Vendor research
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.
Company scale
A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company context
SentinelOne positions Prompt Security as part of Securing AI across employee prompts, custom apps, agents, data, infrastructure, endpoint, cloud, identity, and SecOps
Company intelligence
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Series A · $18M · 2024-11-20
SentinelOne, Inc. (S) · period ended 2026-01-31 · filed 2026-03-19
Jump Capital · Hetz Ventures · Ridge Ventures · Okta Ventures · F5
Co-Founder & CEO before acquisition
Co-Founder & CTO before acquisition
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
Related frameworks
Evaluation questions
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.
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 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.
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.
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.
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.
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
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.
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.
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.
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.
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.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Inventory 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.
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
Test 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.
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
Discover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
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.
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.
Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
Register 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.
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
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.
Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Publicly 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
Showing the first 6 of 19 source records. Open additional records only when needed.
Get complete, searchable logs of every interaction for risk management.
Run pre-production red teaming, prioritize issues using risk scoring and evidence, and confidently ship production-ready AI applications.
MCP risk scoring, dynamically assessing over 13,000 MCP servers on GitHub to identify emerging threats.
Inspecting every request and response in real time to protect sensitive data and information.
Allow/block by user, server, or action according to your security policy.
Fine-grained policies that determine which MCPs are allowed, which commands can be run, and under what circumstances.
No quoted source text is recorded for this claim.
Inventory every AI tool and code assistant in use, including unsanctioned shadow AI
No quoted source text is recorded for this claim.
Yes. Prompt Security integrates with AI code assistants like GitHub Copilot and Claude Code to prevent secrets, credentials, and proprietary code from leaking into prompts, while enforcing secure AI usage policies across development environments.
Set granular, role-based controls for who can use AI, how, and with what data.
Redact sensitive data and enforce policies across 15,000+ AI services in real time
Get a searchable audit log of every agent action, decision, and enterprise system interaction
Allow/block by user, server, or action
Enforce least-privilege access so agents operate only within their defined scope
Attain visibility, security and governance for AI tools usage
Block prompt injections, data leaks and toxic LLM content
No quoted source text is recorded for this claim.
SentinelOne connects endpoint, cloud, identity, and AI telemetry so teams can see risk clearly without stitching tools together.