Vendor research
Island Enterprise Browser
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
Related research availableBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Scaled?
ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.
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ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $375M known funding
- 250-1000 employees
- Founded 2020
Company context
Private independent company; no acquisition or parent-company claim found on reviewed Island-controlled pages
Island positions its Enterprise Platform across enterprise browser, enterprise AI, network, software as a service (SaaS)/web app access, BYOD, contractors, privileged access, and safe browsing use cases
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 14
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 5
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
FoundedFounding year not stated on reviewed Island-controlled pages
HeadquartersDallas, London, and Tel Aviv offices listed on reviewed Island-controlled pages; headquarters not specified on reviewed page
OwnershipPrivate independent company; no acquisition or parent-company claim found on reviewed Island-controlled pages
Employees250-1000
Capital and scaleIndependent company
Island
- Known funding
- $375M
- Operating scale
- Island positions its Enterprise Platform across enterprise browser, enterprise AI, network, SaaS/web app access, BYOD, contractors, privileged access, and safe browsing use cases
- Backing context
- Island says it has raised $750 million from investment funds
Series E · 2025-07-14
Founders and leadership2 people listed
- Mike FeyCurrent role listed
Co-Founder & CEO
- Dan AmigaCurrent role listed
Co-Founder & CTO
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- Founding year not stated on reviewed Island-controlled pages
- Workforce scale
- 250-1000
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
Company sources and research limits6 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceIsland Enterprise AI pageIsland says its AI offering gives visibility and control across browser, desktop, extensions, and network, with data protection, prompt-injection mitigation, governed agents, and spend controls.Company sourceIsland SaaS and Web Apps pageIsland says the Enterprise Browser governs access, stops data leakage, and shows behavior across SaaS and web apps.Company sourceIsland about pageIsland says it has raised $750 million and lists Dallas, London, and Tel Aviv offices.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourcecompany_intelSupports the company facts shown in this profile.Company information sourceStored company websiteSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Browser and extension controlsCore product focusEmployee AI access and usage controlsCore product focusAI data protectionCore product focusNetwork and cloud access controlsRelated coverageAI application runtime protectionRelated coverageAction-taking agent safeguardsRelated coverageAI usage and cost controlsRelated coverage
Buyer context
- Treat Island as an enterprise-browser and AI-governance platform candidate with strong browser, desktop, extension, network, software as a service (SaaS)/web-app, data loss prevention (DLP), and AI usage controls.
- Public evidence supports AI usage visibility/control, corporate versus personal AI tenant boundaries, prompt/response and agent audit logs, prompt-injection controls, governed agents, AI model routing, usage tracking, and software as a service (SaaS)/web-app behavior visibility.
- Public pages reviewed did not expose non-human identity (NHI)/service-account lifecycle, agent-to-agent trust controls, or a public pricing unit beyond quote/demo motions.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCSA AI Controls Matrix
- Requirements with public support
- 13
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 3
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 06Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 07Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 08Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
Limited public supportInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
Strong public supportMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Strong public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Strong public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
Strong public supportApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Strong public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
No supporting claim foundDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
No supporting claim foundAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
No supporting claim foundInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Limited public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Limited public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
Strong public supportVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 19 source records. Open additional records only when needed.
Island claims unified visibility, governance, data protection, policy, audit, human oversight, usage analytics, and return on investment (ROI) reporting across browser, desktop, extension, network, embedded AI, and governed agents.
Every AI interaction across the organization is visible, governed, and protected from the start.
Island AI Services materials reviewed did not provide a public customer-facing product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
Island AI Services materials reviewed did not establish model artifact scanning, provenance, signing, dependency or Model Context Protocol (MCP) component analysis, tamper detection, or model-registry release controls.
No quoted source text is recorded for this claim.
Island claims one policy engine across AI browser, desktop, extensions, network, prompts, outputs, agents, and more than 500 governed Model Context Protocol (MCP) integrations with prompt-injection mitigation and human checkpoints.
MCP Gateway with governed access to 500+ integrations.
Island claims governed agents with defined workflows, scoped permissions, enterprise identity and policy inheritance, human-in-the-loop controls, and complete auditability.
Build, run, and share AI agents with full oversight, scoped permissions, and complete auditability.
Island claims browser, desktop, extension, network, data loss prevention (DLP), and AI policy controls that protect proprietary source code and govern AI applications, locally running agents, outputs, and developer data movement.
Sensitive data is safeguarded before it ever reaches an AI provider, and AI responses are protected before they reach the user.
Show 13 additional evidence records
Island claims it can see and govern all user actions involving AI, including unknown shadow AI tools.
Island can see and govern all user actions involving AI, including with unknown shadow AI tools.
Island claims it captures which AI applications and large language models (LLMs) employees access and what data moves in and out of AI tools.
Island captures AI usage that other tools can't see, whether it happens in the browser or via desktop apps, whether the user is using corporate or personal accounts, and whether an AI app is accessing internal tools.
Island claims AI Protect gives visibility and control across browser, desktop, extensions, and network.
AI Protect See, control, and protect all AI usage across the browser, desktop, extensions, and network.
Island claims data boundaries and data loss prevention (DLP) can prevent corporate data from being entered into unapproved AI apps and services.
Using data boundaries and data loss prevention, Island can ensure your corporate data doesn’t get inputted into unsanctioned AI apps and services.
Island claims complete visibility and control across every AI interaction with data protection and prompt-injection mitigation built in.
Complete visibility and control across every AI interaction, with data protection and prompt injection mitigation built in.
Island claims the Enterprise Browser governs access, stops data leakage, and shows behavior across software as a service (SaaS) and web apps.
With The Enterprise Browser, organizations govern access, stop data leakage, and see all behavior across all SaaS and web apps.
Island claims browser-level controls combat prompt injection and risky extensions.
Browser-level controls combat prompt injection and risky extensions.
Island claims detailed audit logs of prompts, responses, and agent activity.
Island distinguishes corporate and personal tenants, enforces data boundaries before data reaches AI providers and captures detailed audit logs of prompts, responses, and agent activity.
Island materials reviewed did not provide a public claim for agent-to-agent (A2A), inter-agent communication control, agent trust graphs, mutual TLS (mTLS), or inter-agent authorization.
No quoted source text is recorded for this claim.
Island materials reviewed did not provide a public claim for non-human identity (NHI) ownership, service-account lifecycle, credential rotation, scoped credentials, or least-privilege governance.
No quoted source text is recorded for this claim.
Island claims model routing, usage tracking, redundant-tool elimination, and cost-structure controls for AI.
Control spend, maximize ROI. Route the right models to the right users based on task and role. Eliminate redundant tools, track usage, and keep your cost structure in check without slowing your teams down.
Island materials reviewed did not provide a public per-user, per-seat, platform, usage-based, or enterprise pricing model.
No quoted source text is recorded for this claim.
Island claims it can embed any AI provider into user workflows enriched by approved enterprise context.
Embed any AI provider into any user workflow, enriched by the enterprise context you approve.