# Giskard Giskard is an AI security and evaluation platform for testing conversational LLM agents before and after deployment. It combines automated red teaming, quality evaluation, runtime guardrails, and remediation workflows for teams responsible for reliable AI systems. - Findings24: - Site: - Preços: Freemium - Categorias: Assistentes de testes com IA, Avaliação de LLMs ## O que é Giskard? Giskard is an AI security and evaluation platform for conversational LLM agents. It tests agents as black boxes through an API endpoint, without requiring access to their foundation model, vector database, or other internal components. The platform is designed for both pre-deployment assessment and continuous testing after release, covering security risks and response-quality problems. Its security capabilities include automated adversarial testing, multi-turn attacks, prompt-injection testing, data-disclosure checks, harmful-content and stereotype detection, and tool-calling validation. The platform also supports scenario-based tests that model specific personas and business logic. Giskard Guards adds a runtime control layer that evaluates prompts, tool calls, parameters, permissions, and responses against context-aware, policy-driven rules. For quality evaluation, Giskard can generate domain-specific question-and-expectation pairs from a knowledge base, apply RAG quality metrics, and support custom evaluation metrics defined around business requirements. Human reviewers can inspect, debug, annotate, and prioritize results. Dataset import, tags, task assignment, audit trails, scheduled alerts, and CI/CD support connect testing with ongoing engineering and review processes. After an assessment, the platform provides a structured report with a deployment verdict, ranked security findings, and functional scenarios tested against the team’s requirements. Giskard describes a remediation workflow that includes qualifying findings, discussing severity, prioritizing actions, opening tickets, and rerunning tests to confirm fixes. Deployment options described on the site include SaaS, private cloud, on-premises, and air-gapped environments, with enterprise controls for access, data residency, and support. ## O que Giskard pode fazer? ### Automated AI red teaming Runs adversarial tests across security and quality threats, including prompt injection, hallucination, legal and financial risks, harmful content, personal-information disclosure, and other vulnerability classes. ### Agentic and multi-turn testing Tests conversational flows and agent behavior across multiple turns, including tool calls, parameters, user permissions, and complex scenarios rather than evaluating isolated text alone. ### Context-aware policy guardrails Giskard Guards applies natural-language or policy-as-code rules to prompts, tools, and responses, with custom policies and coverage for OWASP LLM risks and selected EU AI Act topics. ### LLM and RAG evaluation Generates domain-specific evaluation data, checks retrieval-augmented responses with fine-grained quality metrics, and supports custom metrics tailored to business logic. ### Human review and evaluation operations Provides interfaces for reviewing, debugging, and annotating results, along with tags, task assignment, versioning, audit trails, and scheduled email alerts for critical failures or newly detected vulnerabilities. ### Deployment and security controls Offers SaaS, private-cloud, on-premises, and air-gapped deployment options described for enterprise use, with data-residency choices, role-based access controls, audit trails, encryption, and a stated zero-training policy. ## Casos de uso ### Pre-production release assessment Product and AI teams can test a conversational agent against security vulnerabilities and functional requirements before deployment, then use the structured report and deployment verdict to decide whether to release or remediate. ### Continuous monitoring after launch Teams can rerun evaluations and adversarial tests as agents, prompts, policies, or connected tools change, helping identify new vulnerabilities and regressions after deployment. ### RAG and knowledge-grounded assistant evaluation Teams building assistants over internal knowledge bases can generate representative question-and-expectation pairs and assess correctness, grounding, omissions, contradictions, and hallucinations. ### Governed agent runtime protection Security, compliance, and platform teams can use Giskard Guards to turn organizational or regulatory requirements into enforceable policies that inspect agent interactions and tool activity in production environments. ### Collaborative remediation and audit Engineering, security, and review teams can qualify findings, assign actions, annotate test cases, track versions, maintain audit logs, and rerun tests to verify that fixes address the original issue. ## Perguntas frequentes ### Should Giskard be used before or after deployment? Both. The site describes pre-deployment testing for production-readiness KPIs and post-deployment continuous testing to detect vulnerabilities that emerge as the application operates or changes. ### What type of agents does Giskard support? The Giskard Hub is described as supporting conversational AI agents in text-to-text mode. It uses black-box testing, so the agent’s internal foundation model and vector database do not need to be exposed; the complete agent must be accessible through an API endpoint. ### What does an assessment produce? Giskard describes a structured report with a clear deploy-or-fix verdict, security vulnerabilities ranked by criticality, and functional scenarios tested against the team’s requirements. It may also provide a Giskard Label when an agent passes. ### Can teams define their own rules and evaluation criteria? Yes. The platform supports custom evaluation metrics and scenario-based tests. Giskard Guards supports custom guidelines in natural language and policy-as-code rules, including OPA/Rego as listed on the Guards page. ### What deployment options are available? The pricing and product pages describe SaaS, private-cloud, on-premises, and air-gapped deployment options for enterprise use. The site also describes regional data residency and isolation choices, including EU or US processing options on the home page. ## Informações rápidas - **Category**: AI security and LLM evaluation platform - **Primary users**: AI, security, engineering, compliance, and review teams - **Testing model**: Black-box testing of conversational agents through an API endpoint - **Core products**: Continuous red teaming, LLM evaluation, and Giskard Guards - **Available plans**: Free solo tier with open-source library and local deployment; enterprise plan with production deployment and advanced security features - **Website**: giskard.ai