract could result in the details becoming discoverable or being used by the LLM, which could lead to privacy breaches and reputational harm.
2. Compliance and Regulatory Headaches
   Operating outside controlled environments, Shadow AI poses significant compliance risks. Regulations like GDPR, HIPAA, and CCPA impose strict data handling requirements, and if employees use unsanctioned generative AI tools that process sensitive data non-compliantly, organizations face fines, legal challenges, and eroded trust. Proving compliance becomes impossible when the AI tools touching your data are unknown, creating a critical gap in data lineage during audits.
3. Model Drift and Inaccurate Outputs
   Sanctioned AI models are continuously monitored and validated for accuracy and to prevent bias, while Shadow AI models operate without this crucial oversight, leaving them susceptible to “model drift”—a decline in performance over time. Employees relying on unmonitored Shadow AI for critical business decisions risk using increasingly inaccurate or biased information, potentially leading to misallocated resources, ineffective campaigns, and lost revenue.
4. Intellectual Property Theft
   Your organization’s intellectual property (IP), including trade secrets and proprietary algorithms, faces a significant risk of theft through Shadow AI. For example, an engineer using a public AI code assistant to optimize proprietary algorithms could inadvertently expose unique code to the AI model, which might then learn from it and replicate similar structures for other users. Similarly, an employee using a public AI translation tool for internal company documents risks exposing confidential business strategies if the AI model learns proprietary terms or phrasing from that data, especially if it’s later used to train the public model.
5. Operational Inefficiencies and Redundancy
   Shadow AI, while benefiting individual tasks and personal productivity, can harm your company by creating silos. Different teams using unapproved AI tools for similar problems can lead to duplicate efforts, wasted resources, and fragmented AI adoption. This can prevent a unified, secure, and efficient AI solution, hindering cost savings and best practice sharing.
6. Security Vulnerabilities and Attack Vectors
   Unsanctioned AI tools lack security testing, introducing vulnerabilities. They can have weak authentication, known AP flaws, or malicious code. When integrated, these tools create new attack vectors, allowing attackers to access machines, escalate privileges, or move laterally within networks, potentially bypassing defenses as an invisible Trojan horse.

How to Unmask and Mitigate Shadow AI

Instead of stifling innovation, proactively mitigating Shadow AI means guiding it securely and strategically. Here’s how to gain visibility and control.

1. Establish Clear AI Governance Policies
   Begin by developing comprehensive governance policies that clearly define acceptable AI use, sanctioned tools, and the protocols for requesting and vetting new AI solutions. These policies must encompass data handling, ethical considerations, and security requirements. Ensure these policies are communicated clearly and consistently throughout the organization, so everyone understands their responsibilities. This proactive approach establishes essential guardrails before any unauthorized AI use occurs.
2. Implement AI Discovery and Monitoring Tools
   You can’t secure what you can’t see. Invest in tools specifically designed to discover and monitor AI usage across your network. These solutions can identify API calls to public AI services, analyze network traffic for AI-related activities, and scan endpoints for unsanctioned AI applications. This proactive monitoring acts like a radar system, detecting hidden AI activity and alerting security teams to potential Shadow AI instances. Some Data Loss Prevention (DLP) solutions are evolving to include AI detection capabilities.
3. Foster a Culture of AI Awareness and Education
   Equip your employees with the knowledge they need to be your first line of defense against Shadow AI. Regular training sessions are crucial for educating staff on the risks of unsanctioned AI tools, proper data handling, and the potential for data leakage, compliance breaches, and IP theft. By understanding the “why” behind these policies, employees are more likely to comply and proactively report potential Shadow AI, rather than inadvertently contributing to the problem.
4. Centralize AI Tooling and Resources
   Instead of letting teams find their own AI solutions, create a centralized “AI hub” or platform. This could be an internal platform that offers a selection of pre-vetted, secure, and compliant AI tools, or a streamlined process for requesting and integrating new tools. By providing secure, easily accessible, and officially supported AI resources, you reduce the incentive for employees to seek out unsanctioned alternatives. Make the compliant path the easiest path.
5. Conduct Regular AI Security Audits
   Proactively review your AI ecosystem, both sanctioned and discovered Shadow AI. These audits should assess the security posture of AI models, their data inputs and outputs, access controls, and compliance with internal policies and external regulations. Penetration testing specifically tailored for AI systems can uncover vulnerabilities in models or their integrations. Think of these as regular health checks for your AI initiatives, ensuring they remain robust and secure.
6. Prioritize Secure AI Development Practices
   For any internal AI development, embed security from the ground up. Implement secure coding practices for AI, rigorously test models for robustness against adversarial attacks, ensure data privacy by design, and employ techniques like differential privacy and federated learning where appropriate. Secure AI is not an afterthought; it’s an integral part of the development lifecycle. This principle extends to evaluating third-party AI solutions, demanding transparency on their security measures.

Sensitive information exposure 

Say, for example, a sales representative pastes a client contract into an AI tool to help summarize key points for a meeting. Without realizing it, they’ve potentially exposed confidential pricing structures, client information, and proprietary terms to servers outside the company’s control. This data could be incorporated into the AI’s training data or accessed by unauthorized parties. 

The act itself sounds innocent enough, but this type of inadvertent data leakage represents one of the most significant risks associated with shadow AI. 

Why traditional bans don’t work on shadow AI 

Many organizations addressed shadow AI by implementing bans on tools like DeepSeek. Governments and entire countries like Italy have taken steps to block certain AI platforms to protect against shadow AI risks. 

Traditional corporate bans, however, have proven difficult to enforce for several reasons: 

• Employees find workarounds when they believe AI will help with productivity 
• Personal devices and home networks provide alternative access points 
• The growing number of AI tools makes comprehensive blocking impractical 
• Employees may not understand the security implications of their actions 

The unique challenge of open-source AI 

Unlike concerns about applications like TikTok or hardware from companies like Huawei, open-source AI tools present different security challenges. 

Open-source models: 

• Enable cybercriminals to launch massive campaigns more efficiently due to their low cost to train and run 
• Create challenges for organizations looking to identify when and how these tools are being used 
• Has code that can be modified and deployed in ways that evade detection 
• Increase vulnerability to targeted attacks due to the open-source models’ transparency 

Effective shadow AI risk mitigation strategies 

It’s not all doom and gloom; organizations can still reap the benefits of AI. Rather than blocking or banning all AI tools, organizations can implement these strategies to manage shadow AI risks while leveraging AI’s benefits. 

Develop clear AI policies 

Organizations can start mitigating AI risk by establishing and communicating clear guidelines about approved AI tools and usage. 

Typical policies include:  

• Create specific protocols for handling sensitive information 
• Define consequences for unauthorized AI tool usage 
• Establish clear channels for requesting access to new AI tools 
• Update data classification policies to account for AI-specific risks 

For example, a marketing team might develop guidelines that allow the use of approved AI tools for brainstorming campaign concepts but require human review before implementing any AI-generated content. 

Offer secure alternatives 

When employees turn to shadow AI, it often indicates they need capabilities not provided through official channels. 

To combat this, organizations should: 

• Consider building isolated instances using open-source code 
• Evaluate enterprise-grade AI solutions with proper security controls 
• Implement walled-off versions that don’t connect to external servers 
• Create internal AI sandboxes where employees can experiment safely 

Software development teams, for example, can benefit from internally hosted coding assistants that help with tasks without exposing proprietary code to external AI platforms. 

Prioritize employee education 

Many shadow AI risks stem from a lack of awareness rather than malicious intent. It’s the responsibility of organizations looking to implement AI to: 

• Educate staff about data security risks associated with AI tools 
• Provide clear alternatives to unauthorized AI apps 
• Explain the implications of sharing sensitive information with AI models 
• Create simple decision frameworks for when AI use is appropriate 

Implement technical controls 

Technical solutions offer strong capabilities in managing shadow AI. To secure their environment for AI, organizations can:  

• Deploy Data Loss Prevention (DLP) tools to identify sensitive data being shared with AI platforms 
• Use DNS and web proxy monitoring to detect unauthorized AI usage 
• Implement least privilege access to minimize potential exposure 
• Regularly audit application integrations to identify shadow AI tools

Enabling shadow AI innovation, but with security 

The key to managing shadow AI effectively in an organization lies in balancing enabling innovation and maintaining security. Finding this balance means: 

• Creating clear pathways for employees to request new AI capabilities 
• Establish risk assessment frameworks specifically for AI tools 
• Regularly review and update AI policies as the technology evolves 
• Involve business units in AI governance decisions 

The Future of Shadow AI: What’s Next?

Shadow AI, or AI models that operate in the background of our everyday tools, will only become more prevalent as AI capabilities advance and become easier to integrate. This will lead to more personalized experiences, but also presents the challenge of balancing seamless integration with user awareness and control. Future developments will likely focus on “transparent AI by design,” with built-in explainability and privacy. Regulations will also continue to evolve to set standards for data collection, usage, and the extent of AI influence without explicit user consent.

Ultimately, the rise of Shadow AI isn’t a sign of inherent danger, but rather a call for proactive risk mitigation. By understanding the risks, implementing strong governance, and educating the workforce, we can responsibly guide innovation and harness the power of AI securely. In alignment with the Cybersecurity and Infrastructure Security Agency (CISA)’s 2025 Cybersecurity Awareness Month theme, Building a Cyber Strong America, organizations must ensure that the benefits of AI don’t come at the cost of security.