AI gateway and tool-connection controlsAdditional if: Model or tool traffic uses a security gateway · Model, tool, connector, API, or Model Context Protocol (MCP) traffic is routed through a security gateway.Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.Agent identity and permissionsAdditional if: Agents act with delegated authority · An agent acts with delegated authority and needs an accountable owner, task-level permissions, access reviews, or rapid revocation.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.AI data protectionAdditional if: Sensitive content enters AI flows · Sensitive content must be inspected or blocked in prompts, responses, files, retrieved information, or tool calls.
Vendor research
Speakeasy AI Control Plane
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.
- $26M known funding
- Employee scale not sourced
- Private-company revenue and profitability not sourced
Company context
Private, venture-backed Speakeasy Development, Inc.; no parent or acquisition announced on reviewed sources
Speakeasy positions its current AI Control Plane for enterprise-wide governance of Model Context Protocol (MCP) servers, skills, assistants, coding agents, and internal tools
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 12
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 7
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.
FoundedPublicly launched in 2023; incorporation year not stated on reviewed Speakeasy-controlled pages
HeadquartersHeadquarters not explicitly designated on reviewed Speakeasy-controlled pages; the company reports operations across San Francisco and London
OwnershipPrivate, venture-backed Speakeasy Development, Inc.; no parent or acquisition announced on reviewed sources
EmployeesNot yet sourced
Capital and scalePlatform provider
Speakeasy Development, Inc.
- Known funding
- $26M
- Operating scale
- Speakeasy positions its current AI Control Plane for enterprise-wide governance of MCP servers, skills, assistants, coding agents, and internal tools
- Backing context
- $26M in disclosed funding across $11M of combined pre-seed and seed financing and a $15M Series A led by FPV Ventures
Series A · $15M
Named investors
FPV Ventures · GV · Quiet Capital
Founders and leadershipMore research needed
Founder names and current roles are not yet supported by a public source in this research.
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
- Publicly launched in 2023; incorporation year not stated on reviewed Speakeasy-controlled pages
- Workforce scale
- Not yet sourced
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- Founder names and current-company status are not yet supported by a public source.
- Current employee range is not yet supported by a public source.
Company sources and research limits4 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 sourceSpeakeasy AI Control Plane homepageSpeakeasy describes an enterprise control plane for centrally managing MCPs, skills, assistants, permissions, threat detection, policy, and observability.Company sourceSpeakeasy MCP Gateway product pageSpeakeasy describes SSO, scoped permissions, runtime guardrails, DLP, prompt-injection detection, audit logs, traces, anomaly detection, and usage attribution for MCP traffic.Company sourceSpeakeasy Series A announcementSpeakeasy announced a $15M Series A led by FPV Ventures with continued support from GV and Quiet Capital and described operations across San Francisco and London.Company sourceSpeakeasy launch announcementSpeakeasy publicly launched in 2023 and disclosed $11M in combined pre-seed and seed funding from company-named investors.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Speakeasy as a target based on its current AI Control Plane and Model Context Protocol (MCP) Gateway product claims; its prior role as visual inspiration is separate from vendor qualification.
- Public evidence supports identity-aware Model Context Protocol (MCP) access, tool-level permissions, real-time prompt and agent-action enforcement, PII and secret controls, prompt-injection detection, shadow-tool governance, searchable audit, traces, and usage attribution.
- Do not infer broad application programming interface (API) discovery or web application and application programming interface protection (WAAP), data security posture management (DSPM), model assurance, enterprise GRC, or complete large language model (LLM)-gateway routing coverage from the Model Context Protocol (MCP) and agent control-plane evidence.
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
- 11
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 11
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 11
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 11
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 11
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 11
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 11
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 11
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 10
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 10
- 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
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
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
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
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
Strong 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
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.
Speakeasy claims identification of AI tools and integrations operating outside an approved environment.
Identify AI tools and integrations operating outside your approved environment.
Speakeasy AI Control Plane materials reviewed did not provide a public claim for AI-feature discovery across the enterprise business-application environment.
No quoted source text is recorded for this claim.
Speakeasy claims centralized provisioning and visibility for approved employee AI tools and agent access.
Every employee gets the right access from day one, with full visibility and control.
Speakeasy claims blocking or sanctioning of unapproved AI-tool usage.
Block or sanction unapproved usage.
Speakeasy claims real-time blocking, redaction, or logging of sensitive data in prompts, responses, and agent actions.
Sensitive data is blocked, redacted, or logged
Speakeasy AI Control Plane materials reviewed did not provide a public claim for session-level browser or software as a service (SaaS) controls for employee AI use.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Speakeasy claims real-time inspection and enforcement across prompts, responses, and agent actions.
Every prompt, response, and agent action is inspected and enforced in real time.
Speakeasy AI Control Plane materials reviewed did not provide a public claim for AI inventory, risk, approval, exception, and compliance workflows.
No quoted source text is recorded for this claim.
Speakeasy AI Control Plane materials reviewed did not provide a public claim for AI-specific adversarial testing and release assurance.
No quoted source text is recorded for this claim.
Speakeasy AI Control Plane materials reviewed did not provide a public claim for model and AI-artifact supply-chain inspection.
No quoted source text is recorded for this claim.
Speakeasy claims SSO, role-based access, and runtime guardrails at its Model Context Protocol (MCP) gateway.
SSO, RBAC, and runtime guardrails at the gateway
Speakeasy claims real-time logs and traces for Model Context Protocol (MCP) requests and agent tool calls.
Real-time logs and traces for every MCP request
Speakeasy claims distributed traces across agents, Model Context Protocol (MCP) servers, and downstream application programming interfaces (APIs).
Follow a tool call across agents, MCP servers, and downstream APIs
Speakeasy AI Control Plane materials reviewed did not provide a public claim for non-human identity and machine-credential lifecycle controls.
No quoted source text is recorded for this claim.
Speakeasy claims SSO and role-based permissions down to individual Model Context Protocol (MCP) tools.
Permission down to the server, toolset, or individual tool.
Speakeasy claims governed Model Context Protocol (MCP) access for coding assistants including Cursor, Copilot, and internal agents.
Point Claude, Cursor, ChatGPT, Copilot, and your internal agents at one URL.
Speakeasy claims AI spend and adoption attribution by team, client, and tool.
Attribute spend and adoption per team, client, and tool.
Speakeasy AI Control Plane materials reviewed did not provide a public claim for a public per-user, per-app, usage-based, or enterprise-platform commercial model.
No quoted source text is recorded for this claim.
Speakeasy positions its control plane between approved AI tools and enterprise software as a service (SaaS), application programming interfaces (APIs), and internal systems.
Connect SaaS, APIs, and internal systems through a single governed platform.