Vendor research
Noma 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
Scaled?
ScaledA private provider with at least $100M in known funding or at least 250 employees.This is a company-scale signal, not a product-quality rating.
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ScaledA private provider with at least $100M in known funding or at least 250 employees.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.
- $132M known funding
- Employee scale not sourced
- Private-company revenue and profitability not sourced
Company context
Private, VC-backed
Came out of stealth in 2024 and reported more than 1,300% ARR growth in the following year
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 15
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 4
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
HeadquartersNew York, New York
OwnershipPrivate, VC-backed
EmployeesNot yet sourced
Capital and scaleIndependent company
Noma Security
- Known funding
- $132M
- Operating scale
- Came out of stealth in 2024 and reported more than 1,300% ARR growth in the following year
- Backing context
- $132M+ raised across Series A and B, including a $100M Series B led by Evolution Equity Partners; investors include Ballistic Ventures, Glilot Capital, Cyber Club London, Databricks Ventures, and SVCI
Series B · $100M · 2025-07-31
Named investors
Evolution Equity Partners · Ballistic Ventures · Glilot Capital · Cyber Club London · Databricks Ventures · SVCI
Founders and leadership2 people listed
- Niv BraunCurrent role listed
Co-Founder & CEO
- Alon TronCurrent 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
- 2023
- Workforce scale
- Not yet sourced
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Current employee range is not yet supported by a public source.
- A current hiring source is not available, so the count is not shown.
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 sourceNoma platform pageNoma reports $132M+ raised and announced a $100M Series B led by Evolution Equity Partners alongside rapid ARR growth.Company sourceNoma Security LinkedIn profilePublic profile lists New York headquarters and 2023 founding year.Company information sourceStored company websiteSupports the company facts shown in this profile.Company information sourceNoma Series B announcementSupports the company facts shown in this profile.Company information sourceNoma Security current CEO announcementSupports the company facts shown in this profile.Company information sourceNoma Security executive leadership 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
- One of the strongest funding and category-visibility signals among AI-agent security specialists.
- Buyer diligence should validate how broad AI lifecycle claims translate into controls for approved large language model (LLM) gateways, Model Context Protocol (MCP), and agent runtime.
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
- 14
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 14
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 14
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 14
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 14
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 14
- 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.
- 03AI-feature discovery in business applications
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.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 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
Strong public supportInventory 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
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
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
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
Strong 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
Strong 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
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 19 source records. Open additional records only when needed.
Noma claims continuous discovery, an enterprise agent and Model Context Protocol (MCP) registry, risk context, accountable ownership, and approved, review, or blocked governance states across AI systems.
Every agent, connected MCP server, and tool surfaces in a dynamic registry with context already attached.
Noma claims agentic red teaming that simulates real-world attacks to identify vulnerabilities in autonomous AI systems before production.
Advanced simulation of real-world agent attacks to identify vulnerabilities in autonomous AI systems before production exploitation.
Noma claims continuous scanning of agent supply chains across tool integrations, Model Context Protocol (MCP) servers, agent frameworks, third-party application programming interfaces (APIs), and model dependencies.
Continuous scanning of agent supply chains for vulnerabilities across toolset integrations, MCP server connections, agent frameworks, third-party APIs, and model dependencies.
Noma claims identity-aware runtime enforcement across Model Context Protocol (MCP) connections, prompts, tool calls, data access, agent actions, model responses, and application programming interface (API) traffic.
Every MCP connection is checked against the registry the moment it’s established.
Noma claims a distinct attributable identity for each autonomous agent, owner and permission context, and policy-based authorization for agents, Model Context Protocol (MCP) servers, and tools.
Noma Agent Access Control gives each autonomous agent a distinct, attributable identity when it connects to MCP servers and tools.
Noma claims discovery, inventory, permission context, access control, and runtime protection for local and coding agents including Claude Code, Cursor, and GitHub Copilot.
Continuous, automatic inventory of every AI agent, MCP server, and tool across local and coding agents.
Show 13 additional evidence records
Noma 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.
Noma claims it discovers, governs, and protects enterprise AI and agents across homegrown AI, software as a service (SaaS) agents, and coding assistants.
Noma discovers, governs, and protects AI and Agents across the enterprise, from homegrown AI to SaaS agents and coding assistants.
Noma claims coverage for commercial AI and software as a service (SaaS) agent platforms already embedded in workflows, including major enterprise agent platforms.
Extend coverage to commercial AI and agent platforms already embedded in your workflows. Native integrations with Microsoft Copilot Studio, Salesforce AgentForce, ServiceNow, and 80+ other. Agentless deployment, with no code changes required.
Noma claims runtime privacy policies can prevent sensitive data from leaving the environment during AI usage.
Privacy policies block sensitive data from leaving your environment.
Noma claims it can discover, test, and protect custom AI applications and agents through application programming interfaces (APIs), SDKs, and centralized gateway enforcement.
Discover, test, and protect custom-built AI applications and agents through our REST API, native Python and JavaScript SDKs for frameworks like LangChain and CrewAI, or our centralized gateway for unified policy enforcement.
Noma claims discovery provides visibility and context across models, agents, Model Context Protocol (MCP) servers, data sources, and their dependency chains.
Discovery provides visibility and deep context for your entire AI landscape: every model, every agent, every MCP server, every data source, and crucially, how they all connect.
Noma claims it can monitor AI and agentic communication in real time and enforce runtime policies.
See all AI and agentic communication as it happens and enforce policies at runtime.
Noma claims it supports identity and access controls for AI applications and agents as part of pre-deployment boundaries.
setting proper identity and access controls for every AI application and agent
No public claim found for this capability.
No quoted source text is recorded for this claim.
Noma claims runtime policy enforcement for AI and agentic communication.
See all AI and agentic communication as it happens and enforce policies at runtime.
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.
Noma claims it discovers, governs, and protects AI and agents across enterprise homegrown AI and software as a service (SaaS) agents.
Noma discovers, governs, and protects AI and Agents across the enterprise, from homegrown AI to SaaS agents