What is Shadow AI?

As the adoption of artificial intelligence (AI) models accelerates across business functions, employees and teams are increasingly experimenting with AI tools. From ChatGPT to Midjourney, Claude, and tools built into the apps they already use, they’re often engaging with Large Language Models (LLMs) without oversight, governance, or visibility. 

It’s given rise to a new relative of Shadow IT: “Shadow AI” refers to these unsanctioned activities that could introduce unseen risks into environments that were never architected to manage them.

The explosion of cloud-based AI tools makes this even harder to contain, and represents an expanding, ungoverned AI surface that stands to undermine core security, compliance, and data governance goals. We’ve looked at AI from a lot of perspectives: generative AI security, AI security posture management, and even AI threat detection.

This article explores what Shadow AI is, how it arises in today’s enterprise environments, and what that means for security teams racing to secure the cloud.

What Is Shadow AI?

Shadow AI refers to the unsanctioned or unmonitored use of artificial intelligence tools, models, or services within an organization. It includes employees using external AI APIs, SaaS-based AI features, or open-source models, practices that introduce risk by bypassing established governance and control frameworks.

By fall 2024, 75% of workers were using generative AI at work. 46% wouldn’t give it up, even if it were banned in the future.

Shadow AI is the next evolution of the shadow IT problem, but in some ways, it’s more unpredictable and potentially damaging. After all, models might expose sensitive information through prompts, amplify bias, leak intellectual property, or influence critical decisions without a transparent understanding of how they came to their conclusions.

And the environment adds to the risks that Shadow AI poses. In a world of cloud computing, teams can integrate LLMs, spin up inference APIs, or plug AI into production workflows in minutes. So while AI’s use is often driven by good intentions like productivity, innovation, and experimentation, Shadow AI bypasses the safeguards that make those outcomes secure.

How Shadow AI Occurs: Causes and Drivers

AI use in everyday life is becoming increasingly normalized. Employees and teams reach for AI tools because they solve real problems, unlock productivity, and, thanks to the cloud, are easier than ever to access. How does AI find its way into organizations? There are multiple routes, and that’s helping make it particularly tough to control.

The Consumerization of AI Tools

Just as shadow IT was driven by the rise of user-friendly SaaS platforms, Shadow AI is fueled by tools that are accessible, affordable, powerful, and require no setup. Employees can upload documents to ChatGPT, fine-tune models on open platforms, or embed LLM APIs into codebases without needing security reviews or infrastructure approvals.

Gaps in AI Governance and Policy

Even if security policies with dedicated AI mentions exist, they often don’t specify acceptable tools, approved models, or data handling rules for AI interactions. This ambiguity creates the illusion that AI use is permitted by default, which leads to unintentional misuse and increased exposure.

Cross-Functional Adoption Without Security Involvement

AI isn’t confined to technical teams. Marketing, HR, legal, and customer support increasingly adopt AI-driven tools and plugins without routing through IT desks. This decentralization of AI adoption makes traditional security models ineffective, as visibility and approval are no longer centralized.

Developer-Led AI Experimentation

Developers and data scientists often experiment with open-source models, third-party APIs, and pretrained datasets. These experiments often happen in unsanctioned environments, such as personal devices, unmanaged cloud platforms, or external workspaces, where oversight is limited. Without automated governance or runtime observability, these pipelines can drift into production without proper model validation, version control, or data lineage tracking.

Risks and Challenges of Shadow AI

Shadow AI introduces fundamental security gaps that are difficult to reverse. And because AI can both generate and act on data, misuse often escapes traditional controls. With AI tools, damages can spread to data inputs, model behavior, and outputs that influence decisions across the business. Here’s a breakdown of the most pressing challenges teams will have to face when Shadow AI takes hold.

Challenge Area	Example	Primary Risk	Security Gap
Tool Visibility	Employees use ChatGPT or Claude without reporting use	Sensitive data exposed in prompt history or third-party logs	No inventory or telemetry for external AI interactions
Lack of Approval Workflows	AI integrations bypass procurement	Risk of violating internal policy or external regulations	No enforcement of security architecture or usage policies
Third-Party App Features	SaaS Tools quietly launch embedded LLM features	Covert model behavior, unknown data exposure	Difficult to audit plug-in layers
Prompt and API Input Leakage	User pastes proprietary code into a chatbot	Prompt injection, data theft, legal exposure	No input sanitization or usage monitoring
Open-Source AI Experimentation	Developers deploy models trained on scraped data	Insecure models, legal risk, uncontrolled dependencies	No vetting, explainability, or Software Bill of Materials (SBOM) enforcement
IAM and Logging Bypass	External LLM endpoints accessed from unmanaged environments	No logs, no authentication, lateral movement risk	IAM and logging tools don’t cover external inference
Data Lineage and Residency Gaps	LLM trained on internal documents is later released publicly	Privacy violations, IP leakage, compliance breaches	No provenance tracking or training data audit trail

What makes Shadow AI most dangerous goes beyond the presence of unapproved tools. It’s how aggregate, decentralized, and invisible use can accumulate and layer risks together in ways that create system-wide exposures. For instance:

• A prompt input leak can become more serious when that input bypasses logging and identity controls, especially if the model retains or trains on it.
• A biased or manipulated output becomes a legal risk if it influences customer-facing decisions without proper human oversight or explainability.
• An AI-generated insight embedded in a business process can lead to operational drift, as decisions based on outputs are untraceable and cannot be validated.

AI risks don’t live in silos. They accumulate across environments, decision layers, and stakeholders. And they can reshape processes in irreversible ways, which often go unnoticed until something breaks or a public breach occurs.

Why Shadow AI Is Hard to Detect

The implications of Shadow AI beg the question: Why can’t we detect it before layered risks, legal exposures, or data breaches happen?

The short answer is that Shadow AI is uniquely difficult to detect because it doesn’t behave like traditional shadow IT. 

With Shadow AI, there’s no rogue server to find, no obvious unauthorized SaaS domain in your traffic logs. Instead, Shadow AI blends into legitimate workflows, like when developers call AI APIs over HTTPS, business users paste customer data into chatbots, or teams integrate pre-trained models into codebases via widely used SDKs. These actions often look like normal behavior unless you have the context to understand how and where AI is being used.

Another challenge is that AI consumption today is abstracted away by platforms. A user might not even realize they’re using AI, only that a tool “summarizes,” “autocompletes,” or “optimizes.” Many AI-driven features are embedded into SaaS platforms with no obvious labeling, logging, or admin controls. Even when APIs are used, they often don’t leave distinct telemetry unless explicitly monitored at the code or network level.

Finally, the explosion of open-source models and BYO AI tools compounds the problem. Teams can fine-tune and self-host models in containers or run them in ephemeral cloud instances using public datasets and GitHub code, all without tripping conventional alerting. Without runtime analysis of workloads, outbound API behavior, and anomaly detection tuned for AI activity, organizations are left flying blind.

Benefits of Shadow AI?

With so much risk, can there be any true benefits of Shadow AI?

While Shadow AI poses real risks, it also reflects a powerful opportunity: employees continue to adopt and embrace AI because it solves problems, accelerates workflows, and opens new creative possibilities. Shadow AI is a symptom of organic demand, showing:

• How the business wants to work and where AI can create real value
• Where policies are missing, controls are too rigid, workflows are too tedious or cumbersome, or legitimate innovation is bottlenecked by bureaucracy

Shadow AI can serve as a signal to modernize security architecture and engagement models, allowing security teams to shift from being gatekeepers to enablers of responsible AI adoption — but only if they can bring it out of the shadows.

Managing and Mitigating Shadow AI

The goal isn’t to kill innovation, but to reassert control and visibility in a way that balances flexibility with security. Here are the steps to take:

1. Establish Clear AI Usage Policies

Start by publishing and socializing a formal AI Acceptable Use Policy. This should define:

• What tools and platforms are approved
  
• What types of data may or may not be shared with AI systems
  
• Who is responsible for vetting AI models, APIs, and vendors
  
• Requirements for documentation, transparency, and oversight

2. Map and Monitor AI Usage Across the Organization

Treat AI use like any other attack surface. Deploy cloud-native monitoring tools, identity analytics, and network traffic inspection to detect:

• Unusual API calls to known AI services (e.g., OpenAI, Anthropic, Hugging Face)
  
• Outbound traffic to third-party AI providers
  
• Code commits or workflow changes that embed model integrations
  
• Use runtime observability platforms to correlate AI behavior with workload activity, identity access, and sensitive data flow

3. Integrate Shadow AI Into Risk Registers and Governance Reviews

Shadow AI should be treated as a live risk domain within the broader cyber risk governance program. Review it in security steering committees, risk registers, and compliance audits. 

• Quantify risk based on usage, data types involved, and business criticality
  
• Include Shadow AI in tabletop exercises and threat modeling
  
• Assign ownership across teams (not just security)
  

This embeds AI in the language and processes executives already understand.

4. Offer Safe, Sanctioned Alternatives

One of the most effective ways to reduce Shadow AI is to provide secure, enterprise-approved AI options. This might include:

• Internal LLM instances with audit logging and content moderation
  
• API gateways that route to vetted model providers
  
• Pre-approved AI plugins or SaaS tools integrated into existing systems
  

Providing people with the necessary tools, along with the right guardrails, makes them far less likely to stray from approved paths.

5. Educate, Don’t Just Enforce

Lastly, focusing on awareness and enablement is better than enforcement. Many employees using AI tools simply assume they’re allowed and are unaware of the risks. To foster conscientious and secure use of AI tools, provide:

• Regular training on data security in AI workflows
  
• Just-in-time education when risky behavior is detected
  
• Simple checklists for evaluating new AI tools or plugins

Future Outlook

Shadow AI is a signal of a fundamental shift in how technology is integrated into organizations. As generative AI becomes embedded in SaaS, DevOps pipelines, customer experience platforms, and business analytics, its use will expand rapidly and organically, well beyond what security leaders can manually approve or monitor. The sheer accessibility of AI, combined with the decentralized nature of modern enterprise IT ecosystems, means that Shadow AI will likely intensify before it stabilizes.

New risks will emerge, not just from prompt injection or data leakage, but also from model supply chain vulnerabilities, fine-tuning with poisoned data, or insecure model-to-model communication. As enterprises adopt more open-source or customizable models, threats might cross the line from AI misuse to full-blown AI-based compromise.

In the near future, we may see:

• Shadow ensembles: Teams stitching together multiple unsanctioned models, like chaining ChatGPT with internal vector databases or third-party classifiers, without shared oversight. These model pipelines will be even harder to audit, especially as decisions get distributed across inference layers.
• Silent drift: Fine-tuned models quietly diverging from their original training due to unauthorized updates. The risk won’t be from the base model, but from what it becomes without anyone noticing.
• Model exfiltration: Internal users could inadvertently export proprietary models, embeddings, or fine-tuned parameters to external collaborators or devices, creating a new class of IP theft not tethered to source code or documents.
• Inference-time compromise: Adversaries may begin targeting runtime AI use via manipulated prompts, payloads designed to subvert token prediction, or misused API access that drives output tampering in real time.

As AI becomes a co-processor throughout the workday, Shadow AI will continue to blur boundaries between user, system, and model behavior. That means teams will need to shift how they think about observability at the decision layer, asking not only what happened but also why the AI chose it.

It’s less about enforcing usage limits and more about policing trust boundaries around behavior. 

Upwind Brings Visibility to Runtime Behavior 

Shadow AI often spreads invisibly across teams and workflows, but in cloud environments, it leaves signals. Upwind helps bring those signals into view. It doesn’t track browser-based AI use on endpoints or embedded AI in SaaS tools, but it leaps into action when Shadow AI reaches your cloud: in unsanctioned inference APIs, model deployments in dev environments, and AI-powered pipelines running on serverless, containers, and virtual machines.

By combining runtime insights with identity, workload, and network data, Upwind detects when any organizational tools behave strangely, leak sensitive data where they shouldn’t, or expose cloud permissions they shouldn’t have. That gives security teams a way to see and respond to Shadow AI risks in places where it’s actually running. 

To see how Upwind discovers and protects cloud stack, get a demo.

FAQs

Is Shadow AI just another term for Shadow IT?

No, Shadow AI is a specific subset of shadow IT focused on the unauthorized use of AI tools, models, and APIs. While shadow IT involves unsanctioned technology in general, Shadow AI introduces unique risks related to data exposure, model misuse, and unmonitored AI-driven decision-making.

How does Shadow AI create regulatory compliance issues?

Shadow AI can lead to unintentional violations of data privacy laws and industry regulations by exposing sensitive data to unvetted AI services. Without oversight, it’s easy for teams to mishandle regulated data (e.g., PII, PHI) in AI models, violating frameworks like GDPR, HIPAA, etc.

Can Upwind detect LLM API usage in my cloud traffic?

Yes, Upwind uses runtime-powered monitoring to continuously analyze cloud workload behavior and outbound traffic. It can detect unauthorized LLM API usage, such as calls to OpenAI or Anthropic, providing immediate visibility into Shadow AI activities and helping enforce governance policies in real time. Upwind uses runtime monitoring to observe real-time behavior of cloud workloads, so if Shadow AI is originating there, it won’t matter that the traffic isn’t pre-approved or visible in IaC scans.

What cloud services are most at risk for Shadow AI?

Shadow AI risks are highest in serverless environments, containerized workloads, and API-driven applications, where AI services can be easily embedded without formal security reviews. Cloud-native development platforms and decentralized SaaS usage further increase exposure to unsanctioned AI integrations.

Shadow AI doesn’t require full-scale infrastructure, and can impact cloud architectures disproportionately due to their speed, decentralization, and lack of unified visibility.

Does Shadow AI impact DevOps speed or workflow integrity?

Yes, unmanaged AI integrations can introduce:

• Insecure code: AI can suggest vulnerable patterns, outdated libraries, or hardcoding of secrets, especially if it is used without validation
• Unstable dependencies: AI can introduce dependencies from unverified sources and make it hard to track or trust what’s running in production
• Pipeline drift: Developers can embed LLM API calls, auto-generated config files, or model weights directly into build or development steps, creating divergence from IaC and version control
• Compliance and licensing violations: Generated code may violate open-source licenses or data use policies without anyone realizing until post-deployment audits

Without governance, Shadow AI creates operational risks that compromise both workflow integrity and long-term maintainability, often resulting in hidden technical debt.