DeepKeep

• Model providers and the platforms that bought runtime AI security rivals in 2025 could bundle an LLM firewall and red teaming into suites an enterprise already buys, undercutting a standalone DeepKeep purchase on its crowded side.
• DeepKeep names no paying customer publicly, so buyers who require current named references could stall enterprise deals for a small company.
• The computer-vision differentiation rests heavily on founder Rony Ohayon's research and pedigree, so his reduced involvement could erode the part of the platform rivals find hardest to copy.
• DeepKeep has disclosed under $25 million in total funding, and a capital-heavy fight for enterprise deployments against better-funded platforms could force a raise on weak terms or a sale.
• The computer-vision and multimodal security that sets DeepKeep apart serves a narrower buyer set, so the vision line could stay a research-grade specialty while revenue depends on the contested language-model side.
• Switching costs may stay low where DeepKeep is deployed as an out-of-band or policy overlay rather than inline in production controls, so a customer could cancel the firewall and red-teaming subscription with less friction, though the firewall can also sit inline in the request flow and the actual integration depth at each buyer is unverified.

DeepKeep treats the AI models, applications, and agents an enterprise builds as the assets under attack, and sells security across the lifecycle for both language and vision models. The homepage frames the platform as covering applications, agents, and models, and the LLM page names prompt injection, adversarial manipulation, data leakage, hallucination, and toxic output as the threats it addresses. The buyer is the enterprise security team standing up AI in a core workflow.

Independent reporting corroborates the pain beyond vendor marketing. SecurityWeek describes DeepKeep as a model-agnostic, multi-layer platform that protects AI from research and development through deployment, covering risk assessment, detection, mitigation, and prevention. That account establishes the lifecycle risks as recognized problems rather than vendor speculation.

The company splits its problem statement across two model families. DeepKeep markets a language-model line and a separate computer-vision line, arguing that securing AI requires protecting image and multimodal systems alongside text. That dual framing is what distinguishes its problem statement from rivals that address language models alone. [s1, s2, s6]

DeepKeep ships a platform of capabilities that span build time and runtime. The AI firewall applies real-time detection and guardrails, automated red teaming runs adaptive robustness tests, model scanning performs static and dynamic supply-chain checks, an agent scanner adds runtime protection for agentic systems, and an AI Lens layer governs employee AI usage. The LLM line covers prompt injection, semantic attacks, data leakage, hallucination, and compliance across agentic AI and MCP connections.

The computer-vision line is the capability rivals are least set up to match. DeepKeep protects vision pipelines against adversarial attacks, and its researchers, including the founder, have published peer-reviewed work on physical adversarial attacks against object detectors and on detecting adversarial attacks on deepfake detectors. That research demonstrates the vision-security craft the product line sells.

The deployment model answers a data-exposure question a security buyer raises early. The founder states that DeepKeep offers cloud-agnostic, on-premises, and air-gapped deployment, so customer data can stay inside the customer environment, and the company recently added an upgraded PII guardrail for stricter data protection. The combination of LLM and computer-vision coverage in one vendor is the capability claim that separates it from single-modality rivals. [s2, s5, s3]

DeepKeep competes against both AI security specialists and the platforms consolidating the category. TrojAI pairs build-time red teaming with a runtime firewall, HiddenLayer runs red teaming and supply-chain security inside a broader platform, Adversa AI sells continuous red teaming, and Lakera shipped runtime guardrails before Check Point acquired it into its platform. The completed 2025 acquisitions of runtime AI security companies, Protect AI folded into Palo Alto's Prisma AIRS and Lakera into Check Point, moved overlapping LLM coverage inside larger suites.

DeepKeep's visible distinction is computer-vision and multimodal security. Where the runtime AI security peers lead with language-model defense, DeepKeep protects vision and multimodal models on top of LLMs, a combination grounded in the founder's adversarial-vision research and a European grant for multimodal protection. That coverage is the asset a bundled LLM-focused competitor cannot quickly reproduce.

The structural risk is who owns the buyer on the crowded side. Model providers can test and protect the LLMs built on their own platforms, and the vendors that bought runtime AI security rivals in 2025 can bundle a firewall and red teaming into deals an enterprise already signs. DeepKeep's vision niche is its neutrality pitch, while its LLM line is its exposure. [s3, s5, s16, s17]

DeepKeep's clearest external endorsements come from funders rather than named buyers. A $10 million seed led by the Canadian-Israeli VC Awz Ventures, placed through the Awz X-Seed Hub joint venture with the Israeli Ministry of Defense R&D directorate, and a EUR 2.5 million European Innovation Council grant for multimodal AI security give it institutional credibility. These are validation signals from investors and a public funder, not evidence of paid deployments.

Verifiable named-customer proof is thin. DeepKeep displays a Trusted By logo wall on its homepage naming firms such as EY, NTT Data, Macnica, and ST Engineering, and SecurityWeek reports it is already used by global enterprises in AI computing, finance, and security, but no buyer speaks publicly with an attributed reference or case study in the cited record. That gap between displayed logos and a named, quotable deployment is the signal a buyer demanding current references would probe first.

The motion is enterprise-direct and research-assisted. DeepKeep routes prospects to a book-demo flow, publishes original adversarial research and a steady blog, and recently launched Vibe AI Red Teaming that pairs human expertise with AI-driven execution. Disclosed customer references would be the signal that this attention has converted to deployments. [s1, s9, s6]

DeepKeep's credibility comes from a repeat-builder founder with an in-domain research record. Founder and CEO Rony Ohayon founded the live-video company LiveU and served as its CTO, ran the autonomous-driving connectivity company DriveU as CEO, and holds a PhD in communication systems engineering along with more than 30 registered patents. That career in video, vision, and autonomous systems is the background the company's computer-vision focus draws on.

The research record reinforces the team's standing. DeepKeep researchers, including the founder, have co-authored peer-reviewed adversarial-ML papers such as a study of physical black-box attacks on object detectors and a method for detecting adversarial attacks on deepfake detectors. This is a sustained in-domain publication pattern rather than a single covered event.

The wider leadership team is publicly identifiable. The about page lists CTO Yossi Altevet, VP of Research and Development Gad Sosa, a VP of Product, a VP of Marketing, and a VP of Sales for the Americas, which shows a built-out function set for a company of its size. [s15, s5, s4]

DeepKeep's trust posture leans on flexible deployment. The founder states that the platform runs cloud-agnostic, on-premises, and air-gapped, so an enterprise can keep customer data inside its own environment, which addresses the data-exposure question a security buyer raises first when a product inspects proprietary AI systems. The company also added an upgraded PII guardrail aimed at global compliance requirements.

The homepage now displays SOC 2, ISO 27001, ISO 9001, and GDPR badges, which signals a compliance posture a procurement team expects. Those badges are vendor-displayed claims rather than the downloadable third-party attestation reports a security review requests, so for a company selling into financial-services and regulated buyers, producing the underlying reports on request is the readiness item that would still surface. [s1, s8, s2]

Add analyzed competitors to compare them side by side with DeepKeep.

DeepKeep sells to the enterprise security team standing up AI in a core workflow. The company frames its platform as covering the applications, agents, and models an organization builds, and SecurityWeek reports it is already used by global enterprises in the AI computing, finance, and security industries. The buyer owns the AI deployment and answers for its safety, not a small team outsourcing the function.

The segment splits across two model families, and that split is the deliberate distinction. DeepKeep markets a language-model line and a separate computer-vision line, and the founder states that its expertise spans both computer vision and LLMs. Rivals in the cited record emphasize language-model protection, so the vision coverage widens the addressable set toward image and multimodal buyers.

The funding mix points at Europe without settling customer geography. The European Innovation Council grant ties the company to European public funding, and SecurityWeek places early use in the finance and security industries. The open question is whether the vision and multimodal segment is large enough to anchor revenue, or stays a specialty while the contested language-model side carries the load.

DeepKeep's claimed advantage is covering the full AI lifecycle across both model families in one platform. The capabilities span an AI firewall for real-time detection and guardrails, automated red teaming for robustness tests, model scanning for static and dynamic supply-chain checks, an agent scanner for runtime protection of agentic systems, and an AI Lens layer that governs internal AI usage. The LLM line names prompt injection, adversarial manipulation, and semantic attacks as the threats it stops.

CV and multimodal coverage appears to be DeepKeep's clearest differentiation in this source set. The company protects vision pipelines against adversarial attacks, and its research page lists a founder-co-authored deepfake-detector defense in TMLR 2024 alongside a physical adversarial-attack study on object detectors. That published research demonstrates the vision-security craft the product line sells rather than asserting it from marketing pages.

The durable technical asset is depth in adversarial ML, not a data advantage. The reasoning behind detection and red teaming is engineering and research a funded competitor can reproduce, and no fetched page names a non-public training corpus or a third-party accuracy benchmark. The breadth across LLM and vision is the verifiable differentiator, and the research record is what makes it credible.

DeepKeep's clearest external endorsements come from funders rather than named buyers. A $10 million seed led by Awz Ventures and a EUR 2.5 million European Innovation Council grant for multimodal AI security give it institutional credibility. These are validation signals from investors and a public funder, not evidence of paid deployments.

Named-customer proof stays thin. The homepage shows a Trusted By logo wall currently naming EY, NTT Data, Macnica, and ST Engineering, with Sumitomo Corporation and Toshiba on the June capture but not the live wall, and SecurityWeek reports use by global enterprises in finance and security, but no buyer speaks publicly with an attributed reference or case study in the fetched pages. That gap between displayed logos and a quotable deployment is what a buyer demanding current references would probe first.

The motion is enterprise-direct and research-assisted. DeepKeep routes prospects to a book-demo flow, publishes original adversarial research and a company blog, and recently launched a Vibe AI Red Teaming offering. Founder-fronted selling and the grant and seed backing fit a company at this stage, and while SecurityWeek reports enterprise use, an attributed customer reference remains the missing public signal.

DeepKeep does not publish pricing in fetched sources, so the charged unit and list price stay private. Every page routes to a book-demo flow rather than a self-serve plan or a pricing table. A vendor that hides prices this way usually targets large negotiated enterprise deals, which fits the global-enterprise buyer SecurityWeek describes.

The charged unit is not stated, and the platform's breadth makes several plausible. Coverage spans model scanning at build time, an inline firewall at runtime, and red teaming on demand, so DeepKeep could meter by models protected, by request volume through the firewall, or by platform subscription. None of these is confirmed publicly, which withholds the budget-anchoring signal some peers publish.

The inferable belief is that buyers pay for lifecycle AI assurance rather than a single feature. The platform framing around risk assessment, detection, mitigation, and prevention points to a suite sale, and confirming the meter and whether consumption is capped would require the sales conversation the hidden-price posture signals as the intended path.

DeepKeep delivers as software the customer configures and runs across the AI lifecycle. The AI firewall inspects every interaction as it happens and blocks or redacts violations before they cause harm, model scanning runs static and dynamic supply-chain checks, and automated red teaming runs adaptive robustness tests. The platform spans build time and runtime rather than a single insertion point.

The deployment model answers a data-exposure question a security buyer raises early. DeepKeep states that it supports cloud, on-premises, and air-gapped deployments, so an enterprise inspecting proprietary AI systems can keep its data inside its own perimeter. That flexibility matters for the finance and defense-adjacent buyers the company courts.

The operational profile is a tool the customer operates, not a managed service. Nothing in the fetched pages shows DeepKeep analysts running the platform on a customer's behalf or accepting hands-on responsibility for outcomes, and no published uptime or support SLA surfaces. The firewall can sit inline in the request flow, which raises the operational stakes of a misfire and is the readiness item a buyer would test.

DeepKeep displays a common enterprise attestation set for a tool that inspects privileged AI traffic. The homepage footer carries SOC 2, ISO 27001, and GDPR badges, confirmed by the served image filenames and the rendered footer. These signal a compliance posture a procurement team expects, but they are self-displayed badges rather than an inspectable third-party portal with downloadable reports.

Deployment flexibility carries part of the trust case. By offering cloud, on-premises, and air-gapped options, DeepKeep lets a buyer keep customer data inside its own environment, which answers the data-residency objection a security review raises when a product reads proprietary models and prompts. The firewall's inline blocking and redaction is framed as the runtime control that enforces the buyer's standards.

The attestations are enterprise-grade but table-stakes rather than a moat. Because the platform handles a customer's prompts, model artifacts, and outputs, a buyer should still resolve data-handling, retention, and the underlying audit reports in a formal review beyond the published badges, and producing those reports on request is the readiness item that would surface for a regulated buyer.

DeepKeep positions itself as a lifecycle platform rather than a point tool. It ties model scanning at build time, an inline firewall at runtime, red teaming, an agent scanner, and an AI Lens governance layer into one offering, so the platform claim rests on covering the whole AI application ecosystem rather than a single control. The founder argues the real risks emerge within the full application ecosystem, not just the models themselves.

The ecosystem bet extends toward agents and multimodal systems. The founder describes evolving the platform to secure custom AI applications and agents, and the next step in which agents interact with each other across domains, while the vision line already protects image and multimodal pipelines. That forward coverage is a roadmap claim more than a shipped network of integrations in the fetched pages.

The exposure is that platforms are moving to own the buyer on the runtime side. Palo Alto Networks completed its purchase of Protect AI into Prisma AIRS in July 2025, and Check Point completed its acquisition of Lakera in October 2025. With both deals closed, the incumbents can fold an LLM firewall and red teaming into suites an enterprise already buys, so the breadth DeepKeep assembles competes against bundles rather than standalone tools. CV and multimodal coverage is the differentiation DeepKeep itself emphasizes, though the cited pages do not map the rival bundles' vision coverage.

DeepKeep's credibility comes from a repeat-builder founder with an in-domain research record. Founder and chief executive Rony Ohayon holds a PhD in communication systems engineering and an MBA, names more than 30 registered patents, founded the live-video company LiveU, and ran the autonomous-vehicle teleoperation company DriveU as chief executive. That career in video, vision, and connectivity is the background the computer-vision focus draws on.

The research record reinforces the team's standing. Ohayon co-authors adversarial-ML work the company publishes, including a deepfake-detector defense in TMLR 2024, and the company frames its expertise as spanning both computer vision and LLMs. This is a sustained in-domain publication pattern rather than a single covered event.

The depth below the founder is the open question in fetched sources. The public record this analysis establishes Ohayon's track record and the company's research output clearly, but individual prior builds for the wider leadership bench do not surface in the press fetched here. The verifiable strength is the founder's pedigree and the institutional backing it has drawn.