Vendor research

Keycard

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

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.

$38M known funding
Founded 2025
Employee scale not sourced
Private-company revenue and profitability not sourced

Company context

Private independent company

Early-stage agent identity and authorization company with published SDK, CLI, Model Context Protocol (MCP), application programming interface (API), SSO, and audit documentation

Vendor website ↗Add to comparison →

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

Vendor statements: 16 records
Source-checked records: 7
Evaluation requirements: 16 in this research model
Unresolved requirements: 11

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.

FoundedPublic launch in 2025

HeadquartersRemote-first company; San Francisco was used for its launch announcement

OwnershipPrivate independent company

EmployeesNot yet sourced

Capital and scaleIndependent company

Keycard

Known funding: $38M
Operating scale: Early-stage agent identity and authorization company with published SDK, CLI, MCP, API, SSO, and audit documentation
Backing context: $38M announced from Andreessen Horowitz, boldstart ventures, and Acrew Capital

Named investors

Acrew Capital · Andreessen Horowitz · boldstart ventures

Founders and leadership3 people listed

Ian Livingstone
Co-Founder & CEO
Current role listed
Matthew Creager
Co-Founder
Current role listed
Jared Hanson
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: Public launch in 2025
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 limits2 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 sourceKeycard homepageKeycard describes composite agent identity, runtime policy, task-scoped credentials, token exchange, coding-agent controls, and centralized telemetry.Company sourceKeycard launch announcementKeycard's company-issued launch announcement states $38M in funding and describes its remote-first operating model.

Solution areas

These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.

Agent identity and permissionsCore product focus Machine and workload identityCore product focus Action-taking agent safeguardsRelated coverage AI gateway and tool-connection controlsRelated coverage Coding-agent and developer workstation securityRelated coverage

Buyer context

Treat Keycard as an agent access and authorization control plane, not a content-safety, data loss prevention (DLP) inspection, or red-teaming product.
Public evidence supports user, device, agent, and task identity; policy at credential issuance; task-scoped credentials; Model Context Protocol (MCP) and application programming interface (API) authorization; coding-agent governance; and attributed audit.

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 referenceCIS Critical Security Controls

Requirements with public support: 7
Related requirements: 11
References: 58

Requirements with public support: 7
Related requirements: 14
References: 71

Requirements with public support: 7
Related requirements: 12
References: 35

Requirements with public support: 7
Related requirements: 12
References: 71

Requirements with public support: 7
Related requirements: 13
References: 39

Requirements with public support: 7
Related requirements: 12
References: 39

Requirements with public support: 7
Related requirements: 12
References: 31

Requirements with public support: 7
Related requirements: 11
References: 27

Requirements with public support: 7
Related requirements: 13
References: 31

Requirements with public support: 7
Related requirements: 13
References: 25

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
Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
02
Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
03
AI gateway, tool-connection, and runtime controls
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
04
AI gateway, tool-connection, and runtime controls
A test policy allows, blocks, transforms, redirects, or rate-limits the request with an explicit reason.
05
Action-taking agent monitoring
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
06
Action-taking agent monitoring
Agent memory, delegated task, autonomy, or runtime decision detail is visible in a timeline or log.
07
Agent-to-agent communication security
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
08
Agent-to-agent communication security
An allowed or denied access attempt produces an audit event with agent or tool identity.

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.

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 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.

Limited 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.

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 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.

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

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.

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 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.

Limited public support

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.

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 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.

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 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.

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 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.

Research incomplete

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 16 source records. Open additional records only when needed.

Open all vendor evidence →

Unapproved AI use discoveryNo supporting claim found

Keycard materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.

AI-feature discovery in business applicationsNo supporting claim found

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

Approved AI usage monitoringSource checkedLimited public support for this requirement

Keycard claims visibility and attribution for agent actions, tool calls, policy decisions, and data access.

Controls for unapproved AI useNo supporting claim found

Keycard materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.

Sensitive-data protection for generative AINo supporting claim found

Keycard materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.

Browser and business-application controlsNo supporting claim found

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

Show 10 additional evidence records

Generative AI application securityNo supporting claim found

Keycard materials reviewed did not provide a public claim for security controls for custom generative AI applications at runtime.

AI governance, risk, and complianceNo supporting claim found

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

AI assurance and adversarial testingNo supporting claim found

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

AI model and supply-chain securityNo supporting claim found

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

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

Keycard claims authentication and task-scoped access controls for Model Context Protocol (MCP), CLI, application programming interface (API), and downstream services.

Action-taking agent monitoringSource checkedStrong public support for this requirement

Keycard claims a real-time event stream of agent actions, tool calls, policy decisions, and attributed audit events.

Agent-to-agent communication securitySource checkedLimited public support for this requirement

Keycard claims authentication for agent-to-agent delegation while preserving user identity.

Non-human identity and service-account securitySource checkedStrong public support for this requirement

Keycard claims workload attestation and short-lived credentials that avoid long-lived secrets and over-permissioned service accounts.

AI agent identity and permissionsSource checkedStrong public support for this requirement

Keycard claims composite user, device, agent, and task identity with runtime policy and task-scoped credentials.

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

Keycard claims governance of coding-agent shell, script, Model Context Protocol (MCP), environment, credential, and audit paths.

Keycard

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

Keycard

Where public vendor statements relate to framework requirements

Vendor statements and quoted evidence

Keycard materials reviewed did not provide a public claim for enterprise-wide discovery of unapproved AI use.

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

Keycard claims visibility and attribution for agent actions, tool calls, policy decisions, and data access.

Keycard materials reviewed did not provide a public claim for policy enforcement over unapproved AI use.

Keycard materials reviewed did not provide a public claim for inspection or enforcement over sensitive data moving through AI.

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

Keycard materials reviewed did not provide a public claim for security controls for custom generative AI applications at runtime.

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

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

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

Keycard claims authentication and task-scoped access controls for Model Context Protocol (MCP), CLI, application programming interface (API), and downstream services.

Keycard claims a real-time event stream of agent actions, tool calls, policy decisions, and attributed audit events.

Keycard claims authentication for agent-to-agent delegation while preserving user identity.

Keycard claims workload attestation and short-lived credentials that avoid long-lived secrets and over-permissioned service accounts.

Keycard claims composite user, device, agent, and task identity with runtime policy and task-scoped credentials.

Keycard claims governance of coding-agent shell, script, Model Context Protocol (MCP), environment, credential, and audit paths.

Growth stage
?