Solution approach
Endpoint AI application controls
Discover and control desktop AI applications and local agents, including files, processes, commands, network activity, credentials, and tool connections.
View research coverageThese counts describe available public research, not product quality or suitability.Expand
- Vendor profiles
- 11 shown in this view
- Security requirements
- 8 to review
- Strong public support
- 73 requirement records
- Limited public support
- 11 requirement records
- Open research
- 4 with no supporting claim · 0 incomplete
Evaluation guide
What buyers should verify
Unapproved AI use discovery
Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
- An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- The test user's AI usage activity can be filtered or exported with AI-specific context.
- Approved and unapproved AI destinations or accounts are distinguishable in the evidence record.
Approved AI usage monitoring
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
- Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- Model or provider usage can be filtered for the scoped workspace, tenant, gateway, or platform.
Controls for unapproved AI use
Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
- A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- The control event records policy reason, user, destination, action, and timestamp.
- An allowlist or exception path changes the outcome for an approved AI destination.
Sensitive-data protection for generative AI
Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
- Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- The evidence record shows data class, user, app or model, action, and timestamp.
Browser and business-application controls
Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
- A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
- The policy decision includes user, app, session, or identity-aware context.
- The event record shows affected action, user, app, and timestamp.
AI gateway, tool-connection, and runtime controls
Mediate 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.
- A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
- A test policy allows, blocks, transforms, redirects, or rate-limits the request with an explicit reason.
- The audit event records caller identity, destination, tool or model, policy decision, and timestamp.
Action-taking agent monitoring
Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
- A test agent run captures plan, steps, tool calls, outcome, and timestamps.
- Agent memory, delegated task, autonomy, or runtime decision detail is visible in a timeline or log.
- Out-of-policy or unusual agent behavior can be flagged or filtered for investigation.
AI coding-agent and workstation security
Discover 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.
- A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
- A policy blocks or records a dangerous command, sensitive-file access, secret exposure, network action, or risky package installation.
- The evidence ties the action to developer, agent, repository or workspace, policy, and timestamp.
Vendor research
Vendors with public research for this approach
Vendors are grouped by whether this approach is a core product focus or related coverage, then ordered by documented support across the requirements shown. This organizes public research coverage; it is not a product ranking or recommendation.
Company maturity remains a filter and profile attribute; it does not affect the research-coverage order.