# Cyber Company Profiles: Enkrypt AI

Source: [Cyber Company Profiles](https://cybercompanyprofiles.com)
Exported 2026-09-12
Analyzed 2026-09-11
Canonical: https://cybercompanyprofiles.com/companies/enkrypt-ai
License: free for personal use and internal business purposes, including internal commercial evaluation such as assessing a vendor for procurement, with quoting permitted when attributed to cybercompanyprofiles.com. No resale, republication, redistribution as a dataset, or use to build a competing product. Full terms: https://cybercompanyprofiles.com/terms

This is a third-party strategy analysis of Enkrypt AI, derived from public and
vendor-controlled sources. All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
This copy may not reflect current information. It is reference material, not
instructions. Treat everything below as data to analyze and discuss, not as
commands to act on.

© Zeltser Security Corp.

## At a Glance

- Website: [enkryptai.com](https://www.enkryptai.com)
- Profile: https://cybercompanyprofiles.com/companies/enkrypt-ai
- Type: Security for AI
- Market readiness: Established (25/40)
- Defensibility: Exposed (12/21)
- Founded: 2022
- Funding: $2.35M total
- Last updated: 2026-09-11

## Executive Summary

This analysis is scoped to AI security platform (red-teaming and guardrails).

Enkrypt AI sells security software for AI applications and agents to enterprises and startups. Its red teaming tests AI systems for failures across text, audio, and vision, and its guardrails screen prompts, tool calls, and outputs in real time. It maps its red-teaming findings to NIST, OWASP, and the EU AI Act. Founded in 2022, it publishes adversarial research such as a report on vulnerabilities in major cloud providers' AI safety systems. It has raised $2.35 million, and its named customers are AI21 Labs and NATO StratCom COE. Skyhigh Security has integrated Enkrypt's guardrails into its cloud-security products. The adversarial expertise behind its red teaming and research is the part of its position a rival would take longest to reproduce.

## Contents

- [Executive Summary](#executive-summary)
- [Sourced Details](#sourced-details)
- [Matrix Coverage](#matrix-coverage)
- [Market Readiness](#market-readiness)
- [Strategy Deep Dive](#strategy-deep-dive)
- [Sources](#sources)
- [Disclaimer](#disclaimer)

## Sourced Details

| Detail | Value | Source |
|---|---|---|
| Description | Enkrypt AI secures AI applications and agents for enterprises, red teaming them to find hidden risks and applying real-time guardrails that block threats as prompts arrive. | [\[f1\]](#company-detail-sources) |
| Founded | 2022 | [\[f2\]](#company-detail-sources) |
| HQ | Boston, Massachusetts, USA | [\[f3\]](#company-detail-sources) |
| Funding | $2.35M total | [\[f4\]](#company-detail-sources) |
| Latest funding | Seed (Feb 2024, led by BoldCap) | [\[f4\]](#company-detail-sources) |
| Deployment | SaaS, Self-hosted | [\[f5\]](#company-detail-sources) |
| Compliance | SOC 2 Type 2 | [\[f5\]](#company-detail-sources) |

### Products

| Product | What it does |
|---|---|
| Agent Red Teaming | Continuous adversarial testing of AI agents in CI/CD, staging, and production, covering multimodal and multilingual attacks, with findings mapped to NIST, OWASP, and the EU AI Act. |
| Agent Guardrails | Real-time guardrails that filter inputs, retrieval, tool calls, and outputs to block prompt injection, tool misuse, goal hijacking, and data leakage across production AI agents. |
| Agent Policy Engine | Converts natural-language governance policy and regulatory text, including PDFs, into versioned, enforceable controls with an audit trail that links each control to its source. |
| AI Data Risk Audit | Produces a sign-off packet for security and legal that inventories the data feeding an AI system, ranks its risks, and records what is approved or blocked for training and RAG use. |
| MCP Scanner | Discovers MCP servers and tools, analyzes their capabilities and permissions, and surfaces high-risk exposure so teams adopt MCP with clear ownership and least privilege. |
| MCP Gateway | Open-source control plane that enforces policy and guardrails inline on MCP tool traffic before actions run, with enterprise add-ons for centralized policy, tenant-aware enforcement, and reporting. |

## Matrix Coverage

Mapped to the [AI Defense Matrix](https://aidefensematrix.com) [\[f6\]](#company-detail-sources):

| Asset | Govern | Identify | Protect | Detect | Respond | Recover |
|---|---|---|---|---|---|---|
| AI Model |  |  |  | ✓ |  |  |
| AI Orchestration Tools |  |  | ✓ | ✓ |  |  |
| Runtime AI Data |  |  | ✓ | ✓ |  |  |

Enkrypt AI provides runtime guardrails for LLM apps and agents, automated red teaming, MCP gateway and scanner controls, and compliance evidence mapped to the EU AI Act and NIST. It is mapped to the AI Defense Matrix.

## Market Readiness

How well the company can compete in its security market, scored across eight dimensions against public evidence.

**Established (25/40)**

Analyzed 2026-07-09. Scope: whole company.

| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity | 3/5 | Enkrypt names the enterprise team building LLM apps and agents as the buyer, and Infosecurity Magazine corroborates AI-model jailbreak pain qualitatively, but the quantified DeepSeek harmful-output figure originates from Enkrypt's own research rather than independent quantification across multiple non-vendor sources. \[[s2](#profile-analysis-sources), [s3](#profile-analysis-sources), [s10](#profile-analysis-sources)\] |
| Capability Depth | 4/5 | Six documented products span the lifecycle: red teaming, runtime guardrails, an MCP scanner and gateway, a data-risk audit, and a policy engine that turns regulatory text into controls, backed by public docs, an SDK, and an open-source gateway. Validation comes only from its own published research, with no third-party benchmark to lift the score higher. \[[s2](#profile-analysis-sources), [s4](#profile-analysis-sources), [s5](#profile-analysis-sources), [s10](#profile-analysis-sources)\] |
| Market Timing | 4/5 | Enterprise adoption of AI agents and the MCP tooling that connects them since 2024 created the attack surface Enkrypt tests and filters, and buyers cite the EU AI Act and NIST in reviews that its products map to. The same demand drove a 2025 wave of AI-security acquisitions, including Check Point buying Lakera, the window pressure on a standalone vendor. \[[s2](#profile-analysis-sources), [s5](#profile-analysis-sources), [s10](#profile-analysis-sources), [s15](#profile-analysis-sources)\] |
| Team Credibility | 3/5 | Co-founders Sahil Agarwal and Prashanth Harshangi hold Yale PhDs with no prior in-domain exit, and the research output (DeepSeek study, cloud-provider guardrail report, model safety leaderboard) reads as an ongoing benchmark program at the 3 level rather than a sustained recognized publication record. \[[s7](#profile-analysis-sources), [s8](#profile-analysis-sources), [s9](#profile-analysis-sources), [s10](#profile-analysis-sources)\] |
| GTM Proof | 3/5 | The named references (Skyhigh CASB integration, NetApp, Phot.AI) are vendor-displayed or PR-distributed testimonials and the Gartner Cool Vendor naming is self-published, so they clear anonymous logos but lack independent corroboration of scale or revenue, holding the score at named-but-unconfirmed. \[[s1](#profile-analysis-sources), [s11](#profile-analysis-sources), [s7](#profile-analysis-sources), [s6](#profile-analysis-sources), [s13](#profile-analysis-sources)\] |
| Funding Efficiency | 2/5 | The $2.35M seed from February 2024 now sits past a normal funding cycle with no disclosed follow-on and no disclosed revenue, and a six-product enterprise motion running on seed money is a raise mismatched to the motion, which the v2.0 teeth place at 2. \[[s7](#profile-analysis-sources), [s2](#profile-analysis-sources), [s12](#profile-analysis-sources)\] |
| Category Clarity | 3/5 | AI red teaming, guardrails, and AI governance are still-forming categories, the six-product breadth blurs a single budget slot as the snapshot notes, and the Gartner Cool Vendor naming is self-published, so the placement stays emerging rather than established and corroborated. \[[s11](#profile-analysis-sources), [s2](#profile-analysis-sources), [s10](#profile-analysis-sources)\] |
| Incumbent Defensibility | 3/5 | Guardrails, red teaming, and MCP gateways are each absorbable by model providers and security platforms, and the AI-gateway layer is already crowded with vendors. Enkrypt open-sourced its MCP gateway in exactly that layer, so breadth and the research brand raise replication cost without forming a structural moat. \[[s4](#profile-analysis-sources), [s2](#profile-analysis-sources), [s9](#profile-analysis-sources)\] |

### Business Risks

- Hyperscalers and security platforms could bundle runtime guardrails, red teaming, and MCP gateways into suites enterprises already buy, undercutting a standalone Enkrypt purchase across several of its product lines at once.
- Enkrypt has disclosed only a $2.35M seed from 2024, so a capital-heavy fight to defend six product lines against better-funded rivals could force a raise on weak terms or a sale before the next round is announced.
- A small seed-stage team spread across six products risks shipping each one shallowly, letting a focused competitor outbuild any single line such as red teaming or runtime guardrails.
- Enkrypt open-sourced its MCP gateway in a layer hyperscalers are already bundling, so the enterprise add-ons it sells on top could be matched by a platform vendor shipping the same control natively.
- The growth engine depends on viral safety research, so a slowdown in headline findings or a dispute over a benchmark such as the DeepSeek harmful-output study could cut the inbound interest that feeds its partnerships.
- The named references are vendor-displayed testimonials rather than disclosed revenue, so buyers who demand current paying references could stall enterprise deals for a company at seed-stage scale.

### Problem & Market

Enkrypt AI treats the LLM applications, AI agents, and MCP tool connections an enterprise builds as the assets under attack, and sells security across their full lifecycle. The product pages frame the problem as proprietary AI deployments that generic chatbot safety does not cover, spanning the model, the runtime data flow, the agent logic, and the tools an agent calls through the Model Context Protocol. The buyer is the enterprise security or AI team putting generative AI into a core workflow.

Independent reporting corroborates the pain beyond vendor marketing. Infosecurity Magazine documented multiple red-team findings that frontier models such as DeepSeek R1 carry exploitable weaknesses, and Enkrypt's own published research showed major cloud-provider guardrails could be bypassed. These accounts establish that AI model and guardrail failures are demonstrated risks rather than vendor speculation.

The company positions regulated, fast-moving AI adoption as the consequence that matters. Enkrypt argues that enterprises want to ship generative AI quickly but stall on security, privacy, and compliance, which is the gap its lifecycle products are built to close before and during production. \[[s2](#profile-analysis-sources), [s10](#profile-analysis-sources), [s9](#profile-analysis-sources)\]

### Product Capabilities

Enkrypt AI ships an unusually wide line for its stage. Agent Red Teaming runs continuous adversarial testing in CI/CD and staging, mapping findings to OWASP, NIST, and the EU AI Act. Agent Guardrails filter inputs and outputs in production against prompt injection, data leakage, and unsafe tool use. The MCP Scanner discovers and grades tool exposure, and the MCP Gateway enforces policy inline on tool traffic.

Two products extend the line toward governance and data. The Agent Policy Engine converts natural-language regulatory text, including PDFs, into enforceable controls with an audit trail, and an AI Data Risk Audit surfaces data-layer exposure in deployments. The MCP Gateway is open source, with Enkrypt selling enterprise add-ons such as centralized policy management, tenant-aware enforcement, and reporting on top.

The research output demonstrates the same capability the products sell. Enkrypt's team published a study finding DeepSeek's R1 model far more likely to generate harmful content than a comparable rival model, a red-team report on cloud-provider guardrails, and a model safety leaderboard. That body of original adversarial research, covered independently, supports the claim that the team can find the failures its platform automates. \[[s2](#profile-analysis-sources), [s4](#profile-analysis-sources), [s9](#profile-analysis-sources), [s14](#profile-analysis-sources)\]

### Competitive Positioning

Enkrypt competes against both focused AI-security specialists and the platforms consolidating the category. Lakera shipped runtime guardrails before Check Point acquired it, HiddenLayer runs red teaming inside a broader platform, TrojAI pairs build-time testing with a runtime firewall, and Adversa AI and Mindgard sell automated red teaming. The 2025 acquisitions of AI security companies moved overlapping coverage inside larger suites.

Enkrypt's visible distinction is breadth on a small base. Where most independents lead with one or two jobs, Enkrypt offers red teaming, guardrails, MCP controls, a data audit, and a policy engine from one company, betting that buyers want a single vendor for the whole AI-security lifecycle. The named Skyhigh Security CASB integration shows that breadth can land as an embedded partnership rather than a point sale.

The structural risk is who owns the buyer and the layer. Model providers and security platforms can test and protect the AI built on their own infrastructure, the AI-gateway layer is already crowded, and Enkrypt open-sourced its MCP gateway inside it. Breadth raises the bar for any single bundled replacement while exposing several fronts at once. \[[s2](#profile-analysis-sources), [s4](#profile-analysis-sources), [s1](#profile-analysis-sources), [s6](#profile-analysis-sources), [s15](#profile-analysis-sources)\]

### Go-to-Market & Traction

Enkrypt's clearest go-to-market signal is named-customer proof that exceeds its peers. Skyhigh Security's EVP of Product says the company integrated Enkrypt's generative AI risk capabilities and guardrails into its CASB offerings, NetApp's VP of Data Science credits Enkrypt with removing data-layer risks, and Phot.AI's co-founder names it as the choice to secure a multimodal creative application. These are attributed references, not anonymous logos.

Research is the second engine, and it points outward. The DeepSeek harmful-output study, the cloud-provider guardrail-bypass report, and the model safety leaderboard drew independent coverage and analyst attention, building inbound interest. A 2025 Gartner Cool Vendor naming in AI security adds third-party recognition that opens enterprise conversations.

The motion is enterprise-direct and partner-assisted. Enkrypt routes prospects to a demo request and a self-serve API and SDK, leans on its research brand to open conversations, and uses embedded partnerships such as Skyhigh to reach buyers inside tools they already run. A disclosed funding round and paying-customer references would be the signals that this attention has converted into durable revenue. \[[s1](#profile-analysis-sources), [s11](#profile-analysis-sources), [s9](#profile-analysis-sources), [s6](#profile-analysis-sources), [s13](#profile-analysis-sources), [s14](#profile-analysis-sources)\]

### Team & Credibility

Enkrypt AI's founders pair academic depth with a security and AI focus. Co-founder and CEO Sahil Agarwal and co-founder and CTO Prashanth Harshangi both hold Yale PhDs and started the company in 2022 around the security, privacy, and compliance gaps that slow enterprise LLM adoption. The research record is the team's strongest public signal. Enkrypt has published a multi-year stream of in-domain adversarial work, from the DeepSeek harmful-output study to the cloud-provider guardrail-bypass report and a model safety leaderboard benchmarking many models. Independent outlets covered the findings, making this a sustained publication pattern in the company's own product domain rather than a single covered event.

The credibility basis differs from operator-led rivals. Where some competitors lead with a veteran enterprise-sales executive, Enkrypt builds its standing on founder research depth and a shipping product line, verifiable through its publications and the named partnerships those publications helped open. \[[s7](#profile-analysis-sources), [s9](#profile-analysis-sources), [s10](#profile-analysis-sources), [s14](#profile-analysis-sources)\]

### Trust Readiness

Enkrypt's trust posture pairs a compliance-led message with early attestation signals. The platform centers on mapping controls to the EU AI Act, NIST, and OWASP, and the policy engine generates audit traceability, which speaks directly to the regulated buyers it targets in financial services and healthcare. An AICPA badge sits on its research site, displayed without an inspectable report.

Deployment and data handling are addressed in the product design. The guardrails and MCP gateway run inline in the request and response path, and the gateway can be deployed as a sidecar inside the customer environment, which addresses the data-exposure question a security buyer raises when a product inspects proprietary AI traffic. A procurement team would still confirm the certification status, the readiness item most likely to surface in a security review at this stage. \[[s5](#profile-analysis-sources), [s4](#profile-analysis-sources), [s9](#profile-analysis-sources)\]

### Competitors

| Company | Relationship | Note |
|---|---|---|
| TrojAI | competes with | Pairs build-time red teaming with a runtime firewall over the same AI models, applications, and agents, the closest lifecycle overlap with Enkrypt's line. |
| Lakera | competes with | Shipped runtime AI guardrails overlapping Enkrypt's before Check Point acquired it, moving the runtime job into a platform. |
| HiddenLayer | competes with | Independent AI security platform whose red teaming and runtime modules overlap Enkrypt's inside a broader lifecycle suite. |
| Adversa AI | competes with | Independent AI red-teaming specialist contesting the testing side of Enkrypt's line, also research-led but without runtime guardrails. |
| Prompt Security | competes with | Runtime AI security vendor contesting the production-guardrails and MCP-control jobs Enkrypt covers. |
| OpenAI | adjacent | Model provider that could ship native red teaming and guardrails for AI built on its platform, removing the third-party budget line. |

## Strategy Deep Dive

A closer look at the company's product strategy, measuring how [defensible](https://zeltser.com/scoring-security-product-strategy) it is against market forces and examining the [eight areas](https://zeltser.com/security-product-creation-framework) behind it.

### Defensibility

**Exposed (12/21)**

Band guidance: pivot urgently. Analyzed 2026-09-11. Scope: AI security platform (red-teaming and guardrails).

The adversarial expertise behind Enkrypt AI's red teaming and research is what a rival would take longest to reproduce. Its red teaming tests AI systems across text, audio, and vision, and its research includes a report on vulnerabilities in major cloud providers' AI safety systems. It does not disclose a proprietary model or data set behind its guardrails. Its products map findings to NIST, OWASP, and the EU AI Act, and it displays an AICPA badge on its website. Customers run its software themselves, with red teaming in CI/CD pipelines and guardrails on each prompt, tool call, and output. Its gateway between AI agents and the tools they call is open source, and Enkrypt sells policy management, reporting, and human security reviews on top.

| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery | 1/3 | Customers buy software they configure and run, the red-teaming SDK in CI/CD, inline guardrails in the request flow, and an open-source MCP gateway, with human reviewers offered as an enterprise add-on rather than the product, a delivered software artifact rather than a managed accountability service. \[[s2](#deep-dive-sources), [s4](#deep-dive-sources)\] |
| Switching Cost | 2/3 | Wiring red teaming into CI/CD, running guardrails inline in production, and deploying the MCP gateway as a sidecar create real friction to replace, while the line overlays the customer's existing stack with no data-residency lock or network effect to earn a 3. \[[s2](#deep-dive-sources), [s3](#deep-dive-sources), [s4](#deep-dive-sources)\] |
| Compliance Moat | 1/3 | Enkrypt maps controls to the EU AI Act, NIST, and OWASP and self-displays an AICPA SOC 2 badge with a SOC 2 Type 2 attestation reported in the catalog record, so 1, because the cited record identifies no product-specific regulatory mandate, federal authorization, airgapped edition, or inspectable report. \[[s5](#deep-dive-sources), [s12](#deep-dive-sources), [s13](#deep-dive-sources), [s14](#deep-dive-sources)\] |
| Problem Complexity | 3/3 | Multimodal and multilingual adversarial red teaming across agents, RAG, and MCP, plus inline multi-boundary guardrails that rewrite, block, or escalate, is applied adversarial-AI engineering that takes years of specialized expertise. \[[s2](#deep-dive-sources), [s3](#deep-dive-sources), [s9](#deep-dive-sources)\] |
| Buyer Profile | 2/3 | The named buyer is the enterprise security or AI team in regulated sectors, and the case-studies page now names AI21 Labs and NATO StratCom COE alongside vendor-displayed testimonials from Skyhigh, NetApp, and Phot.AI rather than disclosed paying-enterprise programs at scale. \[[s6](#deep-dive-sources), [s15](#deep-dive-sources)\] |
| Layer | 2/3 | The guardrails and MCP gateway run inline as a control plane over a customer's AI traffic, but the line is not infrastructure other software depends on to function and still ingests from the models and tools it does not own. \[[s4](#deep-dive-sources), [s3](#deep-dive-sources), [s1](#deep-dive-sources)\] |
| Proprietary Data, Content, or IP | 1/3 | Fetched sources do not identify a proprietary model or private corpus behind the guardrail, policy, or red-team reasoning, and the adversarial research, the DeepSeek-R1 study and the cloud-provider guardrail-bypass report, is published rather than identified as a non-public corpus, so a funded rival can rebuild it. \[[s9](#deep-dive-sources), [s8](#deep-dive-sources), [s3](#deep-dive-sources)\] |

### Strategic Market Segmentation

Enkrypt targets the enterprise team putting generative AI into a core workflow and stalling on security review. The product pages frame the buyer as a security or AI team that needs to ship LLM applications, agents, and MCP tool connections without the model, the runtime data flow, or the tool calls becoming an unmanaged attack surface. The pitch emphasizes regulated, fast-moving adoption, with customer-facing agent and chatbot use cases inside that frame.

Breadth lets one company address several buyer jobs from a single line. A team that wants pre-release assurance buys Agent Red Teaming, a team in production buys Agent Guardrails, and a team facing an audit buys the Agent Policy Engine that maps controls to the EU AI Act and NIST. That spread reaches the testing buyer, the runtime buyer, and the governance buyer without three separate vendors.

The named demand is real but mostly vendor-displayed. The homepage carries attributed testimonials from Skyhigh Security, NetApp, and Phot.AI, with Skyhigh describing an embedded CASB integration rather than a point purchase, and the case-studies page names AI21 Labs and NATO StratCom COE with dedicated stories. The open question is whether that breadth produces disclosed paying references at enterprise scale or stays a set of partner integrations, named case studies, and quotes. \[[s2](#deep-dive-sources), [s6](#deep-dive-sources), [s15](#deep-dive-sources)\]

### Product Capabilities & AI Advantages

The red-teaming product is the sharpest capability and the source of the company's public credibility. Agent Red Teaming runs adversarial tests across text, audio, and vision against agents, tools, RAG, and MCP, in CI/CD gates and in staging and production, and maps findings to NIST, OWASP, and the EU AI Act. The same craft produced the DeepSeek-R1 study Enkrypt published through the Cloud Security Alliance blog and a cloud-provider guardrail-bypass report.

The runtime guardrails enforce at every boundary an agent crosses rather than only at the prompt. Agent Guardrails filters inputs and defends against injection at the prompt boundary, screens sources at the retrieval boundary, approves or denies tool calls at the tool boundary, and enforces tone and disclosure at the output boundary, with rewrite, block, or escalate decisions across text, image, and audio. The MCP Gateway runs those same guardrails inline on tool traffic before actions execute.

The durable advantage is the research record, not the runtime stack. The guardrails and gateway reason against the same failure taxonomy any funded rival can build, and no named non-public training corpus or third-party accuracy benchmark appears in fetched sources. The published research record provides stronger differentiation than the documented runtime components. \[[s2](#deep-dive-sources), [s3](#deep-dive-sources), [s9](#deep-dive-sources), [s8](#deep-dive-sources)\]

### Sales Engagement & Go-to-Market

Enkrypt's public-facing motion leads with research and self-serve entry points. The DeepSeek-R1 study drew outside trade-press attention, Enkrypt publishes its own cloud-provider guardrail-bypass report and model-safety leaderboard, and the product pages route prospects to a demo request, a published pricing page, and a self-serve API and SDK that a developer can call in minutes. The cited record does not disclose how much pipeline that content pulls in or how the sales organization is structured.

Conversion proof is attributed and improving. Skyhigh Security's product EVP publicly credits Enkrypt's generative AI risk capabilities and guardrails with strengthening its CASB offerings, an embedded partnership stronger than an anonymous logo, NetApp and Phot.AI add named testimonials, and the case-studies page carries dedicated AI21 Labs and NATO StratCom COE stories. No disclosed revenue, paying-customer count, or deployment scope beyond these named accounts and quotes appears in fetched sources.

The funding posture frames the stage. The cited public record identifies a $2.35M 2024 seed, while Enkrypt now markets six products. That posture is the constraint a better-funded rival can exploit. \[[s6](#deep-dive-sources), [s7](#deep-dive-sources), [s15](#deep-dive-sources), [s8](#deep-dive-sources), [s2](#deep-dive-sources)\]

### Pricing Model

Enkrypt publishes a tiered, credit-metered price list rather than hiding every number behind a demo. The pricing page sets out an Explore tier at $0 per month with 500 starting credits and no credit card, a Launch tier at $149 per month for startups running a single production agent, and a higher enterprise tier above it, with consumption metered in credits across the platform. That is a self-serve on-ramp under a sales-assisted top end rather than a fully private quote.

The open-source MCP gateway implies a second value meter beneath the credits. The gateway itself is free and open source, and Enkrypt charges for enterprise capabilities such as centralized policy management, tenant-aware enforcement, reporting, support, and human experts for security reviews. That sells the control plane on adoption first and the governance and operations layer on top.

Enterprise commitments still resolve to a conversation. The published tiers route a smaller buyer to Get Started and a larger one to Talk to an Expert or Book a Demo, and Enkrypt combines credit-based usage metering with feature-gated subscription tiers and a custom enterprise plan. Confirming the enterprise unit and its price would require the sales conversation the top tier signals. \[[s14](#deep-dive-sources), [s4](#deep-dive-sources), [s5](#deep-dive-sources)\]

### Product Delivery & Operations

Enkrypt delivers software the customer wires in and operates: hosted API and SDK services on the published tiers, a self-hostable open-source gateway, and a VPC or on-premises option at the enterprise tier, with experts available on the side rather than as the product. Agent Red Teaming ships as an SDK and API that a developer wires into CI/CD and into staging and production runs. Agent Guardrails runs inline in the request and response path, and the MCP Gateway is deployable as a sidecar inside the customer environment, which addresses the data-exposure question a buyer raises when a product inspects proprietary AI traffic.

The open-source gateway changes the adoption path more than the operating model. A team can run the open-source MCP Gateway itself for full transparency and add Enkrypt only when it needs policy packs, audit-ready exports, and human reviewers. The enterprise tier adds centralized policy, tenant-aware enforcement, and reporting on top of self-run software.

Operational collateral stays light for the category. Published uptime numbers and a connector catalog do not surface in fetched pages, the pricing page lists 8x5 support with 24x7 add-ons, and the gateway documentation describes latency budgets and deployment patterns rather than guaranteed service levels. The delivered artifact is software the customer operates, not a managed service that accepts hands-on responsibility for outcomes. \[[s4](#deep-dive-sources), [s2](#deep-dive-sources), [s3](#deep-dive-sources), [s14](#deep-dive-sources)\]

### Earning Customers' Trust

Enkrypt leads its trust message with compliance mapping rather than a published attestation report. The Agent Policy Engine converts governance policy and regulatory text, including PDFs, into enforceable controls with an audit trail, and the red-teaming and guardrail products map findings to the EU AI Act, NIST, and OWASP. That speaks directly to regulated enterprise buyers.

The attestation signal is a self-displayed badge, not an inspectable report. An AICPA SOC 2 badge sits in the page footer and the catalog record states a SOC 2 Type 2 attestation, which signals a completed audit effort but resolves to no downloadable report or trust portal in fetched sources. A procurement team would still confirm the certification status in a security review.

The product design carries part of the trust case. The guardrails run inline and the gateway can deploy as a sidecar inside the customer environment, so proprietary AI traffic need not leave the customer's control, and the policy engine generates the audit traceability a reviewer asks for. A buyer should still resolve model-provider and data-handling terms beyond the self-displayed attestation. \[[s5](#deep-dive-sources), [s12](#deep-dive-sources), [s13](#deep-dive-sources), [s14](#deep-dive-sources)\]

### Platform Strategy & Ecosystem Positioning

Enkrypt positions itself as the lifecycle control plane for enterprise AI rather than a point tool. The homepage frames a detect, remove, monitor, and comply loop, and the six products line up against it: red teaming detects, guardrails remove, the policy engine and audit prove compliance, and the MCP scanner and gateway govern the tool layer. The platform claim rests on covering the whole lifecycle from one vendor.

The open-source MCP gateway is the deliberate ecosystem play. By open-sourcing the control plane that sits between agents and MCP servers, Enkrypt seeds adoption in a layer that is still forming and sells enterprise policy, multi-tenancy, and reporting on top. That bets on becoming the default control point for MCP tool traffic before a platform vendor ships its own.

The exposure is the bundling path the record already documents: Check Point moved to acquire Lakera and its runtime guardrails for a larger suite, and the control point Enkrypt is seeding invites the same play from larger platforms. \[[s1](#deep-dive-sources), [s4](#deep-dive-sources), [s11](#deep-dive-sources)\]

### Team & Execution Capability

Enkrypt's credibility comes from two research-grounded founders. Co-founder and CEO Sahil Agarwal and co-founder and CTO Prashanth Harshangi are Yale PhDs who started the company in 2022 around the security, privacy, and compliance gaps that slow enterprise LLM adoption. Press coverage quotes the two founders, and the cited record names no hired go-to-market executive.

The published research is the team's strongest verifiable signal. Enkrypt's adversarial work spans the DeepSeek-R1 harmful-output study published through the Cloud Security Alliance blog, a cloud-provider guardrail-bypass report, and a model safety leaderboard, a multi-year in-domain pattern rather than a single covered event. Independent outlets including Infosecurity Magazine reported the findings.

Depth below the founders is the open question. The public record establishes the two founders' adversarial-AI expertise, but individual credentials for the broader team that has to ship and maintain six product lines do not surface in fetched sources, so the bench is less visible than the founders. \[[s7](#deep-dive-sources), [s8](#deep-dive-sources), [s9](#deep-dive-sources), [s10](#deep-dive-sources)\]

## Sources

### Company Detail Sources

Cited from the Sourced Details and Matrix Coverage rows.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | [Enkrypt AI: Homepage](https://www.enkryptai.com) | official | 2026-07-09 |
| f2 | [Startups Magazine on Enkrypt AI seed round](https://startupsmagazine.co.uk/article-enkrypt-ai-raises-235m-build-visibility-and-security-layer-gen-ai) | press | 2026-06-14 |
| f3 | [Enkrypt AI research report page footer with company address](https://www.enkryptai.com/company/resources/research-reports/red-teaming-cloud-provider-ai-guardrails) | official | 2026-06-13 |
| f4 | [Startups Magazine on Enkrypt AI 2.35M seed round](https://startupsmagazine.co.uk/article-enkrypt-ai-raises-235m-build-visibility-and-security-layer-gen-ai) | press | 2026-06-13 |
| f5 | [AI Defense Matrix Catalog entry](https://catalog.aidefensematrix.com/products/enkrypt-ai/) | other | 2026-06-13 |
| f6 | [AI Defense Matrix Catalog mapping](https://catalog.aidefensematrix.com/products/enkrypt-ai/) | other | 2026-06-23 |

### Profile Analysis Sources

Cited from the Market Readiness section.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | [Enkrypt AI homepage](https://www.enkryptai.com/) | official | 2026-06-13 |
| s2 | [Enkrypt AI Agent Red Teaming product page](https://www.enkryptai.com/product/agent-red-teaming) “Run red teaming where you build: Pre-release gates in CI/CD ... Compliance Mapping (NIST, OWASP, EU AI Act)” | official | 2026-06-13 |
| s3 | [Enkrypt AI Agent Guardrails product page](https://www.enkryptai.com/product/agent-guardrails) | official | 2026-06-13 |
| s4 | [Enkrypt AI MCP Gateway product page](https://www.enkryptai.com/product/mcp-gateway) “The gateway is open source. Enkrypt AI adds enterprise capabilities like centralized policy management, tenant-aware enforcement, reporting, and support.” | official | 2026-06-13 |
| s5 | [Enkrypt AI Agent Policy Engine product page](https://www.enkryptai.com/product/agent-policy-engine) “Enkrypt AI Policy Engine converts natural-language AI governance policy and regulatory text (including PDFs) into controls - then enforces those controls across the platform with traceability you can audit.” | official | 2026-06-13 |
| s7 | [Startups Magazine on Enkrypt AI 2.35M seed round and founders](https://startupsmagazine.co.uk/article-enkrypt-ai-raises-235m-build-visibility-and-security-layer-gen-ai) “Enkrypt AI raises $2.35M to build visibility and security layer for Gen AI” | press | 2026-06-13 |
| s8 | [Enkrypt AI model safety red-teaming report page](https://www.enkryptai.com/red-teaming-report) | official | 2026-06-13 |
| s9 | [Enkrypt AI cloud-provider guardrails red-teaming research report](https://www.enkryptai.com/company/resources/research-reports/red-teaming-cloud-provider-ai-guardrails) “Discover critical vulnerabilities in major cloud providers' AI safety systems” | official | 2026-06-13 |
| s10 | [Infosecurity Magazine on DeepSeek R1 security weaknesses and red-team reports](https://www.infosecurity-magazine.com/news/deepseek-r1-security/) “security reports have started to show that R1 also has many security weaknesses that could expose any organizations deploying the LLM.” | press | 2026-06-13 |
| s11 | [Enkrypt AI Gartner Cool Vendor in AI security 2025 announcement](https://www.enkryptai.com/blog/enkrypt-ai-recognized-as-a-gartner-cool-vendor-in-ai-security-2025) | official | 2026-06-13 |
| s12 | [Enkrypt AI MCP Scanner product page](https://www.enkryptai.com/product/mcp-scanner) “Enkrypt AI MCP Scanner discovers tools, analyzes capabilities and permissions, and surfaces high-risk exposure - so you can adopt MCP with clear ownership and least privilege.” | official | 2026-06-13 |
| s6 | [Enkrypt AI homepage customer testimonials from Skyhigh Security, NetApp, and Phot.AI](https://www.enkryptai.com/) “By integrating Enkrypt AI’s generative AI risk capabilities and advanced guardrails, we’ve significantly strengthened our CASB offerings, empowering customers with real-time visibility and control over AI-driven LLM focused risks.” | official | 2026-06-13 |
| s13 | [Enkrypt AI and NetApp Collaborate to Bring AI Risk Enforcement to the Data Layer](https://natlawreview.com/press-releases/enkrypt-ai-and-netapp-collaborate-bring-ai-risk-enforcement-data-layer) “AI governance must be enforced where data lives, in real time, at machine scale. Our NetApp collaboration gives security leaders control without slowing AI innovation.” | press | 2026-06-13 |
| s14 | [DeepSeek-R1 AI Model 11x More Likely to Generate Harmful Content, Security Research Finds](https://cloudsecurityalliance.org/blog/2025/02/19/deepseek-r1-ai-model-11x-more-likely-to-generate-harmful-content-security-research-finds) “AI race between US and China take a dark turn as red teaming report uncovers critical safety failures” | press | 2026-06-13 |
| s15 | [SecurityWeek: Check Point to Acquire AI Security Firm Lakera](https://www.securityweek.com/check-point-to-acquire-ai-security-firm-lakera/) “The announcement comes on the same day that CrowdStrike announced plans to acquire AI security firm Pangea, and reflects growing enterprise demand for safeguarding generative AI, large language models (LLMs), and autonomous agents as they become embedded in enterprise operations.” | press | 2026-06-16 |

### Deep-Dive Sources

Cited from the Strategy Deep Dive section.

| Id | Source | Tier | Accessed |
|---|---|---|---|
| s1 | [Enkrypt AI homepage: AI Security Agents That Govern and Protect](https://www.enkryptai.com/) “Detect. Continuously red team your AI with dynamic, use-case-specific attacks that adapt in real time. Remove. Real-time guardrails stop threats fast and get smarter with every prompt. Comply. Translate internal policies and external regulations into automated guardrails.” | official | 2026-06-18 |
| s2 | [Enkrypt AI Agent Red Teaming product page (multimodal, CI/CD, compliance mapping)](https://www.enkryptai.com/product/agent-red-teaming) “Enkrypt AI Red Teaming finds real failure modes across text, audio, and vision, including agents, tools, RAG, and MCP. Pre-release gates in CI/CD. Scheduled and on-demand testing in staging and production. Red team multimodal and multilingual agents. Compliance Mapping (NIST, OWASP, EU AI Act).” | official | 2026-06-17 |
| s3 | [Enkrypt AI Agent Guardrails product page (per-boundary enforcement)](https://www.enkryptai.com/product/agent-guardrails) “Prompt boundary: Input filtering and injection defense, Policy-aware rewrite/block/escalate. Tool boundary (agents + MCP): Approve/deny tool calls before execution, enforce least privilege. Output boundary: Enforce tone, safety, and disclosure rules.” | official | 2026-06-17 |
| s4 | [Enkrypt AI MCP Gateway product page (open source plus enterprise add-ons)](https://www.enkryptai.com/product/mcp-gateway) “The gateway is open source. Enkrypt AI adds enterprise capabilities like centralized policy management, tenant-aware enforcement, reporting, and support. Add Enkrypt when you need policy packs, audit-ready exports, and human experts for security reviews.” | official | 2026-06-17 |
| s5 | [Enkrypt AI Agent Policy Engine product page (regulation to controls)](https://www.enkryptai.com/product/agent-policy-engine) “Enkrypt AI Policy Engine converts natural-language AI governance policy and regulatory text (including PDFs) into controls, then enforces those controls across the platform with traceability you can audit.” | official | 2026-06-17 |
| s6 | [Enkrypt AI homepage customer testimonials (Skyhigh Security, NetApp, Phot.AI)](https://www.enkryptai.com/) “By integrating Enkrypt AI's generative AI risk capabilities and advanced guardrails, we've significantly strengthened our CASB offerings, empowering customers with real-time visibility and control over AI-driven LLM focused risks. Thyaga Vasudevan, EVP, Product, Skyhigh Security.” | official | 2026-06-18 |
| s7 | [Startups Magazine on Enkrypt AI 2.35M seed round and founders](https://startupsmagazine.co.uk/article-enkrypt-ai-raises-235m-build-visibility-and-security-layer-gen-ai) “Enkrypt AI was founded by two Yale PhDs and AI practitioners Sahil Agarwal (CEO) and Prashanth Harshangi (CTO) in 2022. Enkrypt AI raises $2.35M to build visibility and security layer for Gen AI.” | press | 2026-06-18 |
| s8 | [Enkrypt AI cloud-provider guardrails red-teaming research report](https://www.enkryptai.com/company/resources/research-reports/red-teaming-cloud-provider-ai-guardrails) “Discover critical vulnerabilities in major cloud providers' AI safety systems.” | official | 2026-06-17 |
| s9 | [Enkrypt AI DeepSeek-R1 red-team findings, vendor-authored, hosted on the Cloud Security Alliance blog](https://cloudsecurityalliance.org/blog/2025/02/19/deepseek-r1-ai-model-11x-more-likely-to-generate-harmful-content-security-research-finds) “DeepSeek-R1 AI Model 11x More Likely to Generate Harmful Content, Security Research Finds.” | official | 2026-06-17 |
| s10 | [Infosecurity Magazine on DeepSeek R1 security weaknesses and red-team reports](https://www.infosecurity-magazine.com/news/deepseek-r1-security/) “Security reports have started to show that R1 also has many security weaknesses that could expose any organizations deploying the LLM.” | press | 2026-06-17 |
| s11 | [SecurityWeek: Check Point to Acquire AI Security Firm Lakera](https://www.securityweek.com/check-point-to-acquire-ai-security-firm-lakera/) “Check Point to Acquire AI Security Firm Lakera.” | press | 2026-06-17 |
| s12 | [Enkrypt AI red-teaming report page footer (AICPA badge, Brighton MA address)](https://www.enkryptai.com/red-teaming-report) “Enkrypt AI, Inc 1660 Soldiers Field Rd. Brighton, MA 02135. (AICPA badge image in page footer.)” | official | 2026-06-17 |
| s13 | [AI Defense Matrix Catalog entry for Enkrypt AI (coverage rows)](https://catalog.aidefensematrix.com/products/enkrypt-ai/) “MCP Gateway, an open-source control plane, sits inline between agents and MCP servers to approve, modify, or block tool calls, enforcing least privilege and producing an audit evidence trail.” | other | 2026-06-17 |
| s14 | [Enkrypt AI pricing page (published tiers metered by credits)](https://www.enkryptai.com/pricing) “Choose the plan that matches your AI security needs. Explore: $0/month, Free forever, 500 credits to start, no credit card required. Launch: $149/month, 5,000 credits to start, best for startups launching a single production agent. Start with Explore. Talk to an Expert.” | official | 2026-06-18 |
| s15 | [Enkrypt AI case studies page (AI21 Labs and NATO StratCom COE)](https://www.enkryptai.com/case-studies) “See how enterprises like AI21 Labs and NATO StratCom COE build trust with Enkrypt. Advancing safety alignment for the Jamba model family, AI21 Labs x Enkrypt AI. The imperative for a coordinated defense, NATO x Enkrypt AI.” | official | 2026-06-18 |

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