Your guide to the tools that discover and govern how employees use AI in 2026: the approaches they take, what each can enforce, and how they fit alongside the native controls in the AI apps you already pay for.
Your guide to the tools that discover and govern how employees use AI in 2026: the approaches they take, what each can enforce, and how they fit alongside the native controls in the AI apps you already pay for.
Shadow AI discovery and governance tools show security teams how employees actually use AI and give them the controls to keep that use within policy.
These tools approach the problem from different directions: network proxies that inspect AI traffic, endpoint agents on managed devices which see what apps are being used, identity platforms that map who has access to which apps, data security tools that classify what's being shared, and enterprise browsers and browser extensions that work inside the session itself.
Increasingly, they're converging on the browser, because that's where most AI use happens. This means many vendors are now bolting on new browser capabilities, typically through an extension, to get the visibility they need. But a predominantly network or endpoint tool with a browser extension added will see and do different things from a browser-native tool, where the same in-session telemetry supports discovery, enforcement, investigation, and threat detection at once.
A note on scope: this list covers securing how people use AI, not securing the AI agents organizations deploy. Agent security governs what autonomous agents do once they're running, such as the tools they call and the permissions they hold. For most organizations it's a later-stage problem with a different owner, and it's increasingly handled inside the platforms where agents are built. Where the two meet, such as when an employee grants an AI agent OAuth access to their mailbox, the tools below cover the grant.
Here's what the shadow AI tools market looks like in 2026.
The top shadow AI discovery and governance tools in 2026 include Push Security, Harmonic, Island, and Akamai Workforce Protector (formerly LayerX).
1. Push Security – Enterprise browser extension
Push is a browser extension, not a browser, so coverage doesn’t depend on standardizing on one browser. Push discovers the apps that employees use from browser logins and events. It covers every dimension of shadow AI from one deployment, including AI apps, personal accounts on approved tools, AI browser extensions, OAuth integrations, and agentic browsers like Comet, Atlas, and Dia, and it controls how AI is used in the browser in real time.
Push enforces governance policy at the point of use. Each AI app can be set to inform, acknowledge, or block, scoped by user group, with in-browser banners that send people to the approved tool instead of just blocking them, or guide the user’s behavior when using an app. Push detects how people log in (to identify password vulnerabilities, SSO gaps, MFA gaps, and so on) as well as how they interact with an app. This means you can detect and block risky actions such as API keys, credentials, and other sensitive data patterns pasted into AI apps. File upload rules block uploads by file type, app, and user group. AI conversation logs stream prompts (and optionally responses) to your SIEM, giving you one audit trail across every AI app in the browser.
Because Push runs in the browsers you already use, it gets you closest to the user and enforces policy at the point of interaction, across every app. In contrast, native controls stop at one vendor's corporate tenant, proxies act only on traffic they route and decrypt, and enterprise browsers act only inside their own contained browser. API-based tools only see the apps and tenants they've been connected to, which by definition leaves out most shadow AI, and identity tools mostly act after the fact.
AI adoption isn't just a governance problem. Attackers use AI to build convincing phishing pages at scale, and AI-themed lures, from fake AI tool installers to malicious instructions hosted on AI chatbot pages, target exactly the employees keen to try new tools. The same Push deployment detects and blocks those attacks too.
Learn more about how to discover, govern, and secure AI apps with Push.
2. Harmonic Security – Browser extension and endpoint agent
Harmonic is built specifically for AI data protection. Its browser extension inventories AI use across standalone tools and the AI features embedded in approved apps like Canva and Grammarly, and it tells corporate accounts from personal ones, down to the subscription plan. Small language models classify sensitive data in prompts fast enough to coach, warn, ask for a business justification, or block inline, and to steer people from a free tool to the sanctioned one. An endpoint agent extends the same controls to desktop AI apps, coding tools, and local models, and an MCP gateway covers agent tool calls.
Harmonic's focus is the AI interaction itself. Its MCP gateway governs the connections AI coding tools make to external tools and data sources, but it doesn't extend to the wider SaaS estate beyond AI, and its published capabilities don't cover AI browser extensions or the OAuth grants that give third-party AI apps access to Google Workspace and Microsoft 365.
3. Island – Enterprise browser
Island's AI Protect, launched in March 2026, brings AI governance into its managed browser. It separates corporate and personal tenants, enforces data boundaries before data reaches an AI provider, redacts sensitive content from prompts, and records prompts, responses, and agent activity. Island now describes itself as an agentic control plane, and it raised a large Series F in September 2026 to expand further into AI and agent controls.
AI Protect now reaches beyond Island's own browser through an Island extension for other browsers, plus endpoint and network components. The deepest controls, such as session recording and full workspace governance, still depend on moving users onto the Island browser.
4. SentinelOne (Prompt Security) – Browser extension and endpoint platform
SentinelOne acquired Prompt Security in 2025 and has folded it into the Singularity platform as a set of AI products covering AI usage control, agentic AI security, and security for the AI applications teams build themselves. On the workforce side, it gives visibility into AI use across a broad range of AI apps and sites, blocks sensitive data from reaching them, and defends against prompt injection.
Prompt Security's browser extension covers AI use in the browser, and SentinelOne extends the same policies to desktop AI apps and code assistants like GitHub Copilot, Cursor, and Claude Code, redacting sensitive data before a prompt leaves the device. It runs in the same console as SentinelOne's endpoint, identity, and cloud protection, which makes it a natural fit for organizations already standardized on SentinelOne.
5. Microsoft Edge for Business & Purview – Built-in browser and DLP controls
Microsoft is the one platform vendor whose built-in controls reach beyond its own AI app, and if your organization runs on Microsoft they go a long way. At RSAC 2026 Microsoft added inline Purview DLP for AI prompts in Edge for Business, so sensitive prompts and file uploads to consumer AI tools can be audited or blocked, with an option to send the user to Microsoft 365 Copilot instead. DSPM for AI adds discovery and reporting on third-party AI use.
The catch is the setup. Most Purview controls for third-party AI apps need pay-as-you-go billing, the Purview browser extension, and devices onboarded to Purview, and retention and eDiscovery for those apps are limited to Edge. As in our browser security list, these are a foundation that the other tools here build on.
6. Akamai Workforce Protector (formerly LayerX) – Enterprise browser extension
LayerX built its product as a browser extension focused on AI usage control and browser DLP. It captures prompts and file uploads inside AI tools, classifies sensitive submissions, flags personal accounts, and enforces policy without a new browser, including on contractor and BYOD devices. It also scores installed browser extensions for risk, many of which are now AI-powered, and has extended coverage toward desktop AI apps and developer tools.
Akamai completed its acquisition in July 2026 and now sells the product as Workforce Protector within its Zero Trust portfolio, alongside segmentation, ZTNA, and DNS security. For existing Akamai customers that makes it easy to add. For everyone else, the open question is the same one raised with any acquired product: what the roadmap looks like 18 months after close.
7. Zscaler and Palo Alto Networks – SSE / network proxy
The two biggest security service edge vendors both extend their proxies to AI traffic. Zscaler's AI Security Suite pairs an inventory of AI apps, agents, and models with inline inspection and prompt classification for the AI services you allow. Palo Alto's AI Access Security classifies GenAI apps by risk, with inline DLP and user coaching. For organizations already routing traffic through either platform, adding AI policy is a natural next step.
A proxy enforces policy on the traffic it can see and decrypt, which leaves less context about the account, extension, or action involved than a view from inside the session. Both vendors have bought their way into the browser to close that gap: Zscaler with SquareX, and Palo Alto with Talon, now Prisma Browser.
8. Grip Security – Identity and SaaS platform
Grip discovers SaaS and AI apps from identity signals such as email, SSO, and identity provider data, so it can build an inventory without deploying anything to endpoints. Its AI Security Platform, expanded in August 2026, maps users, AI agents, apps, and permissions into an identity graph, and adds AI posture management, non-human identity governance, and automated remediation such as revoking access.
Grip has since added an optional browser extension and prompt monitoring to an identity posture platform. That's a good fit when the question you most need answered is who has access to which AI tools and what permissions those tools hold. Email and identity signals are better at showing that an account exists than whether an app is in use today or which account someone actually signed in with, and the approach is a weaker fit when you need to enforce policy at the moment someone pastes data or signs in with a personal account.
9. Nightfall AI – AI-native DLP
Nightfall applies one AI-native detection engine across SaaS, endpoints, browsers, email, AI apps, and MCP workflows. Its browser plugin inspects prompts and file uploads before submission, recognizes when a file being uploaded originated in a corporate SaaS app, and can redact just the sensitive part of a prompt rather than blocking all of it. When it intervenes, it coaches the user, for example by pointing them to the corporate ChatGPT tenant instead of a personal one.
Nightfall is strongest for teams whose main problem is classifying sensitive data accurately across many channels, with AI as one of them. Like most DLP-first tools, it's typically paired with something else for discovery and access governance.
10. Cyberhaven – Endpoint and browser data security
Cyberhaven tracks where data came from and where it goes, including into AI tools, which makes it a strong fit for insider risk and IP protection programs. In 2026 it added discovery and runtime controls for AI agents and MCP servers on endpoints, and in July launched Cyberhaven Flow, a platform that ties lineage, identity, and behavior together across endpoints, browsers, and cloud, with integrations into the ChatGPT Enterprise and Claude compliance APIs.
Cyberhaven's browser extension originally required its endpoint sensor. A standalone version released in May 2026 extends coverage to ChromeOS, contractor, and other unmanaged devices, inspecting uploads, form inputs, and AI prompts and distinguishing corporate from personal accounts.
Native controls vs. shadow AI tools
Most organizations now pay for at least one business AI plan, and the security controls on those plans are improving. ChatGPT Enterprise exposes a Compliance Platform for audit logs and DLP integrations. Claude Enterprise added inference hooks that send each prompt to your own security server for an allow-or-deny verdict. Copilot inherits Microsoft's identity and Purview controls. If you're paying for these plans, you want people using them, because that's where the return on the license comes from and where those controls apply.
We haven't included them in this list because each one governs a single vendor's corporate tenant, and the richest controls usually sit on the top tier. None of them can see the personal ChatGPT account on a corporate laptop, the AI note-taker someone connected to Google Workspace, the AI extension installed last week, or the dozen AI apps nobody approved. Native controls are the baseline; the tools above cover everything outside it, including steering people back onto the plans you pay for. The one platform vendor on the list is Microsoft, because Edge for Business and Purview govern other vendors' AI apps, not just Copilot.
How the approaches compare
Each approach enforces AI policy at a different point. The table shows typical coverage by approach; individual vendors vary.
Approach | Where it enforces | Personal accounts on approved AI tools | AI browser extensions | MCP grants to third-party AI apps via OAuth | Paste and upload enforcement | Deployment |
Enterprise browser extension | In existing browsers, at login, paste, upload, or consent | Yes | Yes | Yes | Yes | Extension in existing browsers, including BYOD |
Enterprise browser | Inside the managed browser only | Yes, inside the managed browser | Yes, inside the managed browser | Partial | Yes | Browser migration |
SSE / network proxy | On traffic it routes and decrypts | Partial (tenant restrictions) | No | No | Partial, with TLS inspection | Traffic routing and TLS inspection |
Endpoint agent | On managed devices with the agent installed | Partial | Partial | No — consent to third-party apps happens in the browser | Yes | Agent on managed devices |
Identity / SaaS platform | Mostly after the fact (revoke access, notify the user) | Partial | Varies | Partial — only grants against platforms it's connected to | Limited | API connections |
AI-native DLP | On the data channels it monitors | Varies | No | No | Yes | Varies by channel |
Native in-app controls | Inside one vendor's corporate tenant | No (your corporate tenant only) | No | Partial (own connectors) | Top tiers, per vendor | Included in plan tier |
Learn more about Push Security
Push Security is the most powerful AI-native security tool in the browser. Think EDR, but for the browser — high-fidelity telemetry and real-time control across every session, on every device, with no browser migration required.
Push isn't a just an AI governance platform. It's what makes your AI governance policy enforceable: it finds AI use your policy doesn't reach, steers people to the tools you've approved, and blocks what shouldn't leave. The same deployment also covers the other security problems you can solve in the browser, including detecting and stopping advanced attacks, identity and shadow IT security, and DLP and insider investigations.
Book a live demo to learn more.
FAQs: AI discovery and governance
What is shadow AI, and why is it a security risk?
Shadow AI is any use of AI at work that your organization's AI policy doesn't reach. It has four dimensions: unapproved AI apps, personal accounts on approved apps, AI browser extensions, and OAuth integrations that connect AI tools to corporate systems. The risk isn't that people use AI. It's that sensitive data goes into tools and accounts where none of your controls apply, so the policy you wrote for AI never takes effect for that activity.
How do I get visibility into which AI tools my employees are using?
It depends on where the tool gets its signal. Network logs show traffic to AI domains but not which account was used. Identity provider data shows apps connected through SSO or OAuth but misses everything people sign up to directly. Email-based discovery infers apps from welcome messages, receipts, and vendor mail. An email shows a vendor contacted someone or an account was once created, but not that the app is in use today, which account was used, or how the person signs in. Inventories built that way tend to fill up with marketing mail, abandoned trials, and dormant accounts, while missing apps that never send one. Discovery from the login event itself is the most direct signal: it shows actual use, the account, the sign-in method, and whether MFA is on, alongside the extensions installed and the OAuth grants approved.
Treat visibility as the first step of governance rather than governance itself: an inventory shows you where your policy is being ignored, and enforcement is what changes the outcome.
How do I stop employees using personal AI accounts?
A personal account on an approved tool sits entirely outside your policy, with no corporate audit log, no DLP, and no retention control. Okta found that 80% of employees using unapproved AI do so because their own account is easier. Blanket bans tend to push that usage further out of sight. The more effective approach is to catch the personal-account login and redirect the person to the corporate tenant at that moment, escalating from a banner to an acknowledgment to a block.
Network tenant restrictions can enforce part of this for some apps, but they don't explain to the user what to do instead. The only reliable way to do this across all apps is to be able to intervene in the browser session in real time.
We're on ChatGPT Claude, or Copilot enterprise plans. What else do we need for AI security?
Use the native controls first: SSO, audit logs, retention, and prompt-level controls where your plan includes them. Several of the most useful ones, including the ChatGPT Compliance Platform and Claude inference hooks, are only available on enterprise tiers.
But even at the top tier, each vendor governs its own corporate tenant. What's left is the AI apps you don't pay for, personal accounts on the ones you do, AI extensions, OAuth grants to AI tools, and a single view across vendors. A shadow AI tool protects the investment you've made in those plans by steering people onto them, and it applies the same policy everywhere else.
This is particularly cost-effective if you're paying for multiple enterprise plans. Funnelling users to the apps you want them to use, and stripping out expensive subscriptions, means that a shadow AI tool isn't just a fraction of the cost, it can probably save you money.
How do I stop employees pasting sensitive data into AI tools like ChatGPT?
This is the core of AI data security for most organizations. Almost every AI policy says not to paste customer data, credentials, or source code into AI tools, and that rule only holds if something checks at the moment of the paste or upload. Native prompt controls exist at a few vendors, mostly on enterprise tiers, and each covers only its own product. Browser-layer controls apply the same rules across every AI app, including personal accounts.
How do I enforce an AI acceptable use policy?
Governance defines the policy, and enforcement makes it hold. Map each rule in your policy to a control at the point of use: approved tools get allowed, tolerated tools get a banner and an acknowledgment, prohibited tools and sensitive data get blocked. Graduated enforcement matters because blocking everything drives usage underground, the pattern SANS calls the "Framework of No". Pointing people to an approved alternative is what makes a policy realistic enough to follow: making the path you want them to follow the easiest.
What are the best AI governance tools for enterprise?
It depends which side of AI governance you mean. AI governance platforms (Gartner published its first Magic Quadrant for the category in June 2026, with vendors like IBM, ServiceNow, Credo AI, and OneTrust) manage AI policy, model risk assessments, and compliance records. Tools for governing AI use, like Push Security, Harmonic, Island, and Akamai Workforce Protector, discover how employees actually use AI and enforce that policy where they use it. Most organizations need both, and a governance program is only as strong as the enforcement underneath it.
What are the best AI security tools for enterprises?
AI security covers several different problems, and the best tool depends on which one you're solving. Securing how employees use AI, a large part of what's usually meant by generative AI security, is about controlling which AI tools and accounts people use and what data goes into them, and that's where tools like Push Security, Harmonic, Island, and SentinelOne's Prompt Security fit. Securing AI agents covers what autonomous agents do once they're deployed, with vendors like Zenity, Aembit, and Astrix Security. Securing the AI applications and models you build yourself covers testing, runtime protection, and model scanning, from vendors like Lakera, HiddenLayer, and Mindgard. A fourth category, AI-powered security tools, uses AI to improve existing security functions rather than securing AI at all. For most enterprises, employee use is where the exposure is today and where governance needs enforcement first.
What's the difference between securing AI use and securing AI agents?
Securing AI use governs what people do with AI tools: which tools and accounts they use, what data they share, and what access they grant. Securing AI agents governs what autonomous agents do after deployment. Agent governance is increasingly built into the platforms where agents run, such as Microsoft Agent 365 and ServiceNow AI Control Tower. For most organizations, governing AI use comes first, and the moment an employee approves an agent's access through an OAuth grant is itself a point where policy can be enforced.
Which security tools provide audit trails for AI activity?
Native audit logs cover one vendor's corporate tenant, usually at its enterprise tier. Microsoft Purview can audit third-party AI apps for organizations running its stack, with some capabilities limited to Edge. Browser-layer tools like Push can stream AI conversation logs (prompts, and optionally responses) from every AI app used in enrolled browsers into your SIEM. An audit trail is how you prove your AI policy held, which auditors and regulators increasingly ask for.
Can my SWG or CASB control AI usage?
They can cover part of it. SSE platforms from vendors like Zscaler and Palo Alto Networks can allow or block AI apps, apply tenant restrictions, and inspect decrypted traffic for sensitive data. What they see is the traffic, not the session, so they have less context about which account is in use, which extension is acting, or what the user is doing. Browser-layer controls add that context, and the two work well together.
What should I look for in a shadow AI tool?
Start with how the tool enforces policy, not how it builds an inventory. Does it see personal accounts on approved tools? Does it offer graduated enforcement and redirect users to approved tools, or only allow and block? Was its browser extension built in from the start or added to a product built for something else? Does it detect attacks that come in through AI tools, such as malicious extensions and consent phishing? Can it deploy to BYOD and contractor devices, and does it feed your SIEM?
How do I detect and manage MCP connections?
It depends on where the connection is made. MCP (Model Context Protocol) connections let AI tools read from and act on other systems, and they show up in three places. Connectors added inside web AI apps like ChatGPT and Claude, and remote MCP servers that use OAuth, are requested and approved in the browser, so browser-layer tools can see them and stop unapproved ones at the point of connection. Push can block unapproved MCP connection requests in real time.
Local MCP servers that developers configure for tools like Claude Code and Cursor live in config files on the device, so they're visible to endpoint tools rather than the browser. MCP servers your own teams deploy for internal agents fall under agent security. Most organizations need the browser view for employees and an endpoint view for developer machines.
