Vendor research

Runlayer

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.

AI spend and usage1 related approach

AI usage and cost controlsDirectly addresses · Track model and AI-service use, set budgets or limits, and identify unexpected spend.

Company scale

Growth stage
?

A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.

Company scale is separate from product features, effectiveness, and suitability.

$42M known funding
Employee scale not sourced
Private-company revenue and profitability not sourced

Company context

Private independent company

Runlayer says its catalog covers more than 18,000 MCPs and cites enterprise deployments at Gusto, Jane, and Homebase

Vendor website ↗Add to comparison →

Research coverageCounts describe available public research, not product quality.View details

Vendor statements: 17 records
Source-checked records: 10
Evaluation requirements: 17 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.

FoundedFounding year was not stated on the reviewed company-controlled pages

HeadquartersUnited States; exact headquarters was not stated on the reviewed company-controlled pages

OwnershipPrivate independent company

EmployeesNot yet sourced

Capital and scaleIndependent company

Runlayer

Known funding: $42M
Operating scale: Runlayer says its catalog covers more than 18,000 MCPs and cites enterprise deployments at Gusto, Jane, and Homebase
Backing context: $30M financing announced in June 2026 from Felicis and Khosla Ventures; prior backing includes SVCI

Named investors

Felicis · Khosla Ventures · SVCI

Founders and leadership3 people listed

Andy Berman
Co-Founder
Current role listed
Tal Peretz
Co-Founder
Current role listed
Vitor Balocco
Co-Founder
Current role listed

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: Founding year was not stated on the reviewed company-controlled pages
Workforce scale: Not yet sourced
Hiring activity: Not displayed

Open research questions

Current employee range is not yet supported by a public source.

Company sources and research limits3 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 sourceRunlayer homepageRunlayer's homepage describes its AI control plane, customer examples, catalog scale, and $30M financing announcement.Company sourceAbout RunlayerRunlayer identifies founders Andy Berman, Tal Peretz, and Vitor Balocco and their prior AI and MCP experience.Company information sourceRunlayer Series A announcementSupports 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.

AI gateway and tool-connection controlsCore product focus Action-taking agent safeguardsCore product focus Agent identity and permissionsCore product focus Employee AI access and usage controlsRelated coverage Coding-agent and developer workstation securityRelated coverage AI usage and cost controlsRelated coverage

Buyer context

Treat Runlayer as an Model Context Protocol (MCP) and agent enablement control plane with security, identity, policy, audit, and cost controls.
Its public product evidence extends beyond a basic proxy through a catalog, shadow discovery, reusable capabilities, governed agents, and centralized observability.

Related frameworks

Where public vendor statements relate to framework requirements

11 related frameworks · expand when needed

These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →

Current referenceCSA AI Controls Matrix

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: 13
References: 31

Requirements with public support: 10
Related requirements: 13
References: 25

Requirements with public support: 9
Related requirements: 11
References: 58

Requirements with public support: 9
Related requirements: 11
References: 27

Requirements with public support: 3
Related requirements: 3
References: 3

Review related requirements →

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.

01
Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
02
Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
03
Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
04
Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
05
Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
06
Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
07
Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
08
Sensitive-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

Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.

Controls for unapproved AI use

Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Detect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

Sensitive prompt, response, or file test data is detected and classified during an AI interaction.

Browser and business-application controls

Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.

Strong public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP GenAI Security Solutions Landscape

A test agent run captures plan, steps, tool calls, outcome, and timestamps.

Agent-to-agent communication security

Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.

No supporting claim found

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

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.

Limited public support

NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 CIS Critical Security Controls ISO/IEC 42001 OWASP 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

Visibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.

Limited public support

Commercial Metadata NIST AI RMF Playbook NIST Cybersecurity Framework 2.0 ISO/IEC 42001 OWASP 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

Publicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.

Research incomplete

Commercial Metadata CSA AI Controls Matrix

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 17 source records. Open additional records only when needed.

Open all vendor evidence →

Unapproved AI use discoverySource checkedStrong public support for this requirement

Runlayer claims discovery of unmanaged agents, MCPs, skills, plugins, and client configurations with visibility into agent sessions.

Surface Shadow AI from unmanaged agents, MCPs, skills, plugins, and client configs

AI-feature discovery in business applicationsNo supporting claim found

Runlayer materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.

No quoted source text is recorded for this claim.

Approved AI usage monitoringSource checkedStrong public support for this requirement

Runlayer claims centralized monitoring of AI usage, adoption, agent activity, and team-level value.

Monitor AI usage, cost, adoption, and agent activity in one place

Controls for unapproved AI useSource checkedStrong public support for this requirement

Runlayer claims policy over what users and agents may access under identity, budget, OAuth grant, and runtime conditions.

Set what users and agents can access, under which identity, budget, OAuth grant, and runtime conditions.

Sensitive-data protection for generative AISource checkedLimited public support for this requirement

Runlayer claims pre-action runtime scanning of tool calls, outputs, intent, and sensitive data.

scan tool calls, outputs, intent, and sensitive data before risky actions reach company systems

Browser and business-application controlsNo supporting claim found

Runlayer materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.

No quoted source text is recorded for this claim.

Show 11 additional evidence records

Generative AI application securitySource checkedLimited public support for this requirement

Runlayer claims runtime security inspection before agent actions reach enterprise systems.

Secure runtime execution

AI governance, risk, and complianceNo supporting claim found

Runlayer materials reviewed did not provide a public claim for enterprise AI governance, risk, approval, and compliance workflows.

No quoted source text is recorded for this claim.

AI assurance and adversarial testingNo supporting claim found

Runlayer materials reviewed did not provide a public claim for adversarial testing or release assurance for AI systems.

No quoted source text is recorded for this claim.

AI model and supply-chain securityNo supporting claim found

Runlayer materials reviewed did not provide a public claim for model and AI component supply-chain inspection.

No quoted source text is recorded for this claim.

AI gateway, tool-connection, and runtime controlsSource checkedStrong public support for this requirement

Runlayer claims an approved Model Context Protocol (MCP) catalog and governed Model Context Protocol (MCP) gateway across major AI clients.

serve them through a governed MCP gateway across every major AI client

Action-taking agent monitoringSource checkedStrong public support for this requirement

Runlayer claims visibility, policy, and audit across agent sessions, MCPs, skills, plugins, memory, triggers, and scoped permissions.

full visibility into every agent session

Agent-to-agent communication securityNo supporting claim found

Runlayer materials reviewed did not provide a public claim for trust or policy enforcement between agents.

No quoted source text is recorded for this claim.

Non-human identity and service-account securityNo supporting claim found

Runlayer materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.

No quoted source text is recorded for this claim.

AI agent identity and permissionsSource checkedLimited public support for this requirement

Runlayer claims identity-aware access policy and scoped permissions for users and agents.

under which identity, budget, OAuth grant, and runtime conditions

AI coding-agent and workstation securitySource checkedLimited public support for this requirement

Runlayer claims centralized controls for employees using Claude, Cursor, ChatGPT, Codex, internal agents, Model Context Protocol (MCP) servers, and existing AI clients.

employees are adopting Claude, Cursor, ChatGPT, Codex, and internal agents

AI cost and usage controlsSource checkedLimited public support for this requirement

Runlayer claims centralized AI cost monitoring, spend attribution to teams and workflows, and budget-conditioned access for users and agents.

Monitor AI usage, cost, adoption, and agent activity in one place, then tie spend back to the teams and workflows getting real value.

Runlayer

Growth stage?Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.

Private independent company

Runlayer

Where public vendor statements relate to framework requirements

Vendor statements and quoted evidence

Runlayer claims discovery of unmanaged agents, MCPs, skills, plugins, and client configurations with visibility into agent sessions.

Runlayer materials reviewed did not provide a public claim for embedded AI discovery across the business-application environment.

Runlayer claims centralized monitoring of AI usage, adoption, agent activity, and team-level value.

Runlayer claims policy over what users and agents may access under identity, budget, OAuth grant, and runtime conditions.

Runlayer claims pre-action runtime scanning of tool calls, outputs, intent, and sensitive data.

Runlayer materials reviewed did not provide a public claim for browser or software as a service (SaaS)-session controls for employee AI use.

Runlayer claims runtime security inspection before agent actions reach enterprise systems.

Runlayer materials reviewed did not provide a public claim for enterprise AI governance, risk, approval, and compliance workflows.

Runlayer materials reviewed did not provide a public claim for adversarial testing or release assurance for AI systems.

Runlayer materials reviewed did not provide a public claim for model and AI component supply-chain inspection.

Runlayer claims an approved Model Context Protocol (MCP) catalog and governed Model Context Protocol (MCP) gateway across major AI clients.

Runlayer claims visibility, policy, and audit across agent sessions, MCPs, skills, plugins, memory, triggers, and scoped permissions.

Runlayer materials reviewed did not provide a public claim for trust or policy enforcement between agents.

Runlayer materials reviewed did not provide a public claim for non-human identity, service-account, secret, or workload credential lifecycle controls.

Runlayer claims identity-aware access policy and scoped permissions for users and agents.

Runlayer claims centralized controls for employees using Claude, Cursor, ChatGPT, Codex, internal agents, Model Context Protocol (MCP) servers, and existing AI clients.

Runlayer claims centralized AI cost monitoring, spend attribution to teams and workflows, and budget-conditioned access for users and agents.

Growth stage
?