No solution approach in the current research connects this vendor to this use case.
Vendor research
Enkrypt AI
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
No related approach foundBack 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.
- $2.4M known funding
- 11-50 employees
- Founded 2022
- Private-company revenue and profitability not sourced
Company context
Private independent company; reviewed Enkrypt AI-controlled sources do not identify an acquirer or parent company
11-50 employees in the reviewed company snapshot, with published enterprise work involving AI21 Labs and NetApp
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 14
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 5
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.
Founded2022
HeadquartersBrighton, Massachusetts
OwnershipPrivate independent company; reviewed Enkrypt AI-controlled sources do not identify an acquirer or parent company
Employees11-50
Capital and scaleIndependent company
Enkrypt AI
- Known funding
- $2.4M
- Operating scale
- 11-50 employees in the reviewed company snapshot, with published enterprise work involving AI21 Labs and NetApp
- Backing context
- $2.35M seed round announced in February 2024; Enkrypt AI also lists Microsoft for Startups, Kubera VC, Berkeley SkyDeck, ARKA, Boldcap, Intel Liftoff, and NVIDIA among its backers and programs
Seed · $2.4M · 2024-02-27
Named investors
Boldcap · Berkeley SkyDeck · ARKA · Kubera
Founders and leadership2 people listed
- Sahil AgarwalCurrent role listed
Co-Founder & CEO
- Prashanth HCurrent 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
- 2022
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits6 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 sourceEnkrypt AI pricing pageEnkrypt AI publishes productized guardrails, red teaming, MCP scanning and gateway, compliance, deployment, and credit-based pricing tiers.Company sourceEnkrypt AI about pageEnkrypt AI identifies its founders, investors, Boston-area address, and policy-driven approach to AI security.Company sourceEnkrypt AI agent security announcementEnkrypt AI's company announcement states that the company was founded in 2022.Company sourceEnkrypt AI seed announcementEnkrypt AI announced a $2.35M seed round in February 2024.Company sourceEnkrypt AI industry case studiesEnkrypt AI publishes work with AI21 Labs and NetApp, providing a concrete adoption signal without establishing broad enterprise scale or financial durability.Company information sourceEnkrypt AI LinkedIn company profileSupports 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 testing and adversarial assuranceCore product focusAI application runtime protectionCore product focusAI gateway and tool-connection controlsCore product focusAI governance, risk, and complianceRelated coverageAI asset and configuration securityRelated coverageAction-taking agent safeguardsRelated coverageAgent identity and permissionsRelated coverageCoding-agent and developer workstation securityRelated coverage
Buyer context
- Classify Enkrypt AI as an emerging up-and-comer for enterprise procurement: $2.35M in known seed funding and an 11-50 employee range do not establish the operating durability expected for a default large-enterprise dependency.
- A large enterprise should consider Enkrypt AI only for a differentiated requirement or controlled pilot after validating runway or profitability, reference customers, support capacity, contractual SLAs, insurance, and an exit plan.
- Treat Enkrypt AI as an integrated AI assurance, guardrails, governance, and Model Context Protocol (MCP) security platform rather than a workforce browser or SSE control.
- Public evidence supports multimodal agent red teaming, runtime guardrails, policy-to-control mapping, Model Context Protocol (MCP) inventory and scanning, an inline Model Context Protocol (MCP) gateway, audit evidence, identity-aware policies, and CI/CD release gates.
- Public pages reviewed did not establish broad software as a service (SaaS) embedded-AI discovery, generic non-human identity (NHI) credential lifecycle, or AI spend attribution and budget 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
- 13
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 12
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 12
- Related requirements
- 13
- References
- 25
- 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.
- 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
Limited 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
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
No supporting claim foundBlock, 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
Strong 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
Strong public supportTest 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
Limited public supportDiscover, 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
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
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.
Enkrypt AI claims recurring Model Context Protocol (MCP) scanning to identify drift and shadow Model Context Protocol (MCP) infrastructure.
Continuously to catch drift and shadow MCP
Enkrypt AI materials reviewed did not provide a public claim for tenant-level discovery of embedded AI features across enterprise software as a service (SaaS) applications.
No quoted source text is recorded for this claim.
Enkrypt AI claims an inventory of Model Context Protocol (MCP) servers, tools, capabilities, environments, and owners.
Servers, tools, capabilities, environments, owners
Enkrypt AI materials reviewed did not provide a public claim for workforce allow, coach, restrict, or block policy over unapproved AI application use.
No quoted source text is recorded for this claim.
Enkrypt AI claims runtime controls against sensitive-data exfiltration through tools, retrieval, and model outputs.
Sensitive data exfiltration via tools, retrieval, or outputs
Enkrypt AI materials reviewed did not provide a public claim for browser session controls over upload, download, copy, paste, sharing, or form submission.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Enkrypt AI claims a runtime layer that approves, modifies, or blocks risky behavior across agents, tools, retrieval-augmented generation (RAG), and Model Context Protocol (MCP).
approves, modifies, or blocks risky behavior across agents, tools, RAG, and MCP
Enkrypt AI claims policy-to-control mapping with owners, scope, approvals, version history, exceptions, and audit exports.
Approvals, change diffs, rollback, exceptions/waivers
Enkrypt AI claims continuous multimodal red teaming across agents, tools, retrieval-augmented generation (RAG), and Model Context Protocol (MCP) with reproducible findings and CI regression suites.
finds real failure modes across text, audio, and vision—including agents, tools, RAG, and MCP
Enkrypt AI claims Model Context Protocol (MCP) scanner coverage for untrusted servers, tools, and poisoned tool catalogs as agentic supply-chain risk.
untrusted MCP servers/tools and poisoned tool catalogs are treated as supply-chain risk
Enkrypt AI claims an inline Model Context Protocol (MCP) gateway that can approve, modify, require approval for, or block tool calls and record policy decisions.
sits inline between agents and MCP servers to approve, modify, or block tool calls
Enkrypt AI claims action traces containing tool, server, actor, environment, timestamps, and outcomes.
Tool/server, actor, environment, time-stamps, outcomes
Enkrypt AI claims red-team coverage for insecure inter-agent communication and runtime controls across chained retrieval-augmented generation (RAG), tool, and action paths.
Insecure inter-agent communication
Enkrypt AI materials reviewed did not provide a public claim for generic workload identities, service accounts, application programming interface (API)-key discovery, secret rotation, or machine-credential lifecycle management.
No quoted source text is recorded for this claim.
Enkrypt AI claims role- and tenant-aware rules using SSO, identity and access management (IAM), and identity claims to constrain tools, data sources, and actions.
Role/tenant-based rules
Enkrypt AI documents installing its Model Context Protocol (MCP) gateway for Cursor so connected tool actions can pass through policy enforcement.
secure-mcp-gateway install --client cursor
Enkrypt AI materials reviewed did not provide a public claim for AI spend attribution, budgets, chargeback, rate limits, or token-cost anomaly detection.
No quoted source text is recorded for this claim.
Enkrypt AI publishes credit-based monthly plans, a free evaluation tier, and custom enterprise pricing.
$149/month
Enkrypt AI claims application programming interface (API)-first integration with agent orchestrators, identity and access management (IAM), SIEM, ticketing, CI/CD, and multiple deployment models.
SIEM, ticketing & identity integrations