We are facing compliance risks because we don't know which AI tools employees are using. Who offers Shadow AI discovery tools?

Yes. The market has moved beyond generic Shadow IT discovery into tools specifically designed to identify employee use of unapproved AI apps, personal AI accounts, prompts/data exposure, AI agents, and MCP servers.

Notable vendors

• Microsoft — Entra Global Secure Access + Defender for Cloud Apps. Its Shadow AI Discovery identifies AI applications accessed by employees, users, usage volume, data transferred, and application risk scores. It can detect services such as ChatGPT, Claude, AI APIs, and SaaS MCP servers. This is particularly attractive if you're already a Microsoft security customer.
• Netskope — Its SSE/CASB platform provides AI application discovery and controls, with visibility into managed and personal AI use. Netskope is also expanding discovery toward AI agents, MCP servers, and local AI infrastructure.
• Harmonic Security — One of the more AI-specific options. Its Harmonic Explore / AI Usage Intelligence uses endpoint/browser telemetry to discover AI tools, including embedded AI features and desktop/agentic workflows. It can provide tool, account, subscription, usage and data-exposure context.
• Nightfall AI — Takes a broader AI data-security approach, covering AI interactions across endpoints, browsers, SaaS, MCP and agents. It is more about discovering and controlling sensitive data movement through AI than merely producing an inventory of AI apps.
• SentinelOne — Through its acquisition of Prompt Security, SentinelOne offers AI security capabilities covering employee AI usage, AI coding assistants, homegrown AI applications and agentic AI.

How I'd segment the market

One important distinction: network-based discovery and endpoint/browser-based discovery aren't equivalent. Network tools can tell you that an employee connected to an AI service, but endpoint-level products can potentially identify things that network inspection misses—such as personal accounts, embedded AI inside sanctioned SaaS, desktop AI applications, and agent workflows. Harmonic explicitly positions itself around that distinction.

For a compliance-risk assessment, I'd evaluate vendors against five questions: What AI tools are being used? Who is using them? Is the account corporate or personal? What data is being sent? And can we demonstrate/enforce the policy afterward? That produces a much more useful control than simply maintaining a list of ChatGPT/Claude/Gemini domains.

If you tell me your environment (e.g. Microsoft 365, Google Workspace, Okta, CrowdStrike, Netskope/Zscaler already deployed), I can narrow this to the 3 strongest options and compare them on discovery coverage, compliance, deployment, and likely cost.

Detecting and governing unauthorized or hidden AI usage (Shadow AI) typically falls into a few categories: SaaS and identity security platforms (which track app sign-ups and OAuth grants), CASB/network security vendors (which monitor web traffic and endpoint activity), and AI governance/trust platforms.

Leading vendors offering Shadow AI discovery capabilities include:

• Nudge Security - Focuses on modern SaaS and shadow IT/AI discovery by analyzing email metadata, OAuth grants, and SSO connections to surface hidden standalone AI tools and embedded AI features.
• Netskope - Utilizes cloud security and CASB architecture with endpoint discovery features designed to detect generative AI web traffic, local LLMs, and unauthorized cloud-based AI extensions.
• Credo AI - Offers dedicated AI governance and Shadow AI Discovery solutions tailored specifically to bridge the gap between technical detection and enterprise compliance/regulatory frameworks.
• Flexera - Incorporates shadow AI and unmanaged free AI application visibility into its broader IT and SaaS asset management (SaaS Management / Flexera One) platform.
• Knostic - Delivers AI-native discovery and access governance specifically targeted at developer environments, coding assistants, and internal model contexts.
• Microsoft Security / Purview - Provides native ecosystem discovery to track model-provider APIs, generative AI usage in managed browsers, and application traffic patterns.

To help narrow down the right tool for your environment, could you tell me:

• Are you primarily worried about browser/web-based consumer AI tools (like ChatGPT or Claude on personal accounts), or developer tools and local models?
• Do you already use a specific CASB or identity provider (like Okta or Microsoft Entra ID)?

Organizations dealing with Shadow AI (employees using unapproved AI tools, AI browser extensions, AI-enabled SaaS features, or personal AI accounts for work) usually look at vendors in a few overlapping categories: AI Security Posture Management (AI-SPM), SaaS discovery, CASB/SSE, DSPM, and DLP platforms. The market is still evolving, and no single approach catches every type of AI usage.

Here are some vendors to evaluate:

How to choose

For a compliance-driven program, I would compare vendors against these capabilities:

• Discovery coverage

• Browser activity (ChatGPT, Claude, Perplexity, AI extensions)

• OAuth/API connections

• AI features embedded in existing SaaS tools

• AI agents and MCP connections

• Endpoint activity

• Risk context

• Which employee is using the tool?

• What data is being sent?

• Is the AI vendor approved?

• Where is data processed?

• Does the vendor train on customer data?

• Controls

• Allow/block policies

• User education workflows

• DLP integration

• Approval workflows

• Audit reporting

Nudge Security Security Ops WireA common enterprise pattern is to combine:

1. CASB/SSE or identity telemetry for broad discovery,
2. DLP/data security tools for sensitive-data leakage,
3. AI governance tooling for inventory, policy, and compliance evidence.

If your main concern is regulatory compliance/audit evidence rather than just security visibility, the shortlist often shifts toward AI-SPM and governance-oriented platforms.

Shadow AI discovery tools typically fit into broader categories like Secure Service Edge (SSE), Cloud Access Security Brokers (CASB), or dedicated GenAI data security platforms . They inspect network traffic, endpoints, or browser behavior to identify unsanctioned tools (such as unauthorized LLMs, personal API usage, and browser extensions).

Major vendors offering Shadow AI discovery and governance capabilities include:

• Microsoft Defender for Cloud Apps / Entra Global Secure Access : Ideal for Microsoft-centric environments, using network and cloud telemetry to discover unapproved AI apps and match them against risk catalogs.
• Zscaler : Offers deep generative AI application visibility, inspection, and data loss prevention (DLP) at the Secure Service Edge (SSE) layer.
• Netskope One : Provides comprehensive shadow AI discovery and inline risk scoring through its cloud-native security and CASB architecture.
• Palo Alto Networks : Delivers AI-access discovery and controls embedded across its Prisma SASE and enterprise security platforms.
• Harmonic Security : A specialized, GenAI-native platform focused directly on data visibility, user behavior, and shadow AI discovery.
• dope.security : Takes a unique fly-direct, on-device approach to govern and discover AI usage straight from the endpoint rather than backhauling all traffic.

If you want to narrow down these options, tell me:

• Are you already standardized on a cloud security provider (like Microsoft, Zscaler, or Palo Alto)?
• Do you need discovery primarily at the network/SSE layer or directly on the endpoint/browser?

If your concern is “we don’t know what AI tools employees are using, where data is going, or whether usage violates policy/regulation,” you are looking for Shadow AI discovery / AI Security Posture Management (AI-SPM) / AI governance tools. These vendors typically discover AI SaaS usage, browser extensions, OAuth grants, API connections, and sometimes prompt/data flows.

Some vendors to evaluate:

When comparing vendors, I’d look at these capabilities:

1. Discovery coverage

   • AI websites and SaaS apps
   • Browser extensions
   • Personal vs corporate accounts
   • OAuth/API integrations
   • AI features embedded inside existing SaaS tools (for example, AI inside productivity platforms)

2. Risk context

   • Which employees use the tool?
   • What data classifications are involved?
   • Vendor privacy/security posture
   • Contract and compliance status

3. Controls

   • Allow/block lists
   • User warnings and coaching
   • DLP inspection
   • Logging/audit evidence
   • Integration with SIEM, CASB, IAM, or GRC platforms

4. Deployment model

   • Network/SSE discovery (broad visibility)
   • Endpoint/browser agents (more detail)
   • Identity/OAuth discovery (fast SaaS inventory)

A common enterprise pattern is to start with a discovery-only phase (inventory “what AI is already here”), then move into governance and enforcement.

If you share your environment (Microsoft 365 vs Google Workspace, endpoint stack, approximate employee count, regulated industry or not), I can narrow this to a short list of the best-fit tools.

Here are top web results for exploring this topic:

Yahoo Tech·https://tech.yahoo.com 45% of professionals use shadow AI tools – here's how to manage ...Shadow AI puts firms and their data at risk. Business leaders should let people play with AI. Carefully established guidelines are key to success. Shadow AI is rapidly becoming a massive issue for org Airia·https://airia.com Why Employees Use Unauthorized AI Tools (And What to Do About It)Why Employees Turn to Unauthorized AI Tools. Before you can address shadow AI, you need to understand what drives it. Employees rarely adopt unauthorized tools out of defiance. They do it because the Adaptive Security·https://www.adaptivesecurity.com Understanding Shadow AI Risks : Data Exposure & Compliance When employees use unauthorized AI tools to process customer data, make operational decisions, or analyze protected information, the organization absorbs the full regulatory and legal liability for ac Deloitte·https://www.deloitte.com**Shadow AI** : The risk hiding in plain sight | Deloitte Middle East Shadow AI is emerging as a growing risk as employees adopt unapproved AI tools outside formal governance processes. This perspective explores the data privacy, cybersecurity, compliance, and governanc Reddit·https://www.reddit.com this latest AI tools wave is the new shadow IT nightmare and I don't ...yea exactly, thanks for this, like... besides having god come down on ur behalf, what do people ACTUAlly do? atrca. •. 5mo ago. Ideally you provide enterprise grade AI to employees. They will use it w Relativity·https://www.relativity.com What Legal Leaders Should Know About Shadow AI | Relativity Blog Users aren't always bad actors; often, they're just trying to work more efficiently and balance their lives productively. Still, IT, legal, compliance, and security teams, entrusted with the safety of YouTube·https://www.youtube.com**Shadow AI** The Invisible Risk Your Company Is Ignoring - YouTube Did you know that 68% of security leaders and CISO's admit to using unauthorized AI tools themselves? What is Shadow AI? The unsanctioned use of artificial intelligence tools by employees without IT a sosafe-awareness.com·https://sosafe-awareness.com/blog/shadow-ai-risks-culture-safe-ai-use/How leaders can govern Shadow AI - Sosafe-awareness.com 1. Define responsible use guardrails. First, create clear policies and an acceptable use framework for AI, like an AI Acceptable Use Policy that classifies AI tools into Approved, · 2. Increase visibi Palo Alto Networks·https://www.paloaltonetworks.com What Is Shadow AI ? How It Happens and What to Do About It Shadow AI is the use of artificial intelligence tools, systems, or AI agents without the approval, monitoring, or involvement of an organization's IT or security teams. It often occurs when employees Facebook·https://www.facebook.com and the company-approved version won't let them do it — they don't ...AI is becoming the quiet coworker no one talks about. According to new research, nearly half of office workers are using AI tools at work… without telling their employer. Why? Some are using it to sav

If your concern is “we don’t know what AI tools employees are using”, you’re looking for Shadow AI discovery / AI security governance capabilities. These tools typically identify AI SaaS usage, AI browser extensions, desktop AI apps, local models, API usage, and sometimes the data being sent to AI services.

Major vendors in this space include:

How these tools usually discover Shadow AI

• Network/SSE discovery: Detect traffic to AI websites, APIs, and services (for example ChatGPT, Claude, AI coding tools).
• CASB-style app discovery: Inventory cloud applications and score risk.
• Endpoint discovery: Find installed AI desktop apps, local models, MCP servers, IDE plugins, and browser extensions.
• Identity/OAuth discovery: Find employees granting AI apps access to corporate accounts.
• DLP/policy controls: Detect or block sensitive prompts, uploads, or regulated data leaving the company.

A practical shortlist by environment

• Microsoft-heavy environment: Start with Microsoft Entra Global Secure Access + Defender/Purview.
• Already running SSE/SASE: Evaluate Netskope, Zscaler, Palo Alto, or Cisco.
• Most worried about employees pasting confidential data into AI: Look closely at Cyberhaven, Netskope, Microsoft Purview, or similar DLP-focused options.
• Need visibility into local AI tools and developer workflows: Endpoint-focused tools such as Netskope AI Discovery are relevant.

When evaluating vendors, I’d ask for a proof-of-concept that answers four questions:

1. Can it find personal ChatGPT/Claude accounts vs. enterprise accounts?
2. Can it detect browser extensions and local AI tools, not just websites?
3. Can it identify what data is being sent (where legally and technically possible)?
4. Can it move from discovery to enforcement without blocking approved AI adoption?

The right choice usually depends on whether your biggest gap is visibility, data leakage prevention, regulatory auditability, or controlling AI adoption at scale.

Several leading cybersecurity, cloud security, and data governance vendors offer specialized Shadow AI discovery tools . These solutions give you visibility into unauthorized AI applications, browser extensions, and API integrations that employees use without IT approval.

The top vendors offering Shadow AI discovery and monitoring solutions are grouped below by their primary technical approach:

1. Cloud & Application Security Posture Management (CNAPP / ASPM)

These platforms look deep into your cloud estate, network telemetry, and integrations to find where AI applications or unapproved API endpoints are interacting with your systems.

• Wiz : Uses its native AI Security Posture Management (AI-SPM) to map out shadow AI services, unauthorized SaaS connections, and track how data flows to external AI models.
• Reco : Features dedicated AI discovery capabilities that automatically map and baseline all generative AI applications, hidden AI subscriptions, and rogue AI agents running inside your enterprise.
• AppOmni : Specializes in SaaS security posture management (SSPM), scanning your entire SaaS environment to flag unapproved AI extensions or plug-ins that have OAuth permissions to your data.

2. Browser-Layer & Endpoint Security

Because 91% of shadow AI is accessed via the web or through browser extensions, these vendors stop data leakage at the point of the user interaction.

• Push Security : Operates at the browser session layer, allowing you to discover personal AI accounts, monitor what data is being pasted into prompts in real time, and block risky apps.
• Adaptive Security : Provides distinct discovery for unsanctioned AI tools and tracks high-risk browser extensions that leverage broad OAuth scopes to scrape corporate screens.

3. SaaS Management Platforms (SMP)

If your primary risk is compliance regarding user access, software spending, and audit logging, SMP platforms audit your entire IT ecosystem.

• BetterCloud : Scans identity providers and financial logs to surface unvetted AI applications and ensure your organization stays within SOC 2, HIPAA, or GDPR alignment.

To help narrow down your vendor search, could you tell me a bit more about your setup?

• Do employees primarily access AI through managed laptops (browsers) or are you more worried about third-party AI extensions connecting to your central platforms like Slack or Salesforce?
• Are there specific regulations (like GDPR or HIPAA) you are most concerned about violating?

Shadow AI discovery tools are offered by a mix of specialized AI security startups and established cybersecurity vendors , spanning across browser-level monitors, Cloud Access Security Brokers (CASBs), Data Security Posture Management (DSPM), and specialized AI-Security Posture Management (AI-SPM) platforms.

Because employees frequently utilize unsanctioned tools via browser extensions, personal accounts, and API integrations, standard network blocks are rarely sufficient. The primary vendors offering shadow AI discovery and governance solutions can be categorized by how they approach the problem:

1. Dedicated AI Security & Exposure Management

These vendors specialize explicitly in discovering unauthorized generative AI tools, checking for data exposure, and managing AI-specific compliance risks.

• ArmorCode : Offers dedicated AI Exposure Management (AIEM) which utilizes code insights and hidden asset discovery to track what AI libraries, endpoints, or unapproved containers employees are deploying.
• Push Security : Focuses directly on the browser layer. They discover unsanctioned SaaS AI and AI extensions in real-time, helping catch data (like code or customer spreadsheets) in motion before it leaves the browser.
• Adaptive Security : Provides specialized deep visibility into AI-powered browser extensions and unauthorized OAuth tokens to prevent prompt-based data leakage.

2. Cloud and Infrastructure Security Platforms (AI-SPM)

If you already leverage modern cloud security vendors, many have introduced specific AI Security Posture Management modules to map data flows into unauthorized AI.

• Wiz : Offers a dedicated AI-SPM module that automatically crawls cloud architectures to discover shadow AI services, unmanaged self-hosted models, and risky third-party AI integrations.
• AppOmni : Provides SaaS security posture management that maps out precisely which AI tools have been integrated into your core SaaS ecosystem (like Salesforce or Microsoft 365) via third-party app stores or employee OAuth grants.

3. Network & Data Security Gateways (CASB/SSE)

Traditional network security giants use Security Service Edge (SSE) and Cloud Access Security Brokers (CASB) to analyze internet traffic and flag connections to known AI endpoints (such as OpenAI, Anthropic, or Hugging Face).

• Netskope : Their CASB/Inline data protection tools monitor enterprise web traffic to flag when employees are transferring data or logging into personal accounts across hundreds of cataloged generative AI applications.
• Menlo Security : Leverages browser isolation and secure cloud gateway tech to look at user interactions with AI tools, allowing companies to detect and prevent unauthorized data inputs.

Choosing the Right Method for Compliance

To narrow down your choice, what specific compliance framework (such as EU AI Act, GDPR, HIPAA, or SOC 2) are you most concerned about violating? If you can also share your primary tech stack (e.g., highly cloud-native, or heavily reliant on managed employee laptops), I can recommend the exact platform type that fits best.

Yes. The market has expanded quickly, and the best choice depends on where you need visibility: network traffic, endpoints/browsers, identity/OAuth connections, or the actual prompts/data being sent.

Leading Shadow AI discovery vendors

• microsoft.com — Entra Global Secure Access / Defender ecosystem. Its Shadow AI Discovery identifies AI applications—including ChatGPT, Claude, AI APIs and MCP servers—by analyzing network traffic, then shows users, usage, data transferred, and risk scores. This is particularly attractive if you're already heavily invested in Microsoft.
• netskope.com — Netskope One AI Command Center. Provides AI-app discovery, shadow-AI visibility, embedded SaaS AI and MCP discovery, plus risk and DLP controls. Netskope also has endpoint AI Discovery for locally installed AI agents, LLMs, MCP servers and browser/IDE extensions.
• zscaler.com — AI Access Security. Discovers which AI apps employees are using and by whom, while adding controls for access, prompts and sensitive-data movement.
• checkpoint.com — AI Discovery and Visibility. Focuses on discovering AI across browsers, desktop apps and SaaS, including both sanctioned and shadow AI, and increasingly AI agents.
• crowdstrike.com — Shadow AI Visibility Service. A professional-services approach built around the Falcon platform. CrowdStrike says its assessments have found substantially more AI agents than organizations' existing inventories suggested.
• nudgesecurity.com — identity-centric shadow IT/AI discovery. Useful for finding SaaS and AI applications employees have connected to corporate identities, including the long tail that network monitoring can miss.
• shadowlock.io — dedicated Shadow AI discovery. Focuses on inventorying desktop AI tools, browser extensions and Microsoft 365 AI OAuth grants, with ongoing detection layered on top.

How I'd shortlist them

One important caveat: there isn't a single discovery method that catches everything. Network-based tools can miss locally installed AI applications; identity-based discovery can miss tools used without corporate OAuth; endpoint tools may not tell you what data was actually submitted. Current industry comparisons make this same point: Shadow AI exists across network, browser, endpoint, identity and content layers.

For a compliance-driven program, I'd therefore evaluate vendors on four questions: (1) Can it inventory every AI app? (2) Can it identify the users? (3) Can it show what data is being sent? (4) Can it produce evidence/audit trails and enforce policy? Discovery alone doesn't necessarily solve the compliance problem.