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.
Giskard, founded in 2021, sells software that runs simulated attacks on AI agents to detect failures such as leaked data and unsupported answers. It builds that software for banks, insurers, and other regulated sectors. Giskard Guards, launched in 2026, blocks unsafe actions by live agents. Giskard names more than a dozen customers, among them BNP Paribas, AXA, Michelin, and Google DeepMind. Elaia led its €1.5 million first round, and the European Commission and Bpifrance, France’s public investment bank, have also funded it. BPCE, one of France’s largest banking groups, says it secured and launched a customer chatbot after Giskard found flaws in it. Giskard tests three of BPCE’s AI assistants continuously, so Giskard is part of the bank’s release process and harder to replace there.
| Description | Giskard sells red teaming, evaluation, and runtime guardrails for AI agents, testing them for security and quality failures and blocking unsafe actions in production. It offers the Giskard Hub enterprise platform, the Giskard Guards guardrail product, and a free open-source library. | [f1] |
|---|---|---|
| Founded | 2021 | [f2] |
| HQ | Paris, France | [f3] |
| Latest funding | 3M EUR Bpifrance research grant (2024) | [f2] |
| Deployment | SaaS, Self-hosted | [f4] |
| Compliance | SOC 2 Type 2 | [f4] |
| Product | What it does |
|---|---|
| Giskard | AI red teaming, evaluation, and runtime guardrail platform for LLM agents, sold as the Giskard Hub enterprise tier, Giskard Guards, and a free open-source library. |
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. |
Giskard is an AI testing platform that red-teams LLMs and ML models with adversarial probes for prompt injection, hallucination, and sensitive-information disclosure. It is mapped to the AI Defense Matrix. [f5]
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 | Giskard names a clear buyer, enterprises putting AI agents into banking, insurance, and other regulated work, and lists the failures it tests for, from prompt injection to hallucination. Giskard quantifies the pain through its own research, which TechCrunch covered as Giskard’s findings rather than independent corroboration. [s1, s4, s14, s15] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | Giskard documents its products in detail, from a documentation page comparing the library and Giskard Hub feature by feature to product pages describing adaptive multi-turn attacks and runtime detectors. The scanning code is open to inspection in an Apache-licensed GitHub repository with 5,889 stars, and the GitHub Security Lab reported a vulnerability in it, an instance of outside scrutiny of that code. [s8, s5, s6, s13, s20] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | Enterprises adopting AI agents that act on business systems create the testing need, and a European Innovation Council project record from 2023 describes Giskard’s work as preparing organisations for the then-forthcoming EU AI Act, a regulatory enabler. The buyer-side signals from the last year are Giskard’s own BPCE case study and a Tech.eu interview with its co-CEO, indirect signals rather than several forms of independent evidence of demand. [s18, s10, s22] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 | On its about page, Giskard says its co-founders combine 25 years of experience building AI systems at Dataiku and Thales (s2). CTO Matteo Dora and Giskard researchers co-authored the Phare preprint in May 2025 and the StereoTales preprint in May 2026 (s16, s17). That is relevant, continuing research work, but no prior exit or independently recognized standing appears in the cited sources. [s2, s16, s17] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 3/5 | Giskard names its customers on its homepage, including L’Oréal, AXA, Société Générale, BPCE and BNP Paribas (s1). In its BPCE case study, Giskard reports that BPCE now runs three AI assistants through Giskard (s10). Tech.eu repeats a similar list of users, but in an interview with Giskard’s co-founder (s22). The deployments and testimonials are Giskard’s own publications, so Giskard has named customers without independent corroboration of scale. [s1, s10, s22] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | Giskard reports a €1.5 million first round led by Elaia in 2022, followed by public funding from the European Commission’s EIC Accelerator in 2023 and a Bpifrance research grant in 2024, funding broadly proportional to its stage. It ships an enterprise platform, a runtime guardrail product, and an open-source library. The reviewed sources include no revenue or margin figure, so its efficiency is unconfirmed. [s2, s12, s18, s3] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 3/5 | Buyers can place Giskard in AI red teaming and LLM evaluation, a category HiddenLayer, Adversa AI, and Mindgard also market under that name on their own sites. The reviewed sources include no analyst placement of Giskard in that category. [s5, s23, s24, s25, s4] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 3/5 | Giskard Hub adds friction to absorption through the golden evaluation datasets, scheduled runs, and historical performance data a customer accumulates in it, and Guards adds versionable policies built from each customer’s regulatory requirements. Large security vendors can still buy their way into the category, as Check Point did with Lakera in 2025, and the cited record documents no structural moat that bundling could not eventually replicate. [s4, s8, s6, s21] |
Giskard sells testing and runtime protection for the AI agents and chatbots that enterprises put in front of customers and staff. Its homepage lists the failures it looks for, including prompt injection, data disclosure, hallucinations, and omissions. Giskard describes its Hub as built for banking, insurance, and other regulated sectors where AI failures carry compliance and reputational risks.
Giskard quantifies the problem with its own research. TechCrunch covered Giskard’s finding that requests for shorter answers can reduce a model’s factuality. The Decoder described declines in hallucination resistance of up to 20 percent.
A European Union record describes the same problem in regulatory terms. The CORDIS entry for Giskard’s European Innovation Council project describes the work as helping organisations prepare for the then-forthcoming EU AI Act. CORDIS gives the project’s dates as September 2023 to August 2025 and marks it closed. [s1, s4, s14, s15, s18]
Giskard sells products for testing AI agents before and after deployment and, since 2026, for protecting them at runtime. Giskard Open Source is a free Python library for individual developers and researchers, while Giskard Hub is the enterprise platform with team features. Giskard’s documentation describes the library’s vulnerability detection as basic coverage, and Giskard reserves continuous red teaming, scheduled runs, and shared datasets for the Hub.
Giskard Hub runs continuous red teaming with an AI red teamer that adapts its attacks to the agent’s replies. Giskard says it draws on the customer’s documents and knowledge bases to target attacks, and on OWASP and open-source security datasets for coverage. The pricing page lists more than 50 automated adversarial probes, including multi-turn attacks, plus tool-calling security validation. The Hub also adds vulnerabilities its scans detect to the customer’s reusable test datasets.
Giskard Guards is a runtime control layer with detectors for prompts, tools, and responses. Giskard says Guards inspects tool calls, parameters, user permissions, and multi-step reasoning, then blocks unsafe actions before they reach production. Customers can deploy Guards in their own infrastructure, and Giskard converts regulatory requirements into versionable, enforceable policies for it. [s7, s8, s5, s3, s4, s6, s22]
Giskard sells into the AI red-teaming category alongside independent specialists and broader security platforms. HiddenLayer lists red teaming inside a broader AI security platform that also covers supply chain and runtime security, and Adversa AI and Mindgard each market AI red teaming on their own sites. Giskard now sells runtime protection, Giskard Guards, beside its testing products.
Giskard pairs security testing with quality testing for business teams. Its homepage lists quality failures such as hallucinations and omissions beside attacks such as prompt injection and data disclosure. Giskard Hub lets business experts add policies, establish ground truths, and qualify failures, and it exports reports for compliance, risk, and product teams.
Large security vendors can add this capability by acquisition. Check Point completed its purchase of Lakera, a company focused on protecting generative AI applications, in October 2025, according to Check Point’s annual report. In the same report, Check Point says the deal would let it expand its product portfolio. [s23, s24, s25, s6, s1, s4, s21]
Giskard publishes a long list of named customers on its own site. The homepage describes L’Oréal, AXA, Société Générale, BPCE, Decathlon, ACCOR, Michelin, BNP Paribas, Doctolib, Google DeepMind, Crédit Agricole, ByMyCar, Groupe RATP, and BoursoBank as Giskard customers. Giskard’s trust center lists Irish Life alongside BNP Paribas and Michelin, and testimonials from BNP Paribas and Michelin staff appear on the homepage.
A published case study describes a production deployment at BPCE, which the study describes as one of France’s largest banking groups. Giskard assigned two security researchers to attack BPCE’s customer chatbot for several weeks before launch. BPCE now runs three AI assistants through Giskard, and the case study includes quotes from BPCE staff by name and role.
Tech.eu repeated several of these names in May 2026. Its article, built on an interview with co-CEO Jean-Marie John-Mathews, states that Mistral, Google DeepMind, BNP Paribas, AXA, and Doctolib used Giskard’s testing platform before the Guards launch. Giskard also lists Google Cloud, AWS, Microsoft Azure, Databricks, Accenture, KPMG, PwC, and EY among its partners. [s1, s9, s10, s22, s2]
Giskard’s leaders pair enterprise AI engineering with a published research program. Co-founders Alex Combessie and Jean-Marie John-Mathews lead as co-CEOs, and the company says they combine 25 years of building AI systems at Dataiku and Thales. Chief technology officer Matteo Dora holds a PhD, and the team page lists several PhD AI researchers.
Giskard’s researchers have published more than one study in the company’s own product domain. Dora and other Giskard researchers co-authored the Phare safety paper on arXiv in May 2025 and the StereoTales stereotype study in May 2026. Giskard announced that NeurIPS 2026 accepted StereoTales.
European funders and the technology press have backed or covered the team’s work. The European Innovation Council funded a Giskard project that ran from 2023 to 2025, and TechCrunch covered Giskard’s research on hallucinations. Giskard’s investors include Elaia, Bessemer, and Hugging Face’s CTO. [s2, s16, s17, s26, s18, s14, s15]
Giskard runs a trust center that lists a SOC 2 Type 2 report and GDPR documentation, with HIPAA marked in progress. Its homepage and pricing page state HIPAA compliance, so a buyer would need to confirm the current HIPAA status with Giskard. The trust center listed 167 controls in October 2026, in areas such as change management, host hardening, and penetration testing.
Giskard offers deployment options for buyers that keep data in their own environment. Its options include on-premise, private cloud, and SaaS deployment, data residency and isolation, single sign-on, role-based access, and versioning with audit trails. It offers on-premise Hub installs for public-sector, defense, and other sensitive workloads.
Public vulnerability records cover Giskard’s open-source code. The National Vulnerability Database lists a remote-code-execution flaw affecting versions before 0.3.4 and 1.0.2b1, and a denial-of-service flaw that the GitHub Security Lab found, affecting versions before 2.15.5. A security team reviewing the library would weigh both records. [s9, s1, s3, s19, s20]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| Lakera | competes with | Competes for buyers protecting generative AI applications. | |
| HiddenLayer | competes with | Competes for the AI red-teaming buyer, and HiddenLayer markets red teaming within a broader AI security platform. | |
| Adversa AI | competes with | Competes for the same AI red-teaming buyer, and Adversa AI markets an AI red teaming platform on its site. | |
| Mindgard | competes with | Competes for the continuous AI red-teaming buyer, and Mindgard markets AI red teaming on its site. | |
| OpenAI | adjacent | Adjacent as a model provider whose platform customers use to build the agents Giskard tests. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| LangSmith | adjacent | Adjacent on the developer-tooling side, and Giskard’s own FAQ contrasts its product with LLM platforms like LangSmith. |
Add analyzed competitors to compare them side by side with Giskard.
A closer look at the company's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
press the advantage
Giskard builds AI agent testing for banking, insurance, and other regulated sectors, and names BNP Paribas, BPCE, and AXA as customers. It adds an expert assessment and remediation service. Customers accumulate reference test datasets and performance history in Giskard Hub, which adds friction to leaving. Giskard also advertises dataset interoperability with other evaluation platforms, which lowers that friction. The company keeps a private hold-out dataset for its Phare benchmark and publishes research on the failures it tests for. Customers who deploy Giskard Guards, launched in 2026, run a Giskard product that checks their agents’ live traffic.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 2/3 | Giskard sells software plus an expert assessment service, including a deploy-or-fix report after each assessment and a remediation process in which its team qualifies findings and re-runs tests. At BPCE, two Giskard security researchers attacked a chatbot for several weeks, so Giskard blends code and expertise in delivery while customers also run Giskard Hub and the library themselves. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Giskard Hub accumulates the friction of data history, integrations, and learned workflows: each customer’s golden evaluation datasets, scheduled runs, historical performance data, CI/CD integration, and single sign-on. The cited record does not size a migration or document non-portable state. Giskard advertises dataset interoperability with other evaluation platforms. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | Giskard’s trust center lists a SOC 2 Type 2 report and GDPR documentation, with HIPAA in progress, attestations a funded competitor can obtain through ordinary enterprise preparation. The reviewed sources document no regulator-mediated authorization for Giskard’s products that would block a replacement. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | Giskard’s products use machine learning at their core, including an AI red teamer that adapts multi-turn attacks to an agent’s replies and Guards detectors that assess intent and context at runtime, some almost instant according to Tech.eu. Giskard’s PhD AI researchers build these products and publish safety benchmarks. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 3/3 | Giskard Hub is built and sold for banking, insurance, and other regulated sectors, and Giskard names BNP Paribas, Société Générale, Crédit Agricole, BPCE, and AXA among its customers, with a case study on BPCE’s banking chatbot. Giskard also offers on-premise installs for public-sector and defense workloads, which places its buyer in the regulated-enterprise and government class. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 2/3 | Giskard Hub and the open-source library test agents from outside their live traffic, as a platform with application features. Giskard Guards is documented as a runtime control layer that blocks unsafe actions. It launched in 2026, and the cited record documents no deployment of it on real agent traffic, so this runtime product does not yet make the company infrastructure. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 2/3 | Giskard uses a dedicated private hold-out dataset to evaluate models on its Phare benchmark while releasing a portion of samples publicly, and it applies internal prompt templates it built from work with clients. Giskard holds both assets itself rather than in per-tenant custody, and neither is shown public, though a funded rival could build comparable ones with time and effort. |
Giskard sells to enterprises putting AI agents in front of customers, and it describes its Hub as built for banking, insurance, and other regulated sectors. Its homepage describes BNP Paribas, Société Générale, Crédit Agricole, BPCE, AXA, L’Oréal, and Michelin as customers. Giskard’s site also has industry pages for finance, healthcare, and manufacturing.
Inside those accounts, Giskard sells to security, AI, and business teams. Giskard’s trust center describes AI and cybersecurity teams using its tools. Giskard Hub lets business experts add policies and qualify failures, and it exports reports for compliance, risk, and product teams.
Giskard aims the free library at a different segment, individual developers and researchers, according to its documentation. Giskard reserves continuous red teamingand shared datasets for Giskard Hub, which it positions for teams.
Giskard’s core capability is generating attacks and test cases automatically for each customer’s agent. Its AI red teamer adapts multi-turn attacks to the agent’s replies, and Giskard says it uses the customer’s documents and knowledge bases to target attacks to the use case. Giskard Hub then adds detected vulnerabilities to reusable test datasets for checking whether earlier failures recur.
Giskard Guards extends detection to live agent traffic. Giskard describes Guards as a runtime control layer with detectors for prompts, tools, and responses. According to a Tech.eu article, some detectors are almost instant while others are more contextual and deeper. Giskard says Guards inspects tool calls and user permissions, so it can evaluate an agent’s actions as well as its text.
Giskard has published research on the failures its products target. It built the Phare benchmark with Google DeepMind as research partner, and the Phare paper on arXiv describes an evaluation of 17 leading LLMs. Giskard researchers published the StereoTales study of social bias in model-generated stories in 2026, covering more than 650,000 stories from 23 models. Giskard released the StereoTales code and dataset, and its scanning library is open source under Apache 2.0.
Giskard sells directly to enterprises and offers AI assessments. It routes Enterprise-tier buyers to a demo on its pricing page. The about page lists a head of sales and partnerships and a customer success manager.
Giskard publishes its customer references on its own site. It names more than a dozen customers on its homepage and publishes a BPCE case study in which bank staff describe launching a customer chatbot after Giskard’s red teaming. A Tech.eu article built on an interview with co-CEO Jean-Marie John-Mathews repeated several of those names in May 2026.
Giskard extends its reach through partners and a free offer. The about page lists Google Cloud, AWS, Microsoft Azure, Databricks, Accenture, KPMG, PwC, and EY among its partners. Giskard also offers startups and small teams limited free access to its Agentic Red Teamer.
Giskard’s pricing page lists two tiers and no price figures. The free tier offers the open-source library with local deployment, a basic vulnerability scan using adversarial techniques from 2024, and community support. For production deployments, Giskard routes Enterprise-tier buyers to a demo.
Giskard bundles security testing, evaluation, operations, and support features into the Enterprise tier. It lists more than 50 automated adversarial probes, tool-calling security validation, single sign-on, CI/CD integration, hybrid deployment, and dedicated support with SLAs. Services include onboarding with a customer success manager, consulting on agent corrections and custom guardrails, and custom audit reports.
Giskard prices the Hub above a free library that covers basic detection. Giskard’s documentation describes the library’s vulnerability detection as basic coverage, and Giskard reserves continuous red teaming, scheduled runs, and team features for the Hub. Giskard therefore charges for stronger detection and the team workflow around it.
Giskard delivers software alongside a human-led assessment service. After each assessment, the customer receives a report with a deploy-or-fix verdict, vulnerabilities ranked by criticality, and a Giskard Label if the agent passes. Giskard says its team then qualifies each finding, opens tickets in the customer’s workflow, and re-runs tests until each fix is confirmed.
The BPCE case study describes that service in practice. Giskard assigned two security researchers who attacked the bank’s customer chatbot for several weeks before launch. Giskard also pushes newly discovered attack techniques into BPCE’s Hub, according to the case study.
Giskard offers deployment options for buyers that keep data in their own environment. It offers on-premise, private cloud, and SaaS deployment for the Hub, and customers can run Guards in their own infrastructure. Giskard includes dedicated support with SLAs in the Enterprise tier and offers community support for the library.
Giskard runs a trust center that lists a SOC 2 Type 2 report, GDPR documentation, and HIPAA, which the trust center marks in progress. Giskard’s homepage and pricing page both state HIPAA compliance, so a buyer would need to confirm the current HIPAA status with Giskard. The trust center listed 167 controls in October 2026.
Giskard lists product controls for the customer data it handles during testing. They include end-to-end encryption at rest and in transit, data residency and isolation, a zero-training policy, role-based access, and audit trails. On-premise installs keep data inside a customer’s environment for public-sector, defense, and other sensitive workloads.
Public vulnerability records cover Giskard’s open-source code. The National Vulnerability Database lists a remote-code-execution flaw affecting versions before 0.3.4 and 1.0.2b1, and a denial-of-service flaw that the GitHub Security Lab found, affecting versions before 2.15.5. A security team reviewing the library would weigh those records against the inspectability of open code.
Giskard Hub tests agents through their API endpoints. Giskard describes the Hub as a black-box testing tool that needs only the agent’s API endpoint, without access to its foundation models or vector database.
With Giskard Guards, Giskard now sells a product that inspects an agent’s live traffic and blocks unsafe actions. Giskard describes Guards as a runtime control layer that blocks unsafe actions before they reach production, deployed in the customer’s infrastructure. Tech.eu listed users of Giskard’s testing platform before the Guards launch.
Giskard lists major clouds and consultancies as partners. The about page lists Google Cloud, AWS, Microsoft Azure, NVIDIA Inception, Databricks, Mistral AI, Accenture, KPMG, PwC, and EY among its partners. Giskard’s pricing page also lists dataset interoperability with other evaluation and monitoring platforms, which lowers the cost of moving test data elsewhere.
Giskard is led by co-founders and co-CEOs Alex Combessie and Jean-Marie John-Mathews, with Matteo Dora as chief technology officer. The company says the co-founders combine 25 years of building AI systems at Dataiku and Thales. Its team page lists several PhD AI researchers alongside sales and customer-success staff.
The research team publishes in Giskard’s product domain. Dora and other Giskard researchers co-authored the Phare safety paper in 2025 and the StereoTales study in 2026, both on arXiv, and Giskard announced that NeurIPS 2026 accepted StereoTales. The European Innovation Council funded a Giskard project that ran from September 2023 to August 2025.
Giskard’s backers include Elaia, which led a €1.5 million first round in 2022, and Bessemer. Angel investors listed on its site include Julien Chaumond, CTO of Hugging Face. Giskard also won a €3 million Bpifrance research grant in 2024 for a consortium that included Mistral AI.
| Id | Source | Tier | Accessed |
|---|---|---|---|
| f1 | Giskard Trust Center | official | 2026-10-13 |
| f2 | Giskard about page | official | 2026-10-13 |
| f3 | TechCrunch: Asking chatbots for short answers can increase hallucinations, study finds | press | 2026-10-13 |
| f4 | AI Defense Matrix Catalog entry | other | 2026-06-13 |
| f5 | AI Defense Matrix Catalog mapping | other | 2026-10-13 |
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