Company context
Private, VC-backed
non-human identity (NHI) security company now explicitly positioning around AI agent identity lifecycle management
Vendor research
Token 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.
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.
- $28M known funding
- 1-10 employees
- Founded 2023
- Private-company revenue and profitability not sourced
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 20 records
- Source-checked records
- 10
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 8
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
OwnershipPrivate, VC-backed
Employees1-10
Capital and scaleIndependent company
Token Security
- Known funding
- $28M
- Operating scale
- NHI security company now explicitly positioning around AI agent identity lifecycle management
- Backing context
- $20M Series A led by Notable Capital; Token Security reported $28M total funding in January 2026, with participation from TLV Partners, Palo Alto Networks, CrowdStrike, Check Point, and Venafi
Series A · $20M · 2025-01-28
Named investors
Notable Capital · TLV Partners · Palo Alto Networks · CrowdStrike · Check Point · Venafi
Founders and leadership2 people listed
- Itamar ApelblatCurrent role listed
CEO & Co-Founder
- Ido ShlomoCurrent role listed
CTO & Co-Founder
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
- 1-10
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits6 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 sourceFinSMEs Series A profileToken Security raised $20M Series A led by Notable Capital, bringing total funding to $27M.Company sourceCB Insights company profilePublic profile lists Token Security as founded in 2023 and based in Tel Aviv.Company information sourceToken Security company pageSupports the company facts shown in this profile.Company information sourceToken Security 2025 reviewSupports the company facts shown in this profile.Company information sourceToken Security RSAC profileSupports the company facts shown in this profile.Company information sourceStartup Nation Central profileSupports 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 agent identity lifecycle, Model Context Protocol (MCP)/server discovery, and traceability requirements.
- Buyer diligence should examine depth of integrations across IdP, IGA, vault, cloud, software as a service (SaaS), and agent platforms.
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
- 10
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 10
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 10
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 10
- 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.
- 05Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 06Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 07AI governance, risk, and compliance
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
- 08AI governance, risk, and compliance
A policy, assessment, approval, exception, or remediation workflow changes the governed state of the test system.
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
Limited 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
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
Limited 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 20 source records. Open additional records only when needed.
Token Security claims lifecycle governance for AI agents with continuous discovery, ownership, intent, access, policy, compliance validation, audit trails, behavioral monitoring, and automated remediation from creation through retirement.
Token Security unifies Visibility, Control, and Governance into a single platform purpose-built for AI agents and non-human identities.
Token Security product 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.
Token Security product materials reviewed did not establish model artifact scanning, provenance, signing, dependency or Model Context Protocol (MCP) component analysis, tamper detection, or model-registry release controls.
No quoted source text is recorded for this claim.
Token Security claims behavioral monitoring, suspicious-activity alerting, runtime constraints, intent-based permission enforcement, dynamic risk response, and automated remediation for AI agents and their identities.
Automatically enforce least privilege as agent behavior and intent evolve.
Token Security claims discovery and lifecycle control for agent identities, owners, intent, credentials, tokens, roles, permissions, access paths, behavioral baselines, access reviews, least-privilege enforcement, remediation, and deprovisioning.
Token Security helps teams manage the full AI agent identity lifecycle.
Token Security claims discovery and governance of coding agents, custom GPTs, Model Context Protocol (MCP) servers, cloud and software as a service (SaaS) access, credentials, permissions, owners, intent, activity, and least-privilege boundaries.
Token automatically discovers and inventories every AI agent, custom GPT, coding agent, MCP server, and non-human identity.
Show 14 additional evidence records
Token Security product 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.
Token Security claims visibility, control, and governance for every AI agent in the environment.
Token Security delivers visibility, control, and governance of every AI Agent in your environment, so you can move faster and stay secure.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Token Security claims governance of AI agents from creation through retirement, enforcing ownership, intent, access, and governance over time.
Agents fail over time, not just at creation. Token enforces ownership, intent, access, and governance from an AI agent's creation through retirement.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Token Security claims AI-agent access control, right-sizing, least privilege, intent-aware enforcement, and automated remediation.
Token applies intent-aware least-privilege to agents, ensuring they have only the permissions needed for their purpose, and only for the time required.
Token Security claims identity intelligence correlates AI agents, humans, secrets, permissions, and data to reveal blast radius and enable remediation.
AI agents, humans, secrets, permissions, and data
Token Security claims it logs every AI-agent action across ecosystems for compliance and incident investigation.
Log every AI agent action across ecosystems, ensuring compliance and rapid incident investigation
Token Security claims monitoring and auditing of AI agent actions to maintain compliance in multi-agent ecosystems.
Traceability in a Multi-Agent Ecosystem Monitor and audit AI agent actions to maintain compliance
Token Security claims teams can manage the full AI agent identity lifecycle, enforce ownership, and decommission orphaned identities.
Token Security helps teams manage the full AI agent identity 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.