AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Calls to models, tools, or connections are routed through a security gateway, including Model Context Protocol (MCP) connections.Action-taking agent safeguardsAdditional if: AI takes multi-step actions · The employee-facing AI performs multiple steps or takes actions rather than only generating content.Agent identity and permissionsAdditional if: AI acts with delegated user access · An employee-facing AI can take actions through tools, connections, business applications, or a user's signed-in session.Employee AI access and usage controlsDirectly addresses · Discover employee AI use and control access to approved and unapproved AI services.
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.
Company scale
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.
?
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.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
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
Series A · $30M · 2026-06-24
Named investors
Felicis · Khosla Ventures · SVCI
Founders and leadership3 people listed
- Andy BermanCurrent role listed
Co-Founder
- Tal PeretzCurrent role listed
Co-Founder
- Vitor BaloccoCurrent role listed
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
- Founding year was not stated on the reviewed company-controlled pages
- 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.
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 focusAction-taking agent safeguardsCore product focusAgent identity and permissionsCore product focusEmployee AI access and usage controlsRelated coverageCoding-agent and developer workstation securityRelated coverageAI 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
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 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
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
Strong 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
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
Limited 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
No supporting claim foundInventory 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
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
No supporting claim foundAuthorize, 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
No supporting claim foundInventory, 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
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
Limited public supportVisibility, 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
Research incompletePublicly 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 17 source records. Open additional records only when needed.
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
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.
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
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.
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
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
Runlayer claims runtime security inspection before agent actions reach enterprise systems.
Secure runtime execution
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.
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.
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.
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
Runlayer claims visibility, policy, and audit across agent sessions, MCPs, skills, plugins, memory, triggers, and scoped permissions.
full visibility into every agent session
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.
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.
Runlayer claims identity-aware access policy and scoped permissions for users and agents.
under which identity, budget, OAuth grant, and runtime conditions
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
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.