Giskard
FreemiumAI Red Teaming & LLM Security Platform
Giskard is an AI red teaming and LLM security platform that helps organizations detect vulnerabilities and quality failures in conversational AI agents before and after deployment. It combines an open-source Python library for local testing with the Giskard Hub, an enterprise platform for continuous red teaming, team collaboration, and automated evaluation workflows. The platform operates as a black-box testing tool, requiring only an API endpoint to probe agents for prompt injection, data disclosure, sycophancy, hallucinations, and other safety risks.
What is it
An AI red teaming and LLM security platform that detects vulnerabilities and quality failures in conversational AI agents through automated adversarial testing.
What it can do
Run automated vulnerability scans, generate adversarial test cases, evaluate RAG groundedness and correctness, schedule continuous evaluations, and produce go/no-go deployment reports.
Who is it for
AI engineering teams, security teams, product managers, and compliance leaders building or deploying conversational AI agents in regulated enterprises.
Key Features
Automated Vulnerability Scanning — Security & Quality Probes
Automatically scan conversational AI agents for security vulnerabilities such as prompt injection, data disclosure, sycophancy, and inappropriate content, as well as quality failures such as hallucinations, contradictions, omissions, and inappropriate denials. The scanner ranks findings by severity and maps them to recognized taxonomies including the OWASP LLM Top 10.
Continuous Red Teaming — Threat Detection
Schedule recurring evaluations on a daily, weekly, or monthly basis to continuously detect new vulnerabilities that emerge after deployment. The Hub generates sophisticated attack scenarios and converts discovered issues into permanent test suites to prevent regressions.
RAG Evaluation Toolkit — Groundedness & Correctness
Evaluate retrieval-augmented generation systems using fine-grained metrics such as correctness, groundedness, and semantic similarity. Generate synthetic test datasets from knowledge bases and compare evaluation runs over time to track quality trends.
Black-Box Agent Testing — API-First Integration
Connect to any conversational AI agent through a standard API endpoint without needing access to internal components such as foundation models, vector databases, or orchestration logic. The agent only needs to accept messages and return responses in a documented JSON format.
Use Cases
Pre-Deploy AI Agent Security Audits
Run comprehensive vulnerability scans against customer-facing chatbots and copilots before release to catch prompt injection, data leakage, and harmful outputs that could damage brand reputation or expose sensitive data.
Continuous Post-Deploy Monitoring
Schedule automated evaluations against production agents to detect new vulnerabilities that emerge after launch, with alerts and historical dashboards showing success rates over time.
RAG System Quality Validation
Validate retrieval-augmented generation applications by checking answer groundedness, factual correctness, and semantic relevance against imported knowledge bases.
Enterprise Compliance Certification
Generate structured go/no-go reports, maintain audit trails, and demonstrate adherence to GDPR, SOC 2 Type II, and HIPAA requirements when deploying AI in regulated industries such as finance, healthcare, and public sector.
Pricing plans
Frequently Asked Questions
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