Giskard

Giskard is an AI security and red teaming platform designed to secure and evaluate LLM agents. The platform provides continuous AI red teaming, vulnerability scanning, quality evaluation, and remediation support to help teams detect failures before deployment. It operates both as an open-source library and an enterprise-grade platform offering deployment options, data residency guarantees, and compliance features.

• Continuous AI red teaming utilizing over 50 automated adversarial probes, including multi-turn attacks
• LLM vulnerability scanning to identify prompt injection, data disclosure, sycophancy, and inappropriate content
• RAG quality evaluation and domain-specific generation of quality evaluation datasets
• Security validation for tool calling and customizable scenario-based generation
• Remediation management including qualification of findings, workflow ticket creation, and re-running tests
• Enterprise security controls featuring Role-Based Access Control, audit trails, SSIs, and a zero-training data policy
• Deployment options supporting hybrid, on-premise, private cloud, and SaaS configurations with EU or US data residency

Pros

• Provides structured, severity-ranked vulnerability reports with explicit go or no-go deployment recommendations
• Covers both security threats like prompt injection and quality failures such as hallucinations, contradictions, and inappropriate denials
• Includes compliance readiness features such as native GDPR adherence alongside SOC 2 Type II and HIPAA compliance
• Integrates team workflows by prioritizing findings and connecting with CI/CD pipelines and identity providers

Cons

• Exact pricing is not published and requires booking a demo or contacting sales for the Enterprise tier
• Open-source and free tiers are limited to basic LLM vulnerability scans and basic RAG evaluation reports with community support
• Advanced enterprise features require moving away from the free local deployment model to the paid tier

Giskard fits engineering, security, and AI product teams in sectors like finance, healthcare, and manufacturing that need to evaluate and secure production-ready LLM agents against adversarial attacks and quality drift. It is well-suited for organizations requiring regulatory compliance, data residency isolation, and automated CI/CD security validation. It is a poor fit for hobbyists or developers who require fully transparent self-hosted enterprise capabilities without contacting sales, or those needing out-of-the-box fixed public pricing.

The pricing model includes a free tier for solo LLM experiments and an Enterprise tier for production LLM deployments where users must book a demo or contact the company for pricing details.

Unlike generic testing scripts or basic evaluation tools, Giskard combines over 50 automated adversarial probes with human-in-the-loop review interfaces and end-to-end remediation management. Rather than stopping at a static vulnerability report, the platform assists in qualifying findings, opening tickets, and re-running tests until fixes are confirmed.

Integrates with CI/CD platforms, Identity Providers for SSO, and external evaluation or monitoring platforms via dataset interoperability.

Giskard is worth trying for development and security teams seeking to automate LLM red teaming and safeguard production AI agents against sophisticated prompt injections and hallucinations. The free open-source library provides an accessible starting point for basic vulnerability scans and RAG evaluations. However, production teams will need to contact the company for enterprise pricing and deployment options to unlock advanced adversarial probes, hybrid infrastructure, and compliance controls.