Shadow AI: Examples, Risks, and 8 Ways to Mitigate Them – Source: securityboulevard.com

What is Shadow AI?

Shadow AI refers to the unauthorized or unmanaged use of AI tools, models, frameworks, APIs or platforms within an organization, operating outside established governance frameworks. While employees may adopt these AI tools with good intentions, seeking to enhance productivity or solve problems more efficiently, the lack of oversight creates significant security, compliance, and operational risks.

Developers are rapidly integrating AI without the knowledge or oversight of application security teams. Virtually everyone has moved on from using AI solely as an internal tool and are deploying AI models. Many are experimenting with AI agents. Developers don’t ask the application security team what to do when it comes to AI use. This creates an environment where AI components operate in the shadows of an organization’s codebase, invisible to security oversight but fully functional within applications.

From an AppSec perspective, Shadow AI represents a significant blind spot in an organization’s security posture, as these unvetted AI components can process sensitive data, make automated decisions, and introduce vulnerabilities that remain undetected by conventional security scanning and testing processes.

While Shadow AI and Shadow IT share common attributes, they present distinct technical challenges that organizations must understand:

The key difference lies in the nature and potential impact of the technologies involved. While Shadow IT risks are typically contained to teams or specific applications, Shadow AI can have broader implications across an organization due to AI’s data requirements, learning capabilities, and potential to influence decision-making at scale.

Common Shadow AI implementation patterns include embedding AI model imports within application code, making external API calls to AI services, using machine learning libraries for custom models, and integrating vector databases for retrieval-augmented generation (RAG) patterns. Each of these implementation patterns introduces different security considerations that conventional security tools may not detect or evaluate properly.

Causes of Shadow AI

Several factors are contributing to the rise of Shadow AI in modern organizations:

Accessibility and integration simplicity

AI tools are now more accessible than ever, with many being free, inexpensive, or requiring minimal setup, allowing non-technical users to adopt them easily without IT involvement. 

Platforms like ChatGPT, Gemini, and Claude make AI readily available, while frameworks like AutoML and pre-trained models on Hugging Face simplify deploying advanced AI functionality. Modern AI services use simple REST APIs, enabling quick integration with minimal code, though this ease can bypass traditional security reviews. Tools like Mend AI are now essential to identify AI-related endpoints and ensure secure implementations.

Build pipeline and CI/CD integration

Modern development practices allow developers to integrate AI models directly into CI/CD pipelines without security oversight. AI models can be automatically downloaded from public repositories, embedded into applications, and deployed continuously, making it difficult for security teams to track and manage these components.

Containerization of AI workloads

Containerization has made deploying AI systems easier by packaging complete AI environments – including frameworks, models, and dependencies – in self-contained units. These containers can run on platforms like Kubernetes, but their encapsulated nature makes it hard for security teams to inspect their contents, leading to the rise of Shadow AI, where unmonitored AI components can operate undetected.

Productivity pressure

Employees use shadow AI despite its risks because it fosters innovation and allows quick experimentation with new tools, especially in fast-paced environments where time-to-market is critical. Developers and DevOps teams often rely on AI for tasks like code automation, scaling predictions, or incident analysis, bypassing formal governance when it feels too slow or restrictive. When approved tools fall short, workers naturally turn to alternatives that offer immediate productivity gains.

Lack of awareness

Developers often use AI without permission or informing security teams, while many employees underestimate the risks of unauthorized tools. Even security teams may wrongly assume that pre-trained public models carry no compliance risk, ignoring issues like data lineage, intellectual property, and regulatory exposure. This lack of governance enables “shadow AI,” where employees turn to unapproved tools due to unclear policies, insufficient training, and limited internal options. Without proper oversight, organizations face significant security, compliance, and accuracy vulnerabilities.

The risks of shadow AI

Data security

The greatest risk is the lack of protection for sensitive data. Research from Melbourne Business School in April 2025 found that 48% of employees have uploaded sensitive company or customer data into public generative AI tools, and 44% admit to using AI at work against company policies.

When employees use unauthorized AI tools, they may inadvertently share confidential business information, intellectual property, or regulated data with external systems. Once this data leaves the organization’s controlled environment, it becomes virtually impossible to track, manage, or protect.

Incidents like Samsung’s data leak in 2023, where engineers accidentally shared proprietary source code with ChatGPT while seeking coding assistance, illustrate the potential consequences of Shadow AI use. While LLMs are much more sophisticated today and have greater enterprise controls, incidents like that are still very real possibilities with Shadow AI that don’t have the enterprise security features built in. Such data exposures can lead to competitive disadvantages, regulatory violations, and damage to customer trust.

For AppSec teams, this creates a significant challenge as sensitive data might be processed by external AI services without proper security controls, data protection agreements, or compliance validation in a way they would never find out, until it’s too late. 

Model-specific attack vectors

Shadow AI models can also leak sensitive data in other subtle ways. Through prompt injection attacks, data leakage from training sets, or model inversion techniques. Imagine a code-generation LLM that was fine-tuned on proprietary code snippets but also trained on unvetted GitHub repositories containing malware.

AI models introduce unique attack vectors that traditional security scanning tools cannot detect:

1. Prompt Injection Attacks: Malicious inputs designed to manipulate AI behavior by embedding instructions that override system prompts or security constraints. These attacks can cause models to disclose sensitive information, generate harmful content, or bypass security controls.
2. Model Weight Poisoning: Tampering with model weights during training or fine-tuning to introduce vulnerabilities or backdoors. This can occur when using pre-trained models from untrusted sources or when training processes lack proper security controls.
3. Training Data Extraction: Extracting sensitive training data through carefully crafted queries that exploit the model’s knowledge of its training data. Through systematic probing, attackers can potentially reconstruct proprietary or sensitive information used to train the model.
4. Backdoor Triggers: Hidden functionality activated by specific inputs designed to cause the model to behave in unexpected ways when triggered. These backdoors can be inserted during model development or through supply chain attacks on model repositories.

API key management vulnerabilities

AI services typically require API keys that may be improperly secured in code repositories or configuration files. Developers implementing Shadow AI may inadvertently expose API keys in code, configuration files, or logs, creating security vulnerabilities.

Exposed API keys can lead to unauthorized usage, potential data breaches, and financial impacts through unexpected service charges. Since these implementations happen outside governance frameworks, standard security controls for secret management might not be applied, increasing the risk of exposure.

Data leakage through model inputs and outputs

AI models process data that may contain sensitive information. When these models operate outside governance frameworks, they can create data leakage risks. Developers might unintentionally send sensitive data to external AI services for processing, including personally identifiable information (PII), intellectual property, or confidential business information.

The data sent to external AI services may be logged, stored, or used for model training by service providers, creating significant data privacy and security risks. Many AI service providers retain data for model improvement, potentially exposing sensitive information beyond organizational boundaries.

Technical gaps

You cannot protect or govern the things you’re not aware of. Shadow AI components may not adhere to security standards like:

• Input validation and sanitization
• Output encoding and filtering
• Access control constraints
• Audit logging of AI operations
• Compliance with data residency requirements

This creates technical gaps in security posture that can be exploited by attackers. The lack of visibility means security teams cannot ensure these components meet organizational security standards or compliance requirements, creating potential blind spots in security posture.

Compliance violations

Sound security principles haven’t changed in the face of AI. Organizations must ensure public AI models don’t train on sensitive or proprietary data that could lead to unintentional data leakage.

As regulatory frameworks around AI continue to evolve, unauthorized AI usage creates significant compliance risks. Many governments are looking to tame the Wild West of AI development. The EU AI Act, which prohibits some types of AI applications and restricts many, went into effect on August 1, 2024, and imposes heavy compliance obligations onto the developers of AI products. In the United States, the most significant action is from Executive Order (EO) 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. 

Without proper governance, the outputs generated by these models might not align with the organization’s objectives or ethical standards. Biased data, overfitting, and model drift are examples of AI risks that can lead to poor strategic choices and harm a company’s reputation.

From a technical perspective, compliance requirements may include:

• Data residency and sovereignty controls
• Transparency and explainability documentation
• Bias testing and fairness evaluations
• Privacy impact assessments
• Security testing and vulnerability management
• Audit logging and traceability

Shadow AI, by definition, operates outside these governance frameworks, creating significant compliance risks for organizations subject to GDPR, HIPAA, and industry-specific requirements.

Operational disruptions

Unmanaged AI systems can pose serious risks to businesses. Here are the key challenges:

• Model Drift: Predictive models for tasks like capacity planning or fraud detection can degrade silently if inputs change, causing outages, SLA breaches, or customer harm.
• Lack of Monitoring: Unvalidated models in production without retraining or monitoring can lead to critical failures that go unnoticed until damage occurs.
• Operational Inefficiencies: Poorly integrated AI solutions can create technical debt, data silos, and incompatibilities with existing systems, making them unsustainable as needs evolve.
• Data Management Issues: Fragmented data across unauthorized AI tools can compromise accuracy, integration, and governance, leading to poor insights and bad business decisions.
• System Reliability Risks: Shadow AI implementations can undermine system reliability, performance, and maintenance, becoming critical points of failure for DevOps and operations teams. 

Examples of shadow AI

Shadow AI can take many forms across different organizational contexts:

Generative AI tools

One of the most common forms of Shadow AI is the use of generative AI platforms like ChatGPT, Claude, or Gemini. Employees might use these tools to draft emails, create content, generate code, or analyze data without organizational oversight. While these tools offer impressive capabilities, they also present significant risks when used to process sensitive business information.

A marketing intern might be pressured to create a press release quickly. Using ChatGPT for inspiration, they copy information containing confidential client details. While ChatGPT generates an impressive draft, the platform’s data policy allows it to retain user inputs for model improvements, meaning sensitive client information is now stored on external servers without the company’s knowledge.

From a more technical perspective, developers integrating GitHub Copilot into secure repo pipelines without secret scanning can lead to leaking internal API keys in auto-suggested code snippets. Similarly, engineers using something like OpenAI APIs for documentation generation, can accidentally expose internal project code names or roadmap items in generated content.

AI-powered code generation and review

As developers increasingly use AI to generate code snippets, SQL queries, or application logic based on natural language descriptions, they can introduce a number of risks. 

AI-generated code may contain security vulnerabilities, insecure patterns, or implementation errors that developers implement without proper review. The convenience of generating code through AI can lead developers to implement solutions without understanding the security implications or performing adequate security review.

Machine learning tools for predictive modeling

Data scientists and analysts may deploy machine learning models to analyze company data and generate predictions. These models might access sensitive information, create unvetted outputs that influence business decisions, or introduce biases and errors that go undetected without proper validation processes.

A data scientist eager to prove the value of predictive analytics for the sales department might use an external AI platform without understanding how it could result in biased recommendations that alienate certain customer segments.

From a technical perspective, these tools can introduce several risks:

• Unauthorized data access and processing
• Model drift and degradation without proper monitoring
• Biased or inaccurate outputs affecting business decisions
• Lack of model documentation and reproducibility
• Integration with unauthorized data sources or services

AI chat assistants

Teams may integrate AI chat assistants into customer service operations, internal support systems, or collaboration platforms without proper vetting. These assistants can introduce introduce several technical risks:

• Processing of sensitive customer inquiries without proper data protection
• Potential for social engineering or manipulation through adversarial inputs
• Lack of content filtering or safety mechanisms
• Inconsistent or incorrect responses leading to operational issues
• Integration with backend systems without proper security controls

AI browser extensions

Employees might install AI-powered browser extensions that promise to enhance productivity, summarize content, or automate tasks. These extensions often have broad permissions to access browser data, potentially exposing sensitive information or creating security vulnerabilities.

Browser extensions can introduce significant risks such as:

• Access to all browser content, including sensitive internal systems
• Data transmission to external services without proper security controls
• Potential for malicious extensions masquerading as AI tools
• Lack of update management or vulnerability patching
• Bypass of network security controls through browser-based operations

Embedding LLM workflows in applications

Developers often integrate large language models directly into applications without proper security review. A common implementation pattern is Retrieval-Augmented Generation (RAG), which combines vector databases, embedding models, and large language models to enhance AI responses with contextual information.

This pattern combines several AI components that each present security risks if not properly governed. The embedding process can leak sensitive data, vector databases may store information without proper access controls, and external LLMs might process and retain sensitive information without appropriate security measures.

Local model deployment

Developers may download and integrate open-source models from repositories like Hugging Face without security assessment. These models are often implemented directly into applications using machine learning frameworks, allowing developers to perform AI inference locally without external API dependencies.

These models may contain vulnerabilities, backdoors, or generate unexpected outputs that could compromise application security. Since these models often lack the scrutiny applied to commercial offerings, they may contain unintended behaviors or security issues that remain undetected during implementation.

Autonomous AI agents in DevOps workflows

DevOps teams may implement autonomous AI agents that interact with infrastructure, monitoring, or deployment systems. These agents can analyze system performance, detect anomalies, or even implement remediation actions without human intervention.

These agents may have excessive privileges or make infrastructure changes without proper oversight, creating security and stability risks. The autonomy of these systems can lead to unexpected behaviors or cascading failures if they operate without appropriate constraints and oversight.

Shadow AI: Detection and management strategies

Organizations need comprehensive strategies to address the challenges of Shadow AI:

1. Monitor AI Usage

The good news is that we have tools that detect AI in applications. The files and code of AI models and agents have certain characteristics that can be discovered by other AI models trained for that task. Likewise, these models can also detect the licenses of open source AI models.

For instance, a tool like Mend AI scans codebases, application manifests, and dependency trees for hidden AI components It then generates an awareness report (Shadow AI report) which provides a detailed map of AI usage across the organization, offering visibility into the volume of AI usage across different products, projects, and organizational units.

Implementing monitoring tools that can detect AI-related activities across networks, applications, and cloud services is a crucial first step. These monitoring solutions should be capable of identifying:

• AI-related API calls to external services
• Machine learning libraries and frameworks in applications
• Model files and AI components in container images
• Data transfers to AI services and platforms
• Vector databases and embedding services

2. Audit and Inventory of AI Tools

Conducting a comprehensive audit to identify all AI tools and models in use across the organization creates a baseline for governance efforts. This inventory should include details on what AI systems are being used, by whom, for what purposes, and what data they process. This should also include AI artifact discovery for model files, config files, training datasets, LLM fine-tuning checkpoints.

Then, take that audit and maintain an internal AI Asset Registry or a source-of-truth for every AI model and deployment.

Using a tool like Mend AI you can detect various AI technologies, including 3rd-Party LLM APIs like OpenAI and Azure, open ML models from registries like HuggingFace & Kaggle, and embedding libraries. This provides full visibility into the AI components used in your code, including Shadow AI, thus identifying and flagging instances of use not sanctioned from the registry.  This inventory provides critical visibility into the organization’s AI attack surface, enabling more effective risk assessment and mitigation strategies.

3. Establish Clear AI Policies

Employees need clear guidance on acceptable AI use, which makes a well-defined Responsible AI policy essential. This policy should outline the types of data that can be processed, prohibited activities, and security protocols everyone must follow.

These policies should address:

• Approved AI tools and platforms
• Allowed uss cases for AI 
• Data handling requirements and restrictions
• Security and privacy standards
• Compliance obligations
• Approval processes for new AI implementations
• Ethical guidelines for AI development and usage

4. Technical Implementation of AI Governance

AppSec teams need to implement comprehensive governance controls:

• CI/CD Pipeline Integration: Implementing AI security checks in CI/CD pipelines to detect and evaluate AI components during the build and deployment process. These checks can identify unauthorized AI components, validate security configurations, and enforce governance policies before deployment.
• Dependency Governance: Implementing AI-aware dependency controls that restrict which AI packages, libraries, and models can be used in applications. This includes approved repository configurations, version pinning, and automatic vulnerability scanning for AI components.
• Network Controls: Implementing egress filtering for AI API endpoints to control which external AI services applications can communicate with. This includes network policies, API gateways, and proxies that enforce access controls for AI service interactions.

5. Implementing Technical Guardrails

From a technical perspective, guardrails, as IBM notes, can include policies regarding external AI use, sandbox environments for testing AI applications or firewalls to block unauthorized external platforms.

Technical guardrails for AI usage include:

• Proxy Services for AI APIs: Implementing organizational proxies for AI services that mediate interactions between applications and external AI services. These proxies can enforce security policies, filter sensitive data, log interactions, and provide centralized governance for AI usage.
• Container Security Policies: Implementing policy engines like Open Policy Agent (OPA) to enforce security controls for AI workloads. These policies can restrict which AI models can be deployed, enforce security configurations, and ensure compliance with organizational standards.
• Secure AI Development Environments: Providing sanctioned environments for AI development that include pre-approved tools, libraries, and services. These environments can enforce security controls while providing developers with the capabilities they need, reducing the incentive to adopt Shadow AI alternatives.

6. Implement Access Controls

Organizations should implement role-based access controls (RBAC) for AI tools handling security-sensitive tasks and regularly audit input and output logs to detect potential data exposure.

Restricting access to sensitive data and implementing controls that prevent unauthorized data sharing with external AI services can significantly reduce Shadow AI risks. From a technical perspective, these controls might include:

• Data loss prevention tools that detect and block sensitive data transfers
• Network traffic filtering for AI service endpoints
• API gateways that enforce access controls for AI services
• Container security policies that restrict AI workloads
• Secure enclaves for sensitive AI processing
• Monitor for unauthorized use of AI model hosting platforms (e.g., AWS SageMaker, Azure AI).

7. Employee Education and Training

Educating employees about AI risks and best practices is one of the most effective ways to reduce shadow AI. Focus on practical guidance that fits their roles, such as how to safeguard sensitive data and avoid high-risk shadow AI applications.

Raising awareness about the risks of Shadow AI and providing training on proper AI usage is crucial for various teams. This education should cover:

• The security and compliance risks of unauthorized AI usage
• How to request and implement approved AI solutions
• Safe data handling practices when using AI tools
• The organization’s AI governance policies and procedures
• Secure development practices for AI components
• Ethical considerations for AI implementation

For development teams, this training should include practical guidance on secure AI implementation patterns, data protection techniques, and how to integrate AI capabilities within existing governance frameworks.

8. Incident Response Planning

Developing incident response protocols specifically for AI-related security incidents ensures the organization can respond effectively when Shadow AI leads to data exposures or other security breaches. These protocols should include:

1. Detection Mechanisms: Implement monitoring for AI-specific anomalies, such as unusual API usage patterns, suspicious data transfers, or unexpected model behaviors.
2. Isolation Procedures: Defining steps to isolate compromised AI components, including network isolation, service suspension, and containment measures. Cut API keys, revoke access tokens, snapshot affected resources.
3. Eradication: Remove unauthorized models, extensions, or services and clean residual artifacts from repositories, containers, and cloud storage.
4. Forensic Analysis Approaches: Develop specialized procedures for analyzing AI components, including model inspection, data flow analysis, and behavior evaluation. These tools can help security teams understand the nature and extent of security incidents involving AI systems
5. Remediation Steps: Establishing clear processes for addressing security incidents involving AI systems, including model updates, data recovery, and security enhancements.
6. Communication Protocols: Defining how to communicate AI-related security incidents to stakeholders, including regulatory reporting requirements.
7. Postmortem Analysis: Identify root causes of this issue. Training data gaps? Pressure for delivery? Lack of tooling? Update detection rules, policy documents, and access controls accordingly.

Shadow AI is a growing concern across organizations. Without systematic discovery, governance, and education, it’s likely these unmonitored models are already introducing unacceptable risks. It’s a matter of finding them, before bad actors do. 

Gain control over shadow AI risks with Mend AI

Tools like Mend AI provide visibility and control over hidden AI components inside your code and infrastructure, helping organizations move from reactive discovery to proactive management.

Last but not least, employ the fundamentals. Secure coding, risk management, compliance, and policy enforcement – haven’t changed. If your organization already follows secure coding practices, access control policies, and compliance frameworks, you’re well-equipped to handle AI.

By addressing Shadow AI with technical rigor and integrating governance into existing security frameworks, organizations can harness the benefits of AI while maintaining security, compliance, and operational integrity across the development lifecycle.

*** This is a Security Bloggers Network syndicated blog from Mend authored by Mend.io Team. Read the original post at: https://www.mend.io/blog/shadow-ai-examples-risks-and-8-ways-to-mitigate-them/