No solution approach in the current research connects this vendor to this use case.
Vendor research
Zscaler AI Security
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
No related approach foundBack 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
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity 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.
- $2.7B annual revenue (2025-07-31)
- 1000+ employees
- Founded 2007
Company context
Public company (Nasdaq: ZS)
Zscaler reports 9.4K+ customers, 40% of the Forbes Global 2000, and 500B+ daily transactions secured
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 18 records
- Source-checked records
- 17
- Evaluation requirements
- 18 in this research model
- Unresolved requirements
- 1
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.
Founded2007
HeadquartersSan Jose, California
OwnershipPublic company (Nasdaq: ZS)
Employees1000+
Capital and scalePlatform provider
Zscaler
- Latest annual company revenue
- $2.7B
- Operating scale
- Zscaler reports 9.4K+ customers, 40% of the Forbes Global 2000, and 500B+ daily transactions secured
- Backing context
- Public company; pre-IPO venture backing not material to current enterprise buying context
Zscaler, Inc. · period ended 2025-07-31 · filed 2025-09-11
Founders and leadershipBackground context
Current leadership and public filings provide more useful context for this company than historical founder information.
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
- 2007
- Workforce scale
- 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 limits5 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 sourceZscaler AI Security pageZscaler AI Security describes AI Asset Management, AI Access, Endpoint AI Security, AI Access Graph, AI Broker, AI red teaming, and runtime protection.Company sourceZscaler AI Access Security pageZscaler AI Access Security describes visibility, access control, prompt and response classification, inline DLP, and developer-tool controls.Company sourceZscaler about pageZscaler reports customer count, Forbes Global 2000 penetration, and daily transaction scale on its about page.Company information sourcecompany_intelSupports the company facts shown in this profile.Regulatory filing10-K annual filingZscaler, Inc. (ZS) · period ended 2025-07-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Network and cloud access controlsCore product focusEmployee AI access and usage controlsCore product focusAI data protectionRelated coverageBrowser and extension controlsRelated coverageExposed AI asset discoveryRelated coverageAI testing and adversarial assuranceRelated coverageAI gateway and tool-connection controlsRelated coverageEndpoint AI application controlsRelated coverageCoding-agent and developer workstation securityRelated coverage
Buyer context
- For current Zscaler SSE/SASE customers, evaluate AI Access, AI Asset Management, AI Guardrails, AI Broker, and AI Access Graph as extension paths before adding a net-new point product.
- Useful diligence questions include whether AI controls are available in the buyer's existing tenant, which traffic paths are in scope, and how AI findings export into SIEM, data loss prevention (DLP), cloud access security broker (CASB), and data-security workflows.
- Zscaler has strong workforce, browser, software as a service (SaaS), and network-placement relevance; validate custom AI app and agent coverage against each use case.
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
- 17
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 16
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 16
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 4
- 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
Strong 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
Limited 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
Strong public supportTest 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
Limited public supportDiscover, 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
Strong public supportAuthorize, 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
Limited public supportInventory, 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
No supporting claim foundVisibility, 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
Strong public supportPublicly 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 18 source records. Open additional records only when needed.
Zscaler claims AI Asset Management discovers and maps the AI ecosystem, including shadow AI, risky apps, models, and pipelines.
Discover and map your entire AI ecosystem, from shadow AI to risky apps, models, and pipelines.
Zscaler claims AI Access Security detects and classifies AI apps, including embedded AI in software as a service (SaaS) applications.
Detect and classify thousands of AI apps including AI embedded in popular SaaS applications.
Zscaler claims AI Access Security provides visibility into user interaction with AI apps, including prompt and response extraction and classification.
Get a clear view of how users interact with your apps, including deep insights with prompt/response extraction and classification.
Zscaler claims AI Access Security can warn, block, isolate, and restrict copy-paste actions within AI applications.
Use flexible policies to warn, block, or enforce browser isolation, giving you full control over copy-paste actions within AI applications.
Zscaler claims AI Access Security blocks sensitive data loss in prompts using inline data loss prevention (DLP) dictionaries for source code, PII, PCI, PHI, and other data types.
Block the loss of sensitive data in prompts with powerful inline DLP across 100+ DLP dictionaries like Source Code, PII, PCI, PHI, and more.
Zscaler claims Endpoint AI Security detects AI threats on employee devices across browsers, extensions, and plugins.
Find and stop AI threats on employee devices in browsers, extensions, and plugins that traditional EDRs were never built to see.
Show 12 additional evidence records
Zscaler claims AI red teaming can conduct vulnerability assessments and simulate attacks on AI systems.
Conduct vulnerability assessments and simulate attacks on your AI systems.
Zscaler claims AI Access Graph provides real-time visibility into how AI agents use data and identities.
Get real-time visibility into how AI agents use data and identities, reducing unnecessary access and tracking data lineage across every channel.
Zscaler claims AI Broker secures agentic communications through Model Context Protocol (MCP) and agent-to-agent (A2A) brokers with fine-grained access policies.
Secure agentic communications through MCP and A2A brokers and enforce fine-grained access policies across every enterprise AI agent.
Zscaler claims AI Access Graph tracks AI agent use of identities and data, but the reviewed source does not describe full non-human identity (NHI) credential lifecycle management.
Get real-time visibility into how AI agents use data and identities, reducing unnecessary access and tracking data lineage across every channel.
Zscaler claims AI asset management, AI bill of materials, posture and risk assessment, compliance heat maps, governance status, access policy, and data-lineage visibility across the AI lifecycle.
Discover and map your entire AI ecosystem, from shadow AI to risky apps, models, and pipelines.
Zscaler claims automated red teaming across the AI lifecycle, including new adversarial testing for Model Context Protocol (MCP) servers and dynamic risk assessment.
Introduces AI red teaming for MCP servers, a standalone prompt hardening service, and compliance heat maps.
Zscaler claims AI BOM discovery of models, Model Context Protocol (MCP) servers, development tools, and data pipelines plus risk scoring, codebase scanning, and Model Context Protocol (MCP) capability exposure analysis.
AI BOM: Discover AI models, MCP servers, development tools, and data pipelines.
Zscaler claims Model Context Protocol (MCP) and agent-to-agent (A2A) brokers, fine-grained agent access policy, prompt hardening, AI guardrails, and runtime protection across enterprise AI agents.
Secure agentic communications through MCP and A2A brokers and enforce fine-grained access policies across every enterprise AI agent.
Zscaler claims an AI Access Graph that maps AI-agent use of data and identities and enables fine-grained access policies for enterprise agents across Model Context Protocol (MCP) and agent-to-agent (A2A) communications.
Get real-time visibility into how AI agents use data and identities, reducing unnecessary access.
Zscaler claims endpoint discovery and protection for AI activity in browsers, extensions, and plugins plus agentic codebase scanning and Model Context Protocol (MCP) risk analysis for filesystem, network, and code-execution exposure.
Uncover risks in agentic codebases through code scanning, and extend visibility to AI activity on endpoints.
Zscaler AI Security materials reviewed did not establish customer AI workload cost attribution, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Zscaler documents AI Guard as a software as a service (SaaS) subscription with a platform fee and purchased token allocation for the subscription term, with additional tokens ordered through sales or a channel partner.
AI Guard is a Software as a Service product licensed based on a platform subscription fee and the number of tokens purchased.