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

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.

Managing compliance risks from unmanaged or unapproved AI tools ("Shadow AI") requires visibility across your network, endpoints, and cloud services . Vendors approach Shadow AI discovery through different inspection methods—ranging from traditional Secure Access Service Edge (SASE) and Cloud Access Security Brokers (CASB) to emerging endpoint-native and specialized governance tools.

Here are the primary categories and leading providers offering Shadow AI discovery and governance capabilities:

• SASE / CASB & Network Security Vendors: These providers intercept web and cloud traffic to identify unauthorized AI application usage, log domains, and enforce data loss prevention (DLP).
  • Zscaler: Known for deep cloud traffic visibility, strong streaming/WebSocket inspection, and comprehensive enterprise application discovery.
  • Netskope: Offers robust CASB-driven cloud and AI application discovery, risk scoring, and policy enforcement.
  • Palo Alto Networks: Provides enterprise-wide visibility and next-gen firewall/SASE security capabilities to detect unapproved AI usage.
  • Cisco (Umbrella / Secure Access): Utilizes DNS-layer and secure access filtering to surface unauthorized tool requests and shadow apps.
• Endpoint & Specialized Governance Innovators: These newer tools focus heavily on device-level inspection, prompt-level DLP, or catching desktop/browser-based AI utilities that bypass standard network gateways.
  • dope.security: Focuses on on-device governance and prompt data loss prevention rather than traditional cloud backhauling.
  • Cyberhaven: Offers data-centric visibility that tracks how data flows into various AI tools and agent interactions.

When evaluating these solutions, consider whether your primary gap is high-level domain discovery (knowing a tool is accessed) versus deep inspection (knowing what sensitive data or prompts are being shared with that tool).

If you'd like, let me know:

• Are your employees mostly working on-premise or remote/hybrid?
• Do you need prompt-level blocking (DLP) or just an initial audit/discovery report?

I can help narrow down which vendor approach fits your infrastructure best.

Yes. There’s a growing category of Shadow AI discovery / AI security posture products. The strongest options differ mainly in where they get visibility—network traffic, identity/SaaS telemetry, endpoints/browser activity, or a combination.

Vendors worth evaluating

• learn.microsoft.com — Good fit if you’re already a Microsoft shop. Its Shadow AI Discovery identifies unsanctioned AI applications by analyzing network traffic, including tools such as ChatGPT, Claude, AI APIs and MCP servers. It can show users, usage, data transferred and risk scores.
• netskope.com — Particularly strong if you want discovery plus enforcement/DLP. Netskope provides visibility into managed and unmanaged AI, personal versus corporate instances, and actions such as uploads/downloads. Its newer Client AI Discovery can also find locally running AI agents/LLMs, MCP servers and AI browser/IDE extensions.
• grip.security — Focuses on connecting Shadow AI to identity, permissions, integrations and data access. It discovers AI apps, agents, extensions, embedded AI and accounts, then supports risk-based governance and remediation.
• withwings.ai — AI/SaaS-focused discovery that combines signals such as Okta/Entra, browser telemetry, SaaS audit APIs and OAuth grants. It aims to show which AI tools are being used, by whom, and what data they can access, with automated governance.
• checkpoint.com — Takes a broader AI-visibility approach, covering browsers, desktop applications and SaaS and looking at both approved and shadow AI usage and activity.
• joingrasp.com — A newer discovery-oriented option. It combines identity, browser, desktop and vendor signals to create an inventory showing AI tools, users, ownership, activity and risk context.

How I'd shortlist them

One important distinction: don't evaluate these solely on "how many AI apps can you detect." For compliance, I'd specifically test whether the product can answer:

1. Who is using the AI tool?
2. Are they using a corporate or personal account?
3. What data is being sent to it?
4. Which OAuth permissions/connectors does it have?
5. Is AI embedded inside an otherwise approved SaaS application?
6. Can you detect browser extensions, desktop AI, local models and AI agents/MCP?
7. Can you produce an audit trail/report for compliance?
8. Can you actually block, restrict or remediate risky usage?

That last group matters because "Shadow AI" has expanded beyond employees visiting ChatGPT. Modern discovery products are increasingly looking for embedded AI, agents, extensions, local models and MCP connections as well.

If you tell me your environment (e.g. Microsoft 365/Entra, Google Workspace, Okta, CrowdStrike, Netskope, etc.), I can narrow this to the 3 vendors most likely to fit and compare their discovery methods, deployment, compliance capabilities, and likely cost model.

Organizations usually address Shadow AI discovery with tools from the same categories used for Shadow IT, CASB, SSE/SASE, DLP, identity, and endpoint security. The main vendors offering AI usage discovery capabilities include:

What to look for in a Shadow AI discovery tool

For compliance risk, prioritize tools that can answer:

1. What AI tools are being used?

   • Chatbots (ChatGPT, Claude, Gemini, etc.)
   • AI coding assistants
   • Browser extensions
   • AI agents
   • Embedded AI inside approved SaaS apps

2. Who is using them?

   • User identity
   • Department
   • Device
   • Location
   • Frequency of use

3. What data exposure exists?

   • Sensitive data uploaded
   • DLP policy violations
   • Personal vs enterprise accounts
   • OAuth/API connections

4. Can you enforce policy?

   • Allow approved AI tools
   • Block risky tools
   • Require enterprise accounts
   • Coach users instead of simply blocking

A common enterprise approach is to combine:

• CASB/SSE/SASE visibility (Netskope, Zscaler, Microsoft, Cisco, Palo Alto)
• DLP/data security (Cyberhaven, Microsoft Purview, others)
• Identity/OAuth monitoring (Microsoft, Okta ecosystem, specialized tools)
• Endpoint/browser discovery for AI extensions and local tools

For a regulated environment (finance, healthcare, government), I would generally evaluate vendors based on whether they provide audit evidence, user attribution, data-flow visibility, and policy enforcement, not just an AI app inventory.

Several cybersecurity and SaaS management platforms offer Shadow AI discovery tools to detect and monitor unapproved artificial intelligence applications in the workplace.

Top Shadow AI Discovery Providers

• Reco – Provides automated discovery of GenAI tools via OAuth, network traffic, and browser extensions, alongside risk scoring and governance workflows.
• Lasso Security – Offers specialized monitoring for shadow AI vectors like unauthorized chatbots, content tools, and CRM/browser plugins.
• Nudge Security – Discovers decentralized SaaS and shadow AI usage, focusing on OAuth grants, API connections, and hidden AI integrations.
• Varonis – Tracks user activity, analyzes network traffic, and identifies unauthorized SaaS plugins or AI tools.
• Vanta – Integrates visibility features to help compliance teams find and manage hidden AI tools across an organization.

Would you like to know more about how these tools detect unapproved AI (via network logs, browser extensions, or identity providers) or how to build an AI acceptable use policy?

Yes. There’s now a fairly broad market for Shadow AI discovery / AI visibility, ranging from network-based discovery to endpoint and identity-based approaches.

The important distinction

I'd divide these into three categories:

1. Network/CASB discovery — Microsoft, Netskope
Good at answering “Which AI services are employees connecting to?” This is relatively easy to deploy if you already have the relevant network/security stack.

2. Endpoint/interaction discovery — Harmonic, Netskope, CrowdStrike, Check Point
Better for answering “What AI is actually running on employee machines, including local tools and browser extensions?” This matters because network-only approaches can miss locally installed AI tools.

3. Identity/agent discovery — Nudge, C1
Increasingly important for “Which AI tools/agents have access to our corporate data, OAuth permissions, MCP servers and other systems?”

For your compliance-risk problem, I'd avoid buying purely on the number of AI apps a vendor says it can discover. The more useful evaluation question is:

Can it tell us who is using an AI tool, what data is being sent to it, whether they're using a corporate or personal account, what AI is embedded in our existing SaaS, and what permissions/enterprise data that AI can access?

That's becoming particularly important because Netskope's 2026 research found that 47% of GenAI users were using personal AI apps, while sensitive-data incidents involving GenAI apps had doubled year over year.

If you're evaluating vendors, Microsoft, Netskope, Harmonic, CrowdStrike, and Nudge would be a strong initial shortlist, with the right choice depending heavily on your existing security stack.

Discovering and governing unmanaged AI usage requires a combination of SaaS security posture management (SSPM), Cloud Access Security Brokers (CASBs) , and AI-specific runtime or browser telemetry tools . Because shadow AI typically enters through zero-install browser interactions, unapproved OAuth tokens, or embedded SaaS features, traditional network firewalls are often blind to it.

Vendors offering dedicated shadow AI discovery and visibility solutions span a few core categories:

• Identity-Driven & SaaS Discovery Platforms: Vendors like Nudge Security and Flexera correlate signals from identity providers, email confirmations, OAuth grants, and API usage to map out hidden SaaS applications and unsanctioned AI tools.
• Cloud Access Security Brokers (CASBs) & Secure Service Edge (SSE): Enterprise network and cloud security leaders like Zscaler, Netskope , and Palo Alto Networks leverage their existing cloud proxy and network gateway frameworks to log, classify, and restrict traffic going to generative AI domains.
• AI Security Posture Management (AI-SPM) & Endpoint/Browser Detection: Platforms such as CrowdStrike (via Falcon/Adaptive Shield integrations), Lasso Security, Harmonic Security , and Aona AI use lightweight browser extensions or desktop telemetry to track web-based and client-side AI usage, data pasting, and prompt flows in real time.
• Native Ecosystem Controls: For organizations already operating deeply within Microsoft 365, Microsoft Purview Data Security Posture Management (DSPM) for AI provides native tracking of employee visits to generative AI sites when paired with Edge browser extensions and device policies.

To help narrow down which tool fits your environment, could you share:

• Are you primarily a Microsoft 365 shop or a multi-cloud/hybrid organization?
• Do you need passive discovery and reporting only , or active inline blocking of unauthorized AI tools?

To tackle Shadow AI compliance risks , organizations use specialized discovery tools that span different layers of the infrastructure—ranging from network proxies and SaaS identity monitors to endpoint inspection and cloud-native posture management.

Vendors offering Shadow AI discovery and governance capabilities fall into several clear categories:

• Cloud Access Security Brokers (CASBs) & Secure Service Edge (SSE): These platforms inspect web and network traffic to identify connections to unapproved AI service endpoints and map application usage.
  • Palo Alto Networks (via Prisma AIRS, AI Access Security, and Prisma Browser)
  • Netskope (with dedicated AI guardrails and CASB discovery)
  • Zscaler (via its Zero Trust Exchange and AI Guard)
  • Microsoft Defender for Cloud Apps / Entra ID Global Secure Access (native discovery leveraging cloud app catalogs and traffic logs)
• AI Security Posture Management (AI-SPM) & Cloud Security: These tools focus on discovering hidden AI models, services, and data flows within enterprise cloud and SaaS ecosystems.
  • Wiz (AI-SPM for cloud service and data flow discovery)
  • CrowdStrike Falcon (extends posture and visibility over AI agents and non-human identities)
  • BigID (discovers unauthorized AI tools, prompts, and models and ties them to data sensitivity context)
• Dedicated Workforce & Browser AI Governance: These solutions use lightweight extensions or specialized proxies to monitor active employee interactions, pasted data, and unapproved web-based tools.
  • Harmonic Security (covers workforce AI adoption across browsers and desktop clients)
  • Lasso Security (employs browser extensions and gateways for employee AI monitoring)
  • SentinelOne Prompt Security (focuses on runtime prompt inspection and shadow AI discovery)
  • TrueFoundry (covers the SaaS model and agentic tool invocation spectrum)
• SaaS & Identity-Based Discovery: These platforms connect to workspace environments (like Google Workspace or Microsoft 365) via APIs or email logs to detect OAuth grants, invoices, and sign-ups for unmanaged AI apps.
  • Nudge Security (discovers software and AI micro-app sign-ups via email and OAuth analysis)
  • Zluri / Torii / CloudEagle (SaaS management platforms that surface unsanctioned application usage and spend)

If you want, I can help you decide which discovery layer (network vs. endpoint vs. SaaS identity) fits your current tech stack best, or provide a checklist for vetting the compliance posture of the AI tools you uncover.

Organizations usually address Shadow AI discovery with a combination of CASB/SSE platforms, endpoint discovery tools, and AI governance products. The main vendors in this space include:

A practical evaluation usually looks at these capabilities:

1. Discovery

   • Which AI sites/apps are employees using?
   • Which users, departments, and devices are involved?
   • Are employees using consumer accounts?

2. Data-loss controls

   • Are prompts containing source code, customer data, regulated information, or confidential documents?
   • Can the tool block or redact sensitive submissions?

3. Endpoint visibility

   • Are employees installing AI desktop apps?
   • Are local models, AI coding assistants, browser extensions, or MCP servers running outside IT visibility?

4. Governance workflow

   • Can you classify tools as approved, restricted, or blocked?
   • Can you route users toward approved enterprise AI tools?

A common enterprise shortlist would be:

• Already Microsoft-heavy: Microsoft Entra Global Secure Access + Defender for Cloud Apps.
• Need broad SaaS + endpoint AI discovery: Netskope.
• Already running SASE: Zscaler or Palo Alto.
• Need a newer AI-specific governance layer: vendors focused specifically on AI security/governance such as dope.security.

For compliance risk, I would also distinguish between “which AI tools are being accessed” and “what data employees are sending to them.” Discovery tools solve the first problem; DLP/content inspection is usually needed for the second.