No solution approach in the current research connects this vendor to this use case.
Vendor research
Prompt Security / SentinelOne
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.
- $1B annual revenue (2026-01-31)
- $23M known funding
- 11-50 employees
- Founded 2023
Company context
Public-company subsidiary / product line under SentinelOne (NYSE: S)
SentinelOne positions Prompt Security as part of Securing AI across employee prompts, custom apps, agents, data, infrastructure, endpoint, cloud, identity, and SecOps
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 16
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 3
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.
Founded2023
HeadquartersTel Aviv, Israel; U.S. presence commonly listed in New York
OwnershipPublic-company subsidiary / product line under SentinelOne (NYSE: S)
Employees11-50
Capital and scaleIndependent company
Prompt Security
- Known funding
- $23M
- Current owner annual revenue
- $1B
- Operating scale
- SentinelOne positions Prompt Security as part of Securing AI across employee prompts, custom apps, agents, data, infrastructure, endpoint, cloud, identity, and SecOps
- Backing context
- $5M seed led by Hetz Ventures; $18M Series A led by Jump Capital with Hetz Ventures, Ridge Ventures, Okta, and F5 participation; acquired by SentinelOne for cash and stock
Series A · $18M · 2024-11-20
SentinelOne, Inc. (S) · period ended 2026-01-31 · filed 2026-03-19
Named investors
Jump Capital · Hetz Ventures · Ridge Ventures · Okta Ventures · F5
Founders and leadership2 people listed
- Itamar GolanStatus not confirmed
Co-Founder & CEO before acquisition
- Lior DrihemStatus not confirmed
Co-Founder & CTO before acquisition
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
- 2023
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits8 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 sourceSentinelOne acquisition announcementSentinelOne announced a definitive agreement to acquire Prompt Security for GenAI and agent security.Company sourceSentinelOne Prompt Security pageSentinelOne positions Prompt Security as AI usage protection across prompts, models, and responses.Company sourceSentinelOne endpoint security pageSentinelOne describes Singularity Endpoint as AI-powered protection, detection, response, and identity-aware endpoint security.Company sourcePrompt Security seed announcementPrompt Security announced $5M seed funding led by Hetz Ventures with Four Rivers and angel participation.Company sourcePrompt Security Series A announcementPrompt Security announced $18M Series A led by Jump Capital with Hetz, Ridge, Okta, and F5 participation.Company sourcePrompt Security company pagePrompt's about page lists founders and startup backers.Company information sourcePrompt Security LinkedIn company profileSupports the company facts shown in this profile.Regulatory filing10-K annual filingSentinelOne, Inc. (S) · period ended 2026-01-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- For existing SentinelOne EDR and Singularity Endpoint customers, treat Prompt Security as a platform-extension candidate and confirm SKU, console, data-retention, and rollout boundaries.
- Public SentinelOne materials connect endpoint, cloud, identity, and AI telemetry, which may reduce investigation and procurement friction for SentinelOne-installed accounts.
- Do not assume EDR alone solves AI security; Prompt's value is browser, desktop, application programming interface (API), Model Context Protocol (MCP), and agent runtime control layered alongside endpoint protection.
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 referenceCIS Critical Security Controls
- Requirements with public support
- 15
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 15
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 15
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 15
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 15
- Related requirements
- 13
- References
- 25
- 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.
- 03Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 04Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 05Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 06Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
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
No supporting claim foundInventory 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
Limited 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
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
Limited 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
Strong 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
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.
Prompt Security claims enterprise AI and Model Context Protocol (MCP) discovery, risk scoring, policy enforcement, searchable interaction logs, role-based controls, compliance policy, drift monitoring, and human oversight for agentic systems.
Get complete, searchable logs of every interaction for risk management.
Prompt Security claims automated preproduction and continuous red teaming for prompt injection, data exposure, privilege escalation, jailbreaks, unsafe agent behavior, drift, and other AI-specific risks with evidence and remediation guidance.
Run pre-production red teaming, prioritize issues using risk scoring and evidence, and confidently ship production-ready AI applications.
Prompt Security claims dynamic risk scoring of more than 13,000 Model Context Protocol (MCP) servers, vulnerability profiles, certification checks, shadow-server discovery, and agent skill integrity and drift checks.
MCP risk scoring, dynamically assessing over 13,000 MCP servers on GitHub to identify emerging threats.
Prompt Security claims an AI and Model Context Protocol (MCP) Gateway that inspects requests, responses, prompts, templates, agent actions, and server interactions in real time with allow or block policy, threat intelligence, data loss prevention (DLP), and endpoint enforcement.
Inspecting every request and response in real time to protect sensitive data and information.
Prompt Security claims granular policy by user, group, server, and action plus attribution of AI use, data sharing, agent responses, and Model Context Protocol (MCP) activity.
Allow/block by user, server, or action according to your security policy.
Prompt Security claims endpoint and integrated development environment (IDE)-integrated governance for coding assistants, Model Context Protocol (MCP) servers, exposed commands, secrets, PII, generated code, prompt responses, and action-level policy across tools including Cursor and GitHub Copilot.
Fine-grained policies that determine which MCPs are allowed, which commands can be run, and under what circumstances.
Show 13 additional evidence records
SentinelOne Prompt Security materials reviewed did not provide a public product claim for AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
SentinelOne Prompt Security claims it inventories every AI tool and code assistant in use, including unapproved shadow AI.
Inventory every AI tool and code assistant in use, including unsanctioned shadow AI
No public claim found for this capability.
No quoted source text is recorded for this claim.
SentinelOne Prompt Security claims integration with AI code assistants such as GitHub Copilot and Claude Code for secure AI usage policy enforcement in development environments.
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.
SentinelOne Prompt Security claims granular, role-based controls for who can use AI, how, and with what data.
Set granular, role-based controls for who can use AI, how, and with what data.
SentinelOne Prompt Security claims it redacts sensitive data and enforces policies across 15,000+ AI services in real time.
Redact sensitive data and enforce policies across 15,000+ AI services in real time
SentinelOne Prompt Security claims searchable audit logs for every agent action, decision, and enterprise system interaction.
Get a searchable audit log of every agent action, decision, and enterprise system interaction
Prompt Security claims runtime policy controls for Model Context Protocol (MCP) interactions, including allow or block decisions by user, server, and action.
Allow/block by user, server, or action
Prompt Security from SentinelOne claims visibility into agents and Model Context Protocol (MCP) servers plus least-privilege access enforcement for AI agents.
Enforce least-privilege access so agents operate only within their defined scope
Prompt Security claims it provides employees with visibility, security, and governance over AI tools usage.
Attain visibility, security and governance for AI tools usage
Prompt Security claims it blocks prompt injections, data leaks, and toxic large language model (LLM) content for homegrown generative AI apps.
Block prompt injections, data leaks and toxic LLM content
No public claim found for this capability.
No quoted source text is recorded for this claim.
SentinelOne positions AI security as connected with endpoint, cloud, identity, and AI telemetry, making Prompt Security a platform-extension candidate for existing Singularity Endpoint customers.
SentinelOne connects endpoint, cloud, identity, and AI telemetry so teams can see risk clearly without stitching tools together.