Vendor research
Entro 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
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
Emerging?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.
?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.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.
- $24M known funding
- 50-250 employees
- Founded 2022
Company context
Private, VC-backed
Entro reports dozens of paying customers and names SolarWinds, Elastic, Kayak, Regatta, Silverfort, and Sprinklr among its customer evidence
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 21 records
- Source-checked records
- 9
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 9
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.
Founded2022
HeadquartersTel Aviv, Israel
OwnershipPrivate, VC-backed
Employees50-250
Capital and scaleIndependent company
Entro Security
- Known funding
- $24M
- Operating scale
- Entro reports dozens of paying customers and names SolarWinds, Elastic, Kayak, Regatta, Silverfort, and Sprinklr among its customer evidence
- Backing context
- $18M Series A / $24M total reported; early seed led by StageOne Ventures and Hyperwise Ventures
Series A · $18M · 2024-06-18
Named investors
Dell Technologies Capital · StageOne Ventures · Hyperwise Ventures
Founders and leadership2 people listed
- Itzik AlvasCurrent role listed
Co-Founder & CEO
- Adam CherikiCurrent 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
- 2022
- Workforce scale
- 50-250
- 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 limits9 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 sourceCB Insights company profilePublic profile lists Entro as founded in 2022, based in Tel Aviv, and Series A stage.Company sourceTechCrunch seed coverageTechCrunch reported Entro's $6M seed round led by StageOne Ventures and Hyperwise Ventures.Company sourceEntro Series A announcementEntro's CEO announced the $18M Series A and identified dozens of paying customers, including SolarWinds, Elastic, Kayak, Regatta, and Silverfort.Company sourceEntro customer pageEntro publishes named customer spotlights and describes deployments across Elastic, Sprinklr, SafeBreach, and Deep Voice.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceEntro current co-founder briefingSupports the company facts shown in this profile.Company information sourceEntro founding announcementSupports the company facts shown in this profile.Company information sourceEntro current author and leadership indexSupports the company facts shown in this profile.Company information sourceEntro funding historySupports 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.
Buyer context
- Relevant to non-human identity (NHI), secrets, and service-account lifecycle issues that sit adjacent to AI-agent security.
- Named customers improve the enterprise-adoption signal, but $24M in known funding remains below the tool's growth-stage capital threshold and private-company revenue and profitability are not disclosed.
- Buyer diligence should confirm current AI-agent coverage versus the original secrets-security foundation, along with runway, support capacity, renewal history, and referenceable deployments.
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
- 9
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 9
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 9
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 9
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 9
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 2
- 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.
- 03Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 04Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 05AI governance, risk, and compliance
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
- 06AI governance, risk, and compliance
A policy, assessment, approval, exception, or remediation workflow changes the governed state of the test system.
- 07AI gateway, tool-connection, and runtime controls
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
- 08AI gateway, tool-connection, and runtime controls
A test policy allows, blocks, transforms, redirects, or rate-limits the request with an explicit reason.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Limited 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
No supporting claim foundMonitor 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
No supporting claim foundDetect, 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
No supporting claim foundApply 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
No supporting claim foundProtect 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
Limited 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
Strong 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
Strong 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
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 21 source records. Open additional records only when needed.
Entro claims discovery, ownership, lineage, blast-radius mapping, approval workflow, lifecycle provisioning and offboarding, continuous policy, segregation of duties, compliance dashboards, and audit-ready reports for agents and NHIs.
Every discovered agent and identity is mapped with ownership, permissions, lineage, and blast radius.
Entro agent and non-human identity (NHI) security materials reviewed did not provide a public 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.
Entro agent and non-human identity (NHI) security materials reviewed did not establish model artifact scanning, provenance, signing, model dependency analysis, tamper detection, or model-registry release controls.
No quoted source text is recorded for this claim.
Entro claims real-time identity policy across agents and NHIs, just-in-time scoped access, intent monitoring, anomaly detection, and Model Context Protocol (MCP) session auditing for prompts, servers, and agent contacts.
Entro’s AI Detection and Response monitors agent intent in real time, and catches threats at the identity layer.
Entro claims automated agent and non-human identity (NHI) provisioning, accountable ownership, minimum permissions, approval routing, just-in-time access, time bounds, continuous policy, access change, and offboarding.
Entro extends IGA to every AI agent and NHI in your environment.
Entro claims Claude Code intent and Model Context Protocol (MCP) session auditing, endpoint discovery of local agents and Model Context Protocol (MCP) configurations, secret scanning across the SDLC, and identity context for vibe-coding access paths.
The MCP Audit plugin tracks Claude Code sessions and every MCP server each agent contacts.
Show 15 additional evidence records
Entro Security platform 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.
Entro claims discovery of shadow agents and every agentic AI deployment across cloud, code, CI/CD, on-prem, and collaboration tools.
Discover every NHI, secret and agentic AI deployment across clouds, code, CI/CD, on-prem and collaboration tools
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Entro claims it can enforce least-privilege access, right-size excessive access, detect unapproved deployments, and remediate risky agent/non-human identity (NHI) behavior.
Detect and fix unsanctioned agent deployments, rogue MCP servers, and other unwanted behaviors before they escalate.
Entro claims agentic AI security maps agents to non-human identities, entitlements, creators, secrets, and permissions to contain overprivileged access.
mapping agents to non-human identities, entitlements , and creators
No public claim found for this capability.
No quoted source text is recorded for this claim.
Entro Security claims it monitors agents, infers their intent, and secures every action across the environment.
One platform to monitor agents, understand their intent, and secure every action across your environment.
Entro claims continuous monitoring of AI-agent permissions and activity can expose abuse and risky actions through NHIDR.
continuously monitor their permissions and activity on your resources
Entro claims lineage mapping for AI agents across Model Context Protocol (MCP), NHIs, secrets, resources, and environments to expose risky connections.
Entro maps every MCP, NHI, and secret across resources and environments to expose over-privileged access and risky connections.
Entro Security claims it secures the agentic AI and non-human identity (NHI) lifecycle from discovery and classification through observability and remediation.
Secure the Agentic AI and NHI Lifecycle
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.