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Artificial intelligence capabilities are now widely available through consumer tools, SaaS platforms, and developer APIs. This accessibility has accelerated adoption across enterprises, often outside formal approval and governance processes.
IBM has identified Shadow AI as a growing concern driven by employees and teams using AI tools with enterprise data without security oversight. These tools frequently process sensitive information, yet operate outside established data protection, audit, and compliance frameworks.
Shadow AI is an unsanctioned AI usage that expands the enterprise attack surface while remaining invisible to traditional security controls. This invisibility arises because AI interactions often occur through external services, embedded SaaS features, or unmanaged APIs.
Industry reporting and analysis indicate that Shadow AI usage is not limited to technical staff. Business users and executives increasingly rely on AI tools for productivity, analysis, and decision support, introducing risk in environments where AI usage is neither inventoried nor monitored.
Shadow AI is therefore not an edge case. It is a systemic visibility problem created by the mismatch between rapid AI adoption and existing security and governance models.
Why Shadow AI Exists in Modern Enterprises
Shadow AI exists because AI adoption is decentralized and frictionless. Employees and teams can access powerful AI capabilities without infrastructure changes or procurement cycles.
Many AI tools are delivered as cloud based services that require minimal setup. Users can submit text, documents, or data directly through web interfaces or integrations. These interactions occur outside enterprise networks and are not consistently logged or inspected.
SaaS platforms increasingly embed AI features into existing products. These features may be enabled by default or activated without explicit security review. Once enabled, they can process enterprise data without visibility into how models are invoked or how data is handled.
Developers also integrate AI services directly into applications and workflows. API based LLM services can be added quickly to support automation, analysis, or customer interaction. When these integrations bypass formal review, they introduce AI execution paths that are not governed centrally.
Governance frameworks for AI adoption often lag usage. Policies may exist, but enforcement mechanisms are limited when AI tools operate outside managed infrastructure. Shadow AI emerges in the gap between what is permitted on paper and what executes in practice.
What Is Shadow AI?
Shadow AI refers to AI systems, tools, models, agents, or workflows that operate within an organization without explicit approval, security oversight, or governance controls. These systems may be used by employees, embedded within applications, or enabled through third party platforms.
Shadow AI is defined by the absence of visibility and control rather than by intent. AI usage may be well intentioned and aligned with business goals, yet still qualify as Shadow AI if it operates outside approved frameworks.
Shadow AI includes several categories of activity. Employees may use external AI tools with corporate data. Developers may embed AI services directly into code or workflows without review. SaaS platforms may process data through AI features that are not governed by enterprise policy.
Shadow AI is distinct from Shadow IT. Shadow IT refers broadly to unapproved applications or infrastructure. Shadow AI specifically involves systems that ingest data, generate outputs, or influence decisions through probabilistic models. This distinction matters because AI systems can transform, infer, and act on data in ways that are not transparent or easily auditable.
Shadow AI should also be distinguished from sanctioned AI usage. Approved AI systems operate under defined governance, monitoring, and data handling controls. Shadow AI operates outside those boundaries.
How Shadow AI Is Introduced Into Production
Shadow AI is introduced through common operational pathways rather than through deliberate circumvention of controls.
Employees frequently adopt external AI tools to improve productivity. These tools may be used for summarization, analysis, drafting, or decision support. Data submitted to these tools may include internal documents, customer information, or proprietary content.
Developers integrate AI APIs into applications to enable new features or automation. These integrations may be added incrementally and deployed through standard CI/CD pipelines. When AI usage is not explicitly tracked, these execution paths remain undocumented.
SaaS platforms increasingly provide AI capabilities as part of core functionality. Once enabled, these features may process data automatically. Visibility into model behavior, data retention, and output handling is often limited.
Automation frameworks and AI agents introduce additional complexity. These systems may invoke models dynamically, chain multiple tools together, or execute actions based on model output. When deployed without oversight, they create autonomous AI behavior that is difficult to observe.
In each case, Shadow AI enters production because AI execution is treated as a feature rather than as a distinct security domain requiring dedicated controls.
Security and Data Risks of Shadow AI
Shadow AI introduces security and data risk because AI systems process information and influence outcomes without consistent oversight. These risks arise from how AI is used and integrated rather than from the presence of AI itself.
Sensitive Data Exposure
Sensitive data may be exposed when employees submit internal information to external AI tools. These submissions can include proprietary documents, customer data, or operational details. Data handling practices of external AI services may not align with enterprise requirements for privacy, retention, or data residency.
Unintended Data Processing
AI systems embedded in workflows may process data for purposes beyond those originally intended. Without visibility into model inputs and outputs, organizations cannot determine how data is transformed, combined, or inferred during execution.
This limits the ability to assess whether data usage complies with internal policies or regulatory obligations.
Expanded Attack Surface
Shadow AI expands the attack surface by introducing unmanaged dependencies. External AI services, embedded SaaS features, and autonomous agents create additional execution paths that are not covered by traditional application or network security controls.
These components may interact with internal systems in ways that are not documented or monitored.
Operational Risk From Unmonitored Outputs
Operational risk arises when AI outputs influence decisions or automated actions without oversight. Recommendations, classifications, or actions generated by AI may be incomplete, biased, or incorrect.
Without auditability, it is difficult to trace how AI output contributed to downstream outcomes or to correct errors when they occur.
Compliance and Governance Gaps
Compliance risk increases when AI systems operate outside approved governance frameworks. Regulatory requirements related to data protection, transparency, and accountability depend on the ability to observe and document data processing activities.
Why Traditional Security and Governance Miss Shadow AI
Traditional security and governance frameworks are not designed to observe or control AI usage that occurs outside managed infrastructure and approved application boundaries. Shadow AI operates in these gaps.
Focus on Approved Systems and Assets
Security programs are typically structured around known applications, managed endpoints, and sanctioned cloud services. Controls assume that systems of interest are registered, inventoried, and onboarded into monitoring pipelines.
Shadow AI operates outside these assumptions. External AI tools, embedded SaaS features, and developer integrated AI services are often not registered as systems requiring oversight.
Limited Visibility Into AI Interactions
Most security tooling does not capture how data is submitted to or processed by AI models. Network logs, endpoint telemetry, and application logs provide limited insight into model inputs, outputs, or execution context.
When AI interactions occur through web interfaces or third party platforms, telemetry is further reduced. As a result, AI usage is not observable at the level required for risk assessment.
Governance Models Built on Policy, Not Execution
AI governance efforts often focus on policy definition, usage guidelines, and approval processes. These mechanisms describe acceptable behavior but do not enforce or validate behavior during execution.
Without execution level visibility, governance relies on self reporting and adherence rather than observation.
Embedded AI Features as Black Boxes
Many SaaS platforms embed AI features into existing products. These features may process enterprise data automatically once enabled. Visibility into model behavior, data retention, and output handling is limited or unavailable.
Security teams often lack the ability to audit or constrain these AI execution paths.
Separation Between AI Usage and Security Enforcement
AI adoption frequently occurs through business led initiatives rather than security led programs. Enforcement mechanisms remain focused on infrastructure and applications, while AI usage evolves independently.
This separation results in AI execution paths that are not subject to the same controls applied to other systems.
Traditional security and governance mechanisms are effective within their intended scope. Shadow AI exists outside that scope because it operates through execution paths that are not inventoried, instrumented, or continuously observed.
Shadow AI vs Related Concepts
Shadow AI is often conflated with adjacent concepts that describe unmanaged technology usage. While these terms overlap in practice, they represent different risk conditions and require different control approaches.
Shadow AI vs Shadow IT
Shadow IT refers broadly to applications, devices, or services used without organizational approval or visibility. Shadow AI is a specific subset of this phenomenon that involves AI systems capable of processing data, generating outputs, or influencing decisions.
The distinction matters because AI systems can infer, transform, and act on data in ways that traditional applications cannot. As a result, the impact of unmanaged AI usage is typically broader and more difficult to audit than other forms of shadow IT.
Shadow AI vs Unapproved SaaS Tools
Unapproved SaaS tools may store or process enterprise data, but their behavior is generally deterministic and constrained by application logic. Shadow AI systems introduce probabilistic behavior and model driven outputs.
An AI feature embedded in a SaaS platform may qualify as Shadow AI even if the underlying SaaS product is approved. The determining factor is whether AI execution is governed and observable, not whether the host application is sanctioned.
Shadow AI vs Experimental or Pilot AI
Experimental AI systems are often deployed in controlled environments for testing or evaluation. These systems may lack full governance but are typically limited in scope and exposure.
Shadow AI differs in that it operates in production environments, processes real data, and influences real outcomes without consistent oversight. The risk profile changes when AI execution is no longer isolated or temporary.
Shadow AI vs Prompt Injection
Prompt injection is a security risk that affects how AI systems interpret instructions at runtime. Shadow AI describes the absence of visibility and governance over AI usage.
The two concepts are related but distinct. Prompt injection can occur within both sanctioned and shadow AI systems. Shadow AI increases exposure to prompt injection because affected systems are not instrumented or monitored adequately.
Shadow AI vs Automation
Automation systems execute predefined logic based on explicit rules. AI systems generate outputs based on probabilistic inference and learned patterns.
Shadow automation introduces operational risk. Shadow AI introduces both operational and data risk because model behavior is less predictable and less transparent. Controls designed for automation do not fully address AI specific execution characteristics.
Clear differentiation between these concepts helps organizations apply appropriate controls. Shadow AI requires discovery and governance mechanisms focused on observing AI execution rather than managing application inventories alone.
Why Runtime AI Discovery Is Required
Shadow AI cannot be identified reliably through policies, approval workflows, or static inventories. These mechanisms describe intended usage rather than observing how AI systems are actually used in production.
Observing AI Usage as It Occurs
Runtime discovery identifies AI usage based on observed execution. This includes interactions with external AI tools, embedded SaaS AI features, developer integrated models, and autonomous agents. Detection is grounded in how data is submitted to models and how outputs are produced. This approach captures AI activity regardless of whether it is sanctioned, documented, or formally registered.
Identifying Unapproved Models and Workflows
Runtime discovery surfaces AI models, tools, and workflows that are not represented in governance records. This includes AI services accessed through browsers, APIs embedded in applications, and AI features enabled within third party platforms.
Identification is based on execution characteristics rather than procurement or approval status.
Understanding Data Flows Into and Out of AI Systems
AI risk depends on how data is processed. Runtime discovery observes data flows associated with AI usage, including the types of data submitted to models and the destinations of generated outputs. This enables assessment based on actual data handling behavior rather than inferred intent.
Continuous Discovery Rather Than Point in Time Review
AI usage patterns change frequently as tools are adopted, features are enabled, and workflows evolve. Point in time reviews do not capture these changes reliably. Runtime discovery operates continuously and reflects current execution state rather than historical declarations.
Aligning Governance With Execution
Effective governance requires visibility into how systems operate. Runtime discovery provides the factual basis required to apply policy, assign ownership, and prioritize controls for AI usage that exists in practice. Shadow AI persists when governance relies on policy definition without execution level observation.
How Levo Enables Shadow AI Discovery and Control
Levo addresses Shadow AI by observing how AI systems actually run in production and relating that behavior to security and governance requirements. The focus is on execution rather than on declared usage, approvals, or inventories.
AI Runtime Visibility
Levo observes AI interactions directly in production environments. It records when models are invoked, where input data originates, and how generated outputs are handled across tools, platforms, and workflows. Visibility is based on how AI systems execute rather than on configuration files or documentation.
AI Detection
Levo identifies AI usage that does not appear in approved governance records. This includes use of unregistered models, activation of unmanaged AI features, and operation of autonomous or agent-driven workflows. Detection relies on execution patterns rather than self-reporting, manual inventories, or policy attestations.
AI Monitoring
Levo tracks AI behavior over time. This includes how frequently models are used, how they are accessed, and the context in which execution occurs. Monitoring applies across AI usage regardless of approval status and is grounded in observed behavior.
Sensitive Data Observation
Levo’s AI Runtime Visibility observes how AI systems interact with data during execution. Observed data interactions are correlated with governance context by AI Monitoring and Governance, showing where sensitive or regulated data is processed by AI systems outside approved controls.
Governance and Policy Correlation
Levo’s AI Monitoring and Governance compares observed AI execution behavior with governance criteria defined by the organization. Differences between expected controls and observed behavior are identified by comparing runtime observations with policy definitions.
Conclusion: Why Shadow AI Requires Runtime Visibility
Shadow AI exists when AI systems operate outside visibility and governance boundaries. This condition arises as AI capabilities are adopted across tools, teams, and platforms without consistent execution-level oversight.
Design time controls and policy frameworks define acceptable use. They do not describe how AI systems behave once deployed or how data is processed during execution.
Runtime visibility represents AI usage based on observed behavior rather than intent. This representation is necessary to identify Shadow AI, characterize associated data flows, and apply governance consistently.
Shadow AI persists when governance models are detached from execution reality. Runtime visibility aligns security and governance controls with how AI systems operate in production environments.
Levo delivers full spectrum AI Security Testing with Runtime AI detection and protection, along with continuous AI Monitoring and Governance for modern organisations giving complete end to end visibility. Book your Demo today to implement AI security seamlessly.
