10 Best Shadow AI Discovery Solutions

Generative AI adoption is exploding across enterprises, but governance is struggling to keep pace. Employees now use AI through standalone tools, browser extensions, embedded SaaS features, copilots, and AI agents, often without IT or security approval. This growing gap between AI usage and oversight is what we call Shadow AI.

The challenge is visibility. Traditional IT asset management, CASB, and security tools were not designed to detect AI embedded inside SaaS products or accessed directly through browsers. As a result, organizations frequently lack clarity on which AI tools are in use, what data is being shared, and whether usage aligns with internal policies or regulatory requirements.

Shadow AI discovery solutions address this gap by continuously identifying AI usage across the enterprise. They form the foundation of enterprise AI governance, helping teams reduce risk, protect sensitive data, and enable safe AI adoption instead of uncontrolled experimentation.

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TL;DR

• Shadow AI discovery tools help uncover hidden AI usage across SaaS apps, browsers, and cloud environments.
• They give IT and security teams visibility into who is using AI, how, and with what data.
• Strong discovery enables better AI risk management, compliance, and governance.
• The right tool depends on SaaS sprawl, data sensitivity, and existing security stack.
• CloudEagle.ai stands out for unified SaaS and AI visibility with governance-ready insights.

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1. What is Shadow AI? And why do you need shadow AI discovery solutions?

Shadow AI is when employees use AI tools (like ChatGPT, image generators) without IT approval, often for productivity, creating huge risks like data leaks, compliance fines (GDPR, HIPAA), and IP loss, so businesses need Shadow AI Discovery solutions to find, monitor, and govern these unapproved tools to balance innovation with security, ensuring safe AI adoption.

2. What Are the Best Shadow AI Discovery Solutions in 2026?

Below are ten Shadow AI discovery solutions worth evaluating in 2026. Each tool takes a slightly different approach to AI discovery, governance, and risk management.

1. CloudEagle.ai

CloudEagle.ai is the first truly unified SaaS and AI governance platform built for modern IT, Security, and Procurement teams. In an era where over 60% of AI and SaaS tools operate outside IT visibility, CloudEagle enables enterprises to discover, analyze, govern, and remediate shadow AI usage, all from a single, intelligent platform.

Features

1. AI-Powered Discovery Engine

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• Detects AI-enabled SaaS tools, embedded GenAI features (like ChatGPT, Copilot), OAuth authorizations, and browser logins.
• Correlates identity, usage, and spend data to identify shadow apps and rogue AI usage, even those outside IDP/SSO coverage.

2. Unified SaaS + AI Visibility

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• Provides a centralized inventory of every SaaS and AI app in use, approved or not.
• Leverages over 500 direct integrations with SSO, HRIS, card data, browser extensions, and ITSM tools to give complete coverage.

3. Risk-Based Governance for AI Tools

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• Flags unsanctioned or high-risk AI tools automatically for review.
• Enables security and compliance teams to audit, restrict, or deprovision access to tools like ChatGPT, Midjourney, DeepSeek, etc..

4. Access & Privilege Reviews with AI

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• Auto-detects and alerts on privilege creep, orphaned access, and excessive privileges, common across shadow AI apps.
• Reduces review cycles by 80% and provides SOC2-ready audit logs.

5. Workflow Automation

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• Converts alerts into actionable remediation workflows via Slack, email, or integrated ITSM tools like Jira.
• Automates license harvesting, onboarding, and offboarding, even for apps not managed by IDP.

Pros

• Shadow AI + SaaS discovery in one platform.
• Governance-ready alerts, not just raw discovery.
• Deep visibility across login, spend, and usage vectors.
• Tailored for mid-market to enterprise scale (100 - 2000+ employees).
• Integrates with finance systems to detect shadow IT spend patterns.

Ideal For

CIOs, CISOs, IT Managers, and GRC leads who need to:

• Detects shadow AI before it causes compliance gaps or data leakage.
• Govern access to free AI tools adopted by Marketing, Sales, and Engineering.
• Meet audit requirements around access control and AI risk.
• Optimize spend on AI-enabled SaaS apps.

Pricing

• Custom-tiered pricing based on employee count and SaaS footprint.
• Includes AI discovery and shadow IT modules at no extra cost.

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2. BigID

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BigID extends its data intelligence platform into Shadow AI discovery, with a strong focus on data exposure, privacy risk, and regulatory compliance. It helps organizations understand how sensitive data is being accessed, processed, or shared by AI tools and models across the enterprise.

Key features

• Discovers AI models, tools, and services interacting with enterprise data
• Maps data flows between datasets and AI systems to identify exposure points
• Classifies sensitive and regulated data used in AI workflows
• Generates AI risk and privacy impact reports for governance teams
• Supports compliance with data protection regulations through audit-ready insights

Pros

• Excellent visibility into data-centric AI risk
• Strong fit for privacy-driven and regulated industries
• Helps connect AI usage directly to data sensitivity and exposure
• Mature data intelligence capabilities extend naturally into AI governance

Cons

• Limited context on SaaS-level AI usage and employee adoption patterns
• Higher setup and operational complexity compared to SaaS-focused tools
• More aligned to data governance teams than day-to-day IT operations

Pricing

• Enterprise pricing model
• Cost varies based on data volume, integrations, and governance scope
• Pricing available upon request

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3. LayerX

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LayerX specializes in browser-level Shadow AI discovery, focusing on AI tools accessed through web browsers, copilots, and extensions. It detects both sanctioned and unsanctioned AI tools used by employees in real time.

The platform monitors browser activity to uncover AI usage that traditional security tools often miss.
It provides identity-based visibility showing which users are accessing specific AI tools.
LayerX tracks data interactions and potential data exposure through browser-based AI workflows.

Pros: 

strong coverage of browser-based AI tools and extensions, effective detection of embedded AI accessed via the web, useful for organizations with browser-heavy workflows.

Cons: 

limited visibility beyond the browser layer, weaker coverage for backend AI systems or non-browser SaaS activity.

Pricing: 

quote-based, typically dependent on number of users and deployment scope.

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4. Reco

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Reco focuses on GenAI discovery across SaaS environments, with a strong emphasis on uncovering hidden OAuth connections and SaaS-to-SaaS AI integrations.

It identifies AI tools and embedded GenAI features operating inside approved SaaS applications.

The platform analyzes OAuth permissions to detect risky or over-privileged AI connections.
Reco maps AI agents and automated workflows that connect multiple SaaS tools.
It provides visibility into how data moves between SaaS apps through AI-powered integrations.

Pros:

Deep visibility into complex SaaS AI connections, strong OAuth and integration risk analysis, useful for organizations with large SaaS ecosystems.

Cons: 

Limited coverage beyond SaaS environments, less visibility into browser-only or infrastructure-level AI usage.

Pricing: 

Custom pricing based on SaaS footprint and integration scope.

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5. Acuvity

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Acuvity combines Shadow AI discovery with runtime data protection and policy enforcement to reduce AI-related data risk.

It detects approved and unapproved AI usage across enterprise environments. The platform monitors data flowing into and out of AI tools in real time. Acuvity enforces security and data-handling policies to prevent sensitive data exposure. 

Pros: 

Strong focus on prevention rather than just visibility, effective data protection for sensitive and regulated information, useful for security-led teams.

Cons:

Heavier security and operational overhead for some organizations, may require tighter configuration and ongoing management.

Pricing: 

Available upon request, typically based on deployment scope and security requirements.

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6. Auvik SaaS Management

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Auvik expands traditional SaaS discovery into Shadow AI by monitoring SaaS usage, OAuth grants, and AI-enabled extensions.

It identifies AI-enabled applications operating within the broader SaaS stack. The platform provides visibility into OAuth permissions that may expose data to AI tools. Auvik tracks application usage patterns to help uncover unapproved or under-the-radar AI apps.
It helps teams understand where AI fits into existing SaaS sprawl.

Pros: 

Easy to adopt for teams already using SaaS discovery tools, good baseline visibility into AI-enabled SaaS usage, useful for early-stage Shadow AI awareness.

Cons: 

Limited depth for AI-specific governance and risk controls, less suited for advanced AI policy enforcement.

Pricing: 

Varies by deployment size and feature requirements.

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Aiming to improve Identity & Access Management?

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7. Teramind

Teramind detects Shadow AI usage through real-time user behavior monitoring and data loss prevention controls.

It monitors user activity to identify interactions with AI tools and services. The platform applies behavioral DLP to prevent sensitive data from being shared with unauthorized AI tools. Teramind generates compliance logs to support audits and investigations. It helps organizations detect risky or anomalous AI-related behavior early.

Pros: 

Strong insider risk detection, effective prevention of data leakage through AI tools, useful for compliance-driven environments.

Cons: 

Potential employee privacy concerns, may feel intrusive in some organizational cultures.

Pricing: 

Tier-based, typically determined by user count and monitoring depth.

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8. Holistic AI

Holistic AI focuses on AI asset discovery and governance mapping rather than real-time usage enforcement. It helps organizations build and maintain an inventory of AI tools, models, and use cases across the business.

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The platform classifies AI assets based on risk, business function, and compliance impact.Holistic AI supports governance workflows, documentation, and reporting for audit readiness. It enables structured oversight for enterprise AI programs.

Pros: 

Strong AI governance and documentation capabilities, useful for policy-driven and compliance-led teams, clear visibility into AI ownership and accountability.

Cons: 

Limited real-time or user-level AI usage monitoring, less effective for detecting day-to-day Shadow AI activity.

Pricing: 

Enterprise-focused, typically based on scope of governance and number of AI assets.

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9. Portal26

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Portal26 emphasizes continuous GenAI discovery for emerging and fast-changing AI tools.

• It identifies new and unknown AI tools in real time as they appear in the environment.
• The platform monitors AI usage patterns to surface potential risks early.
• Portal26 adapts to changes in the AI landscape without relying solely on static allowlists.
• It is designed for environments where blocking AI tools is difficult or undesirable.

Pros: 

Highly agile in fast-evolving AI environments, effective discovery of newly launched or niche AI tools.

Cons: 

Narrower governance and policy management capabilities compared to broader platforms.

Pricing: 

Available on request, based on deployment scale and monitoring needs.

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10. Hybrid or Internal Discovery Stacks - Some organizations combine SaaS discovery, DLP, IAM logs, and network monitoring to uncover Shadow AI.

Hybrid or internal discovery stacks combine multiple tools to uncover Shadow AI.

Organizations typically use SaaS discovery platforms, DLP systems, IAM logs, and network monitoring together.

This approach allows teams to tailor discovery to their specific environment and risk profile. Manual audits are often used to supplement automated discovery.

Pros: 

High customization and control, flexible for unique or complex environments.

Cons: 

Higher operational complexity, ongoing maintenance effort, and slower time to value.

Pricing: 

Varies widely depending on the tools and infrastructure selected.

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3. How Should You Evaluate a Shadow AI Discovery Tool?

Start by understanding your real AI usage. Look at how many SaaS tools your teams use, how often new tools are adopted, and whether employees rely more on browser-based AI, embedded copilots, or AI agents. High SaaS sprawl and decentralized teams usually mean higher Shadow AI risk.

Then, evaluate detection coverage. A good Shadow AI discovery tool should be able to identify:
– Embedded AI inside approved SaaS applications
– Standalone AI tools and GenAI platforms
– Browser-based AI tools and extensions
– OAuth integrations and SaaS-to-SaaS AI connections
– AI agents and automated workflows

Next, focus on visibility and context. Detection alone isn’t enough. The tool should clearly show who is using AI, how frequently, which teams are involved, and what type of data may be shared. User-level insights and easy-to-understand reports make discovery actionable.

Integration is critical for turning insights into action. Make sure the solution works with your existing IAM, DLP, SIEM, and compliance tools so AI risks can be governed through familiar workflows instead of creating new operational silos.

Finally, assess scalability and rollout. The right tool should support global teams, regulated data, and audit requirements without adding heavy overhead. Pilots or phased deployments help validate accuracy, ease of use, and long-term fit before rolling it out enterprise-wide.

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4. What Features Define a Strong Shadow AI Discovery Solution?

• Broad Detection: Covers AI across SaaS apps, embedded features, browser tools, extensions, OAuth integrations, and AI agents to avoid visibility gaps.
• Real-Time Discovery: Continuous or near real-time scanning with alerts to catch new AI tools and changes promptly.
• Clear Usage Visibility: Shows who is using AI, which tools, frequency of use, and type of data shared; provides user- and team-level context for governance.

Risk Management Features:

• AI risk scoring or classification based on data sensitivity and usage
• Policy enforcement to control or flag risky behavior
• Audit logs and reporting for compliance and investigations
• Governance workflows assigning ownership and next steps

Outcome: Moves organizations from awareness to control, enabling responsible and compliant AI adoption.

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5. Conclusion

Shadow AI discovery is now a foundational requirement for enterprises adopting AI at scale. Without visibility, organizations cannot manage risk, protect data, or enforce governance. In 2026, dedicated Shadow AI discovery solutions make it possible to safely enable AI innovation while maintaining control.

Platforms like CloudEagle.ai go beyond detection by combining SaaS visibility, AI usage insights, and governance workflows in a single system. This approach helps enterprises move from reactive risk management to proactive AI governance.

If your organization is struggling to understand where AI is being used and what risks it introduces, it’s time to start with visibility. Explore how CloudEagle.ai helps enterprises discover, govern, and optimize AI-powered SaaS usage without slowing innovation.

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FAQs

1. How does Shadow AI usually enter an organization?

Shadow AI often enters through employees experimenting with GenAI tools, enabling AI features inside existing SaaS apps, installing browser extensions, or connecting AI tools via OAuth without security review.

2. Is Shadow AI always a security threat?

Not always. The risk comes from lack of visibility and control. Without discovery, even well-intentioned AI usage can expose sensitive data or violate compliance rules.

3. Can Shadow AI discovery tools slow down innovation?

No. Most tools are designed to enable safe AI adoption by providing visibility and guardrails, not by blocking AI usage outright.

4. Who typically owns Shadow AI discovery, IT, security, or compliance?

In most organizations, ownership is shared. IT manages visibility, security assesses risk, and compliance ensures AI usage aligns with regulations and internal policies.

5. How often should AI discovery run in an enterprise environment?

Ideally, discovery should be continuous or near real-time, since new AI tools, features, and agents can appear daily across SaaS and browser environments.

6. What’s the difference between AI discovery and AI governance?

AI discovery identifies what AI is being used, where, and by whom. AI governance builds on that visibility to apply policies, controls, and accountability.