Machine and workload identityAdditional if: Apps or agents use machine credentials · The application or agent uses service accounts, API keys, machine identities, secrets, scheduled tasks, or other machine credentials.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.Action-taking agent safeguardsDirectly addresses · Observe and govern agent plans, memory, tool use, delegated tasks, and actions while the agent runs.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.AI application runtime protectionDirectly addresses · Protect custom AI applications, information-retrieval systems, model calls, prompts, and outputs while they run.
Vendor research
Aembit
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
Emerging?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.This is a company-scale signal, not a product-quality rating.
?
EmergingAn early-stage provider with less than $25M in known funding, or 50 or fewer employees without at least $50M in known funding.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.
- $26.5M known funding
- <50 employees
- Founded 2021
Company context
Private independent company; no acquisition or parent-company claim found on reviewed Aembit-controlled pages
Aembit positions itself as Workload identity and access management (IAM) and an identity and access management (IAM) control plane for agentic AI and machine-to-machine access across clouds, software as a service (SaaS), and on-premise environments
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.
FoundedFounding year not stated on reviewed Aembit-controlled pages
HeadquartersHeadquarters not stated on reviewed Aembit-controlled pages
OwnershipPrivate independent company; no acquisition or parent-company claim found on reviewed Aembit-controlled pages
Employees<50
Capital and scaleIndependent company
Aembit
- Known funding
- $26.5M
- Operating scale
- Aembit positions itself as Workload IAM and an IAM control plane for agentic AI and machine-to-machine access across clouds, SaaS, and on-premise environments
- Backing context
- Aembit-controlled about page lists investors and advisors including Sanjay Beri, Rajiv Gupta, Jim Alkove, Niels Provos, and Mario Duarte; funding amount not stated on reviewed pages
Aembit-controlled about page lists investors and advisors including Sanjay Beri, Rajiv Gupta, Jim Alkove, Niels Provos, and Mario Duarte; funding amount not stated on reviewed pages
Founders and leadership2 people listed
- David GoldschlagCurrent role listed
Co-Founder & CEO
- Kevin SappCurrent 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
- Founding year not stated on reviewed Aembit-controlled pages
- Workforce scale
- <50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Open research questions
- A current hiring source is not available, so the count is not shown.
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 sourceAembit llms.txtAembit says it enforces policy-based, secretless, identity-driven access between workloads, AI agents, and sensitive resources.Company sourceAembit homepageAembit describes IAM for Agentic AI, agent audit/control, MCP, A2A, short-lived credentials, and no stored secrets.Company sourceAembit about pageAembit's about page lists founders, leadership, investors, and advisors.Company information sourceStored company websiteSupports 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
- Treat Aembit as an non-human identity (NHI), workload identity and access management (IAM), Model Context Protocol (MCP) authorization, and AI-agent access-control specialist rather than an employee browser/software as a service (SaaS) shadow-AI control.
- Public evidence supports secretless identity-driven access, short-lived credentials, agent policy enforcement, agent-to-agent (A2A)/Model Context Protocol (MCP) support, agent audit logs, and per-agent pricing.
- Public pages reviewed did not expose AI app discovery, embedded software as a service (SaaS) AI inventory, employee browser-session controls, or AI spend governance.
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
- 10
- Related requirements
- 11
- References
- 58
- 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
- 11
- References
- 27
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 10
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 4
- 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.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 04Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 05Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 06Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 07Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 08Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover 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
Limited 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
Limited 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
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
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
Strong 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
No supporting claim foundDiscover 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
Strong public supportPublicly 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.
Aembit claims centralized agent access policy, cryptographically verifiable identity, per-request policy decisions, and audit logs tying agent identity, user identity, target server, credential, and resource access together.
Every MCP request is logged with agent identity, user identity, target server, and policy decision.
Aembit identity and access management (IAM) for Agentic AI materials reviewed did not provide a public product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
Aembit identity and access management (IAM) for Agentic AI materials reviewed did not establish model artifact scanning, provenance, signing, dependency or Model Context Protocol (MCP) component analysis, tamper detection, or model-registry release controls.
No quoted source text is recorded for this claim.
Aembit claims an Model Context Protocol (MCP) Identity Gateway that validates workload identity, enforces per-request policy, approves or denies access, exchanges credentials, and logs agent-to-resource communications.
The gateway authenticates the agent, enforces policy, and performs token exchange.
Aembit claims cryptographically verified blended agent and user identity, OAuth 2.1 authorization, secure token exchange, ephemeral just-in-time credentials, least privilege, immediate revocation, and full access attribution.
Aembit IAM for Agentic AI assigns each agent a cryptographically verified identity, issues ephemeral credentials, enforces policy at runtime.
Aembit identity and access management (IAM) for Agentic AI materials reviewed did not establish governance of coding-agent commands, developer-workstation files or networks, integrated development environment (IDE) extensions, skills, hooks, secrets, or package actions.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Aembit materials reviewed did not provide a public claim for employee AI app discovery, unapproved AI usage discovery, prompt/user activity inventory, or AI domain detection.
No quoted source text is recorded for this claim.
Aembit materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory, embedded software as a service (SaaS) AI features, or third-party AI service monitoring.
No quoted source text is recorded for this claim.
Aembit claims policy, context, and audit for all agent interactions based on unique identities.
Aembit enforces access to your sensitive data and accelerates your AI use with confidence. Apply policy, context, and audit to all agent interactions based on their unique identities.
Aembit claims the ability to stop AI access and audit access in real time based on an agent's unique identity.
Stop AI access with a click of a button. Audit access in real-time — based on the agent’s unique identity, even if it’s operating on behalf of a user.
Aembit claims policy-based, secretless, identity-driven access between AI agents and sensitive resources across clouds, software as a service (SaaS), and on-premise environments.
Aembit enforces policy-based, secretless, identity-driven access between workloads, AI agents, and the sensitive resources they need — across clouds, SaaS, and on-premise environments.
Aembit materials reviewed did not provide a public claim for browser extension enforcement, enterprise browser controls, software as a service (SaaS)-session controls, or user activity policy.
No quoted source text is recorded for this claim.
Aembit claims policy-based, identity-driven controls for AI agents accessing large language models (LLMs), application programming interfaces (APIs), and tools.
Streamline and secure access from AI agents to leading LLMs like OpenAI, Claude, and Gemini, APIs, and tools with policy-based, identity-driven controls.
Aembit claims full attribution for every agent action with audit logs that distinguish human-initiated and agent-initiated access.
Full attribution for every agent action – audit logs definitively distinguish human-initiated access from agent-initiated access
Aembit claims Model Context Protocol (MCP) Authorization enforces policy-based controls over which agents can reach which tools and data.
Aembit secures access between AI agents and MCP (Model Context Protocol) servers, enforcing policy-based controls over which agents can reach which tools and data.
Aembit claims non-human identity (NHI) coverage for AI agents, application programming interfaces (APIs), microservices, CI/CD pipelines, scripts, and service accounts with dynamically issued short-lived credentials.
non-human identities (NHIs) — including AI agents, APIs, microservices, CI/CD pipelines, scripts, and service accounts
Aembit materials reviewed did not provide a public claim for AI spend attribution, model cost routing, budget enforcement, rate limits, runaway token controls, or AI return on investment (ROI) reporting.
No quoted source text is recorded for this claim.
Aembit publishes an Agentic AI Teams tier priced at $20 per agent per month.
Teams $20 /agent/mo Perfect for an individual team running a set of agents in production.
Aembit claims policy-based, identity-driven controls for AI agents accessing OpenAI, Claude, Gemini, application programming interfaces (APIs), and tools.
Streamline and secure access from AI agents to leading LLMs like OpenAI, Claude, and Gemini, APIs, and tools with policy-based, identity-driven controls.