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.
Enkrypt AI sells software that tests enterprises’ AI agents with simulated attacks before release and blocks risky prompts, actions and outputs while the agents run. Anaconda, maker of a Python and data-science platform, acquired Enkrypt in August 2026 and sells it as Enkrypt AI by Anaconda. Anaconda said products, plans and support would stay unchanged while the companies work toward a unified platform. Enkrypt, which dates its founding to 2022, raised a $2.35 million seed round led by Boldcap. Its named customers are Skyhigh Security, NetApp, Phot.AI and AI21 Labs. Gartner named Enkrypt in its 2025 Cool Vendors in AI Security report, Enkrypt announced. Replacing Enkrypt means integration and workflow changes for a customer that runs its pre-release tests and runtime blocking policies.
| 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] |
|---|---|---|
| Acquisition | Anaconda, announced 2026-08-04 | [f2] |
| Founded | 2022 | [f3] |
| HQ | Boston, Massachusetts, USA | [f4] |
| Funding | $2.35M total | [f3] |
| Latest funding | Seed (Feb 2024, led by BoldCap) | [f5] |
| Deployment | SaaS, Self-hosted | [f6] |
| Compliance | SOC 2 Type 2 | [f6] |
| 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. |
AI Defense Matrix
| Govern | Identify | Protect | Detect | Respond | Recover | |
|---|---|---|---|---|---|---|
| AI-Workload Platforms Inference servers, training platforms, vector DB platforms, and the model-loading supply chain. | ||||||
| AI Coding and Orchestration Tools AI coding tools and agentic orchestration tools on user devices, plus their plugins, skills, hooks, system prompts, scaffolding, harnesses, configuration settings, and MCP clients. | ||||||
| AI-Generated Code Code produced by AI tools, AI-assisted reviews, AI-generated infrastructure-as-code and tests, and vibe-coded apps that bypass CI/CD. | ||||||
| AI Traffic Paths to external and self-hosted AI services, MCP tool channels, agent-to-agent calls, model-registry downloads, and egress to unapproved AI services. AI gateways, LLM routers, and MCP gateways steer traffic along those paths. The steering decisions and their inputs belong here too: routing policies, MCP server registries, and agent naming and discovery services. | ||||||
| AI Model Model weights, fine-tuning checkpoints, model cards, registries, an AI bill of materials (AIBOM), and the third-party LLMs your enterprise consumes. | ||||||
| Training Data Datasets used for training, fine-tuning, and continued learning. | ||||||
| Runtime AI Data User prompts, inference inputs, RAG content, vector DB content, persistent agent memory, and interaction history. | ||||||
| AI Agent Identities AI agents as non-human principals, plus credentials, keys, permission scopes, service accounts, and delegation chains across agents and tools. |
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. [f7]
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 3/5 | Enkrypt names the enterprise team building AI agents, RAG systems and MCP tool connections as its buyer. Infosecurity Magazine reported its DeepSeek-R1 findings, and Euronews reported the risks its research found in two Mistral AI models. The quantified pain, such as its scan finding vulnerabilities on 73% of 25,000 MCP servers, is Enkrypt’s own measurement, which keeps the score at a clear problem without independent quantification. [s8, s21, s30, s31, s3] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 3/5 | Enkrypt’s product pages carry concrete mechanisms: red-team findings with exact inputs and expected versus observed behavior (s8), guardrails that approve or deny tool calls before execution (s9), and an MCP gateway that runs guardrails in the request and response path (s13). The outside coverage relays Enkrypt’s own published findings and Anaconda’s announcement rather than evaluating the product (s18). That is capability detail without an external validation point the next rung would credit. [s8, s9, s13, s18] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 4/5 | Two kinds of buyer-side signal fall within the last year. In October 2025 VentureBeat relayed an analyst category note, Gartner’s view that MCP gateways serve as essential security intermediaries (s29). NetApp’s SEC-filed May 2026 results release reports a buyer integration, its collaboration with Enkrypt on AI risk insights for data-layer security (s45). VentureBeat names the enabler, the 2025 spread of Anthropic’s Model Context Protocol (s29). [s29, s45] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 4/5 | Founders Sahil Agarwal and Prashanth Harshangi hold Yale PhDs, VentureBeat reports. Independent outlets reported the team’s in-domain research across several studies, DeepSeek-R1 red-teaming results in Infosecurity Magazine and a Mistral study in Euronews. Researchers at universities and at China Unicom cite its DeepSeek-R1 testing in their own published papers. That plurality of independently reported research meets the recognized-publication route, short of a category-defining record. [s33, s21, s30, s31, s48, s47] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 | Enkrypt names Skyhigh Security, NetApp and Phot.AI under a trusted-by heading on its homepage, presents AI21 Labs and NATO StratCom COE case studies, and is called an OpenAI compliance integration partner in Anaconda’s announcement. NetApp’s SEC-filed results release confirms one collaboration, and Enkrypt or its acquirer publishes the other relationships. The named references therefore lack the multiply sourced, independent corroboration the next rung needs, and the acquisition is an indirect signal that does not establish customer scale. [s1, s15, s45, s4, s18] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 4/5 | Enkrypt raised a $2.35 million seed led by Boldcap (s33), shipped guardrails, red teaming and an open-source MCP gateway with enterprise additions (s13), and was acquired by Anaconda in August 2026 for an undisclosed sum (s18). Under scoring policy 1.2.0 an exit is rung-4 evidence of confirmed output per dollar. The undisclosed price and the absence of revenue figures keep it from rung 5. [s33, s13, s18] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 | Enkrypt fits the emerging AI security category that platform vendors now sell. Anaconda offers it as Enkrypt AI by Anaconda, a security layer for its AI platform, and Gartner’s September 2025 Cool Vendors in AI Security report named it, according to Enkrypt’s announcement. A 2024 paper by Samsung researchers lists Enkrypt AI among commercial guardrail solutions (s50). The record holds one independent placement and a recognition known through Enkrypt’s own announcement, short of the multiply evidenced placement the next rung needs. [s17, s4, s19, s50] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | Anaconda acquired Enkrypt AI in August 2026. Customers run Enkrypt’s red-team tests in release pipelines and its guardrail policies at runtime, which adds some friction to absorption. The reviewed sources show no structural moat that bundling could not eventually match. [s4, s8, s9, s13] |
Enkrypt AI sells security for the AI applications and agents that enterprises build, covering the models, the data flowing through them and the tools agents call. Its product pages frame the buyer as the security or AI team shipping agents, RAG systems that retrieve documents for a model, and Model Context Protocol (MCP) tool connections.
Independent reporting shows that the failures Enkrypt tests for occur in models. Infosecurity Magazine reported Enkrypt’s finding that DeepSeek-R1 was 11 times more likely than OpenAI’s o1 to create harmful outputs. ZDNET reported its research finding that the models Enkrypt studied are more susceptible to novel jailbreak techniques.
The quantified pain comes mostly from Enkrypt’s own research. Anaconda’s acquisition announcement cites Enkrypt’s scan of more than 268,000 tools across 25,000 MCP servers, which found more than 143,000 vulnerabilities affecting 73% of those servers. [s8, s9, s21, s30, s3, s18]
Enkrypt AI sells six products that cover testing, runtime protection and governance of AI systems. Agent Red Teaming attacks agents, RAG systems and MCP tools across text, audio and vision, and it runs as a pre-release gate in CI/CD pipelines. Agent Guardrails approves, modifies or blocks risky behavior at runtime and logs each decision with a policy ID, policy version and reason code.
Four more products extend the line to MCP tools and governance. MCP Scanner discovers MCP tools, analyzes their permissions and surfaces high-risk exposure, and MCP Gateway sits inline between agents and MCP servers to approve, modify or block tool calls. The Agent Policy Engine turns policy and regulatory text, including PDFs, into enforceable controls, and AI Data Risk Audit produces a sign-off packet for security and legal teams.
The MCP Gateway’s code is public. Its GitHub repository lists an Apache 2.0 license, and Enkrypt sells centralized policy management, tenant-aware enforcement, reporting and support on top of it. [s8, s9, s12, s13, s10, s11, s41]
Enkrypt now competes as a product line of Anaconda, the company behind the Python and data-science platform, which acquired it in August 2026. Anaconda says Enkrypt adds the security, governance and compliance layer for the complete AI agent stack, and it sells the product as Enkrypt AI by Anaconda.
Platform vendors have been buying specialists in this category. SecurityWeek reported in September 2025 on Lakera’s pre-deployment assessment and runtime enforcement products. It also reported CrowdStrike’s plans to acquire Pangea.
Enkrypt sells testing, runtime guardrails, MCP controls and governance from one vendor, and Anaconda describes the product as vendor-neutral and cloud-agnostic. A team can deploy the open-source MCP Gateway without buying anything, and Enkrypt charges when that team needs policy packs, audit-ready exports and human experts for security reviews.
Outside researchers also place Enkrypt among guardrail vendors. A 2024 paper by Samsung R&D Institute UK researchers lists Enkrypt AI among commercial guardrail solutions. The same list names Nvidia’s NeMo Guardrails and OpenAI’s Moderation API. [s18, s6, s4, s17, s23, s13, s50]
Enkrypt names its customers on its own pages. Its homepage lists Skyhigh Security, NetApp and Phot.AI under the heading “Trusted by enterprises building the future with AI”, each with a quoted executive. Its case-studies page, headed “Trusted by the Organizations Shaping the Future of AI”, presents work with AI21 Labs and NATO StratCom COE.
The named relationships differ in kind. Skyhigh Security’s product executive says Skyhigh integrated Enkrypt’s guardrails into its CASB offerings, the cloud access security broker products it sells. NetApp’s fiscal 2026 results release, filed with the SEC, says NetApp collaborated with Enkrypt to integrate AI risk insights with data-layer security. The NATO StratCom COE case study describes a joint examination of AI-enabled influence operations, and the AI21 Labs case study describes red teaming and alignment work used to ship the Jamba 1.5a model.
Partnerships widen the channel. Anaconda’s announcement calls Enkrypt an OpenAI compliance integration partner for ChatGPT Enterprise customers, and Enkrypt’s partners page quotes a Citrix NetScaler executive on a collaboration with Enkrypt. Enkrypt publishes self-serve prices from a free tier to $1,499 a month, and Startup Fortune reports that Anaconda acquired it for an undisclosed sum. [s1, s15, s39, s40, s45, s4, s27, s14, s18]
Enkrypt AI’s founders pair research training with a security focus. VentureBeat reported that Yale PhDs Sahil Agarwal and Prashanth Harshangi founded the company, and Startups Magazine’s copy of the company’s announcement dates the founding to 2022, while VentureBeat’s seed-round article says 2023. Agarwal is CEO and Harshangi is CTO, and the team page lists Nathan Trueblood as Chief Product Officer.
Independent outlets have reported the team’s research across several studies. Infosecurity Magazine covered its DeepSeek-R1 red-teaming results, and Euronews covered its study of two Mistral models. VentureBeat interviewed Merritt Baer, Enkrypt’s Chief Security Officer.
Outside researchers cite Enkrypt’s model testing in their own papers. A 2025 survey by researchers at Tennessee Tech University and the University of Nebraska at Omaha cites its DeepSeek-R1 testing. The survey describes those findings as not peer-reviewed. A China Unicom team cites Enkrypt’s DeepSeek red-teaming report in its safety evaluation of DeepSeek models in Chinese contexts.
Gartner named Enkrypt a Cool Vendor in AI Security in a September 2025 report, according to Enkrypt’s announcement. Agarwal co-wrote Anaconda’s acquisition announcement as Enkrypt’s co-founder and CEO, alongside Anaconda CEO David DeSanto. [s33, s20, s16, s21, s30, s31, s29, s48, s47, s19, s3]
Enkrypt’s trust material centers on compliance mapping and audit evidence. The Agent Policy Engine ships OWASP, NIST and EU AI Act mappings as defaults and links each control to its source clause, and Agent Guardrails logs each decision with its policy version and reason code.
Enkrypt announced a SOC 2 Type 2 certification in September 2024. Its Enterprise plan offers VPC or on-premises deployment, and the MCP Gateway runs as a sidecar or gateway in the customer’s tool path, which gives buyers options to run inspection in their own environment. [s10, s9, s28, s14, s13]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| TrojAI | competes with | Competes for the same buyer of AI red teaming and runtime protection for models, applications and agents. | N/AWe captured the evidence for these companies under different evidence-model versions (v1 vs v2), so the totals were scored under different conditions and are not directly comparable. |
| Lakera | competes with | Competes for AI red-teaming and runtime-guardrail budgets that Enkrypt also targets. | |
| HiddenLayer | competes with | Competes for enterprise AI security budgets across testing and runtime protection. | |
| Adversa AI | competes with | Competes on the AI red-teaming side of Enkrypt’s product line. | |
| Prompt Security | competes with | Competes for runtime AI security and MCP-control budgets. | |
| OpenAI | adjacent | Provider of ChatGPT Enterprise, for which Anaconda describes Enkrypt as a compliance integration partner. | N/AWe scored these companies at different scopes, so the totals measure different things. |
Add analyzed competitors to compare them side by side with Enkrypt AI.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
reinforce or reposition
Enkrypt AI sells red teaming, runtime guardrails and MCP software for securing connections between AI agents and tools. Customers configure and run the software themselves, with human security reviews as an add-on. Enkrypt has accumulated its own scan data from more than 268,000 tools across 25,000 MCP servers, Anaconda reports. It also runs continuous red teaming of more than 200 models for its leaderboard. Customers run its red-team tests in automated release-testing pipelines and apply its guardrail policies at runtime. Replacing the product involves integration and workflow work. Its compliance evidence is a SOC 2 Type 2 certification and control mappings to OWASP, NIST and the EU AI Act. Its MCP Gateway is open source. Since August 2026 it has been part of Anaconda’s platform.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | Customers buy software they configure and run, including red-teaming SDKs in CI/CD, inline guardrails and a self-hosted MCP Gateway. Human experts for security reviews come as an enterprise add-on, so the software itself is the product. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Red-team regression suites pinned in CI, guardrail policies enforced inline and a gateway in the tool path create integration and workflow friction to replace. The switching mechanism is documented, and the cited record does not size the migration. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Enkrypt announced a SOC 2 Type 2 certification in 2024 and maps controls to OWASP, NIST and the EU AI Act. A funded competitor can obtain those attestations and build those mappings, and the reviewed sources show no product-carried authorization or retained liability. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Enkrypt’s software requires applied machine-learning and adversarial expertise. It tests AI agents across languages and text, audio and images. Its guardrails enforce controls on prompts, retrieved data, tool calls and outputs. Its policy engine converts regulatory PDFs into traceable controls. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 3/3 | Enkrypt’s chief executive says its customers include Fortune 500 companies in finance and healthcare (s19), and a testimonial from an insurance services company describes a customer-facing chatbot kept safe and compliant with Enkrypt (s1). Under buyer_profile policy 1.0.0, evidenced regulated-enterprise buyers suffice. Both statements are Enkrypt’s own, and the insurance company is unnamed. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 3/3 | Enkrypt describes its guardrails as the runtime layer that approves, modifies or blocks agent, tool, RAG and MCP behavior (s9). Its MCP gateway runs as a sidecar or gateway in the customer’s tool path (s13), so applications deployed that way wait on Enkrypt’s decision before a tool call proceeds. That is the infrastructure relationship. These are Enkrypt’s own descriptions of its architecture. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 | Enkrypt’s guardrails run on its own models (s33), and it benchmarks more than 200 models on a public safety leaderboard (s19). The cited record documents models in use and published research. It contains no evidence of a retained, non-public dataset that accrues to Enkrypt or a granted patent. Its MCP gateway is open source (s13). Under policy 1.3.0 that is rung 1. |
Enkrypt sells to enterprise security, AI and product teams deploying agents and systems that retrieve documents for AI models. These teams also deploy connections between agents and external tools. Its red-teaming outputs address both audiences, with regression suites for product teams and prioritized vulnerabilities and evidence trails for security teams.
Its price list spans a wide range of buyers. The free Explore tier targets students, individual developers and researchers, the Launch tier targets startups with a single production agent, and the Enterprise tier targets enterprises in regulated industries that need VPC or on-premises deployment.
The organizations Enkrypt names are mostly technology companies. Its homepage names Skyhigh Security, NetApp and Phot.AI and quotes an unnamed insurance services company, and its case studies cover AI21 Labs and NATO StratCom COE. Anaconda says 95% of the Fortune 500 rely on Anaconda. That figure describes Anaconda’s customer base, and Anaconda says details of the unified platform with Enkrypt will follow.
Enkrypt’s red-teaming software generates attacks tailored to a customer’s policies, domain and connected tools. Customers can repeat these tests automatically before each release. Its coverage includes memory and retrieval poisoning as well as attacks across text, audio and vision, and Anaconda’s announcement counts more than 300 attack categories.
The guardrails enforce policy at runtime at the prompt, retrieval, tool and output boundaries. Enkrypt advertises millisecond response times, and red-teaming findings can be promoted into guardrail policies and monitored in production.
The research team also runs large benchmarks. Enkrypt’s LLM Safety Leaderboard benchmarks more than 200 models with continuous red teaming, and its cloud-guardrail study ran 2,160 targeted attack attempts against Microsoft Azure, Amazon Bedrock and Meta safety models. Its AI21 Labs case study describes red teaming and synthetic alignment data used to ship Jamba 1.5a.
Enkrypt sells through self-serve plans, a sales-led enterprise tier and partners. A developer can start on the free tier without a credit card and install the SDK to call the red-teaming and guardrail APIs, while enterprise buyers contact sales for custom plans.
Enkrypt also reaches buyers through other companies’ products. Skyhigh Security integrated Enkrypt’s guardrails into its CASB offerings, and NetApp’s SEC-filed results release describes a collaboration integrating Enkrypt’s AI risk insights with data-layer security. Enkrypt’s partners page quotes a Citrix NetScaler executive on a collaboration, and Anaconda’s announcement calls Enkrypt an OpenAI compliance integration partner for ChatGPT Enterprise.
Before the acquisition, VentureBeat reported a $2.35 million seed round led by Boldcap. Anaconda also sells the product as Enkrypt AI by Anaconda, and SecurityInfoWatch reports Anaconda saying existing products, plans and support continue unchanged while the two companies work toward a unified platform.
Enkrypt publishes a four-tier price list. The free option includes 500 starting credits. Paid options cost $149 or $1,499 a month, and annual billing lowers these prices to $134 or $1,349 a month. Enkrypt offers custom pricing through sales. Enterprise adds role-based access control, VPC or on-premises deployment, unlimited usage for VPC deployments, custom data retention and SLAs, and 24x7 support as an option.
The MCP Gateway is also free as open source under an Apache 2.0 license. Enkrypt charges when a team needs policy packs, audit-ready exports and human experts for security reviews on top of the gateway.
Customers wire Enkrypt’s software into their own systems. Customers integrate red teaming through Enkrypt’s software development kit and API for automated release testing, staging and production. Customers integrate guardrails through an API or agent middleware. They deploy the MCP Gateway in their tool-call path using Kubernetes, edge or service-mesh setups.
Integrations connect the products to existing security operations. Guardrails exports logs to SIEM tools and uses SSO and identity claims from Okta or Azure AD for role-aware policies, and the Policy Engine connects to Jira, ServiceNow, Splunk, Sentinel and Datadog.
Human expertise is an add-on to the software. Enkrypt offers human experts for security reviews with the enterprise gateway, and its Enterprise tier offers custom SLAs and 24x7 support.
Enkrypt backs its trust claims with an audit report and control traceability. It announced a SOC 2 Type 2 certification in September 2024, using Scrut’s compliance platform during the audit. The Policy Engine keeps version history, approvals and rollback for each control and links every control to the clause that triggered it.
Deployment options address data handling. The Enterprise tier offers VPC or on-premises deployment, and the MCP Gateway runs in the customer’s tool path, which gives buyers options to run inspection in their own environment. Anaconda’s announcement describes runtime guardrails deployable inside a customer’s own environment.
Enkrypt is now the security layer of Anaconda’s platform. Startup Fortune describes Anaconda as the company behind the Python and data-science platform used by millions of developers. Anaconda’s announcement says any agent a customer builds on the Anaconda Platform has that security and safety layer built into it.
The open-source MCP Gateway is Enkrypt’s entry point for developers. Its GitHub repository lists 57 stars and 8 forks, and its install commands target Cursor and Claude Desktop.
Anaconda describes Enkrypt as vendor-neutral and cloud-agnostic, and says support for enterprises that rely on Anthropic is coming soon. Enkrypt’s guardrails already work with a customer’s existing model stack through an API.
Enkrypt’s founders are research-trained. VentureBeat reported that Yale PhDs Sahil Agarwal and Prashanth Harshangi founded the company, and Startups Magazine’s copy of the company’s announcement dates the founding to 2022, while VentureBeat’s seed-round article says 2023. The team page lists Nathan Trueblood as Chief Product Officer and describes a core team of mathematicians, physicists and computer scientists.
Independent outlets have reported the team’s research across several studies. Infosecurity Magazine covered its DeepSeek-R1 red-teaming results, and Euronews covered its Mistral study. VentureBeat interviewed Chief Security Officer Merritt Baer. Gartner named Enkrypt a Cool Vendor in AI Security in 2025, according to Enkrypt’s announcement.
Outside researchers cite Enkrypt’s model testing in their own papers. A 2025 survey by researchers at Tennessee Tech University and the University of Nebraska at Omaha cites its DeepSeek-R1 testing. The survey describes those findings as not peer-reviewed. A China Unicom team cites Enkrypt’s DeepSeek red-teaming report in its safety evaluation of DeepSeek models in Chinese contexts.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Enkrypt AI: Homepage | official | 2026-10-03 |
| f2 | Anaconda: Anaconda Acquires Enkrypt AI to Secure the Trillion-Token Enterprise (press release) | official | 2026-10-03 |
| f3 | Startups Magazine on Enkrypt AI seed round | press | 2026-10-03 |
| f4 | Enkrypt AI research report page footer with company address | official | 2026-10-03 |
| f5 | Startups Magazine on Enkrypt AI 2.35M seed round | press | 2026-06-13 |
| f6 | AI Defense Matrix Catalog entry | other | 2026-06-13 |
| f7 | AI Defense Matrix Catalog mapping | other | 2026-06-23 |
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