About
Giskard is a continuous AI red teaming and LLM evaluation platform built for enterprise teams that need to secure and quality-test AI agents before and after deployment. Rather than relying on reactive monitoring, Giskard proactively generates sophisticated adversarial attack scenarios—covering prompt injection, data disclosure, inappropriate content generation, sycophancy attacks, hallucinations, omissions, and contradictions—to expose vulnerabilities that manual audits and standard network-layer security tools routinely miss. The platform delivers the largest coverage of both security and quality vulnerabilities, with high domain specificity, in a single automated scan. Detected issues are automatically converted into reproducible test suites that continuously enrich a golden dataset and prevent regression after each model update. Tests can be triggered programmatically via a Python SDK or scheduled through the web UI. Giskard's visual Human-in-the-Loop dashboards let business, engineering, and security teams collaboratively review, customize, and approve tests using a shared language. For compliance-sensitive organizations, Giskard offers end-to-end encryption, Role-Based Access Control (RBAC), audit trails, Identity Provider integration, EU/US data residency, a zero-training data policy, and certifications including SOC 2 Type II, HIPAA, and GDPR. It is trusted by enterprise AI leaders across industries including manufacturing (Michelin) and finance.
Key Features
- Continuous Automated Red Teaming: Automatically generates and runs sophisticated adversarial attack scenarios—including prompt injection, sycophancy attacks, and data disclosure—whenever new threats emerge, without manual effort.
- Security & Quality Vulnerability Coverage: Detects both security risks (data leakage, jailbreaks) and quality failures (hallucinations, omissions, contradictions, inappropriate denials) in a single unified scan with high domain specificity.
- Regression Prevention via Reproducible Test Suites: Automatically converts discovered vulnerabilities into permanent, reproducible test cases that enrich a golden dataset and prevent regressions after every model update or prompt change.
- Human-in-the-Loop Collaborative Dashboard: Visual interface that allows business, engineering, and security teams to jointly review, customize, and approve AI tests, creating a shared language around AI quality and safety.
- Enterprise-Grade Security & Compliance: Offers EU/US data residency, RBAC, audit trails, IdP integration, end-to-end encryption, a zero-training policy, and certifications including SOC 2 Type II, HIPAA, and GDPR.
Use Cases
- Red-teaming a customer-facing AI chatbot to detect prompt injection vulnerabilities before public release.
- Running automated regression tests on an LLM pipeline after every model update to ensure no new hallucinations or safety failures are introduced.
- Aligning security, engineering, and product teams around a common AI quality dashboard for collaborative review and sign-off on agent behavior.
- Ensuring compliance with GDPR and HIPAA by auditing AI agent outputs for data disclosure risks in regulated industries like healthcare or finance.
- Building a continuously enriched golden test dataset by converting discovered AI vulnerabilities into reproducible test cases over time.
Pros
- Proactive Rather Than Reactive: Catches AI failures during development rather than after deployment, reducing reputational and compliance risk before users are impacted.
- Broad Vulnerability Coverage: One of the most comprehensive tools covering both LLM security attacks and output quality issues, going far beyond typical network-layer security scanners.
- Team Alignment Tool: Bridges the gap between business, engineering, and security teams by giving all stakeholders a shared, visual interface for AI testing and approval workflows.
- Strong Enterprise Compliance: Native GDPR, SOC 2 Type II, and HIPAA support with data residency options makes it suitable for regulated industries like finance and healthcare.
Cons
- Primarily Enterprise-Focused: Pricing and feature depth are geared toward enterprise teams; smaller startups or solo developers may find it over-engineered or cost-prohibitive at scale.
- Learning Curve for Full Setup: Integrating the Python SDK, configuring RBAC, and building out a robust test pipeline requires meaningful engineering investment upfront.
- Limited Transparency on Free Tier: Public documentation does not clearly define what's available for free versus paid, making it harder to evaluate before committing.
Frequently Asked Questions
What types of AI vulnerabilities does Giskard detect?
Giskard detects both security vulnerabilities (prompt injection, data disclosure, sycophancy attacks, inappropriate content generation) and quality failures (hallucinations, omissions, contradictions, and inappropriate denials) across LLM-powered agents.
How does Giskard differ from traditional security scanning tools?
Traditional security tools operate at the network layer and miss domain-specific LLM failures. Giskard is purpose-built for AI agents, generating adversarial prompts and evaluating model outputs for both safety and business-logic quality issues.
Does Giskard support CI/CD pipeline integration?
Yes. Giskard provides a Python SDK that allows tests to be executed programmatically within CI/CD pipelines, and tests can also be scheduled via the web UI to run automatically after each model update.
Is Giskard compliant with data privacy regulations?
Yes. Giskard is a European entity offering native GDPR compliance, SOC 2 Type II and HIPAA certifications, EU/US data residency options, end-to-end encryption, and a zero-training policy to protect your intellectual property.
Who is Giskard designed for?
Giskard is designed for enterprise AI teams that include engineers building LLM applications, security teams assessing AI risk, and business stakeholders who need visibility into AI quality and safety—all collaborating through a shared platform.
