Directory AI & ML PlatformsAI Governance & SafetyGiskard

Giskard

About Giskard

Giskard provides an AI governance platform focused on continuous red teaming and security evaluation of large language models (LLMs). Designed for enterprise AI teams, it detects vulnerabilities such as prompt injections, data disclosures, hallucinations, and inappropriate content before deployment and throughout the AI lifecycle. The platform automates sophisticated adversarial testing, enabling organizations to proactively identify and mitigate risks that could impact data security, compliance, and user experience.

The solution is tailored for enterprises deploying AI agents, offering granular access controls, data residency options, and compliance with GDPR, SOC 2 Type II, and HIPAA standards. Giskard unifies testing across business, engineering, and security teams through collaborative dashboards and continuous integration capabilities. By converting detected issues into reproducible test suites, it helps prevent regressions and ensures AI quality and security remain aligned with organizational requirements.

How to evaluate AI Governance & Safety

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Giskard. It comes from our AI Governance & Responsible AI buyer guide.

25%

AI Inventory & Lifecycle Governance

Automated discovery and a registry of every model, LLM/prompt, agent, and embedded-AI feature (including shadow and third-party AI); risk tiering by use case; intake-to-retirement workflow with approvals, deployment gates, and named owners; versioning and change control

25%

Regulatory Mapping & Compliance Evidence

Out-of-the-box, maintained policy packs for the EU AI Act, NIST AI RMF, ISO 42001, SR 11-7, and sector rules (e.g. NYC LL144); control mapping and gap analysis; one underlying assessment that satisfies many frameworks; audit-ready, exportable evidence and model cards

20%

Model Monitoring & Explainability

Production monitoring for drift, performance decay, and data quality; bias and fairness testing across protected groups; explainability (e.g. SHAP / feature attribution) for tabular and NLP models; the depth of this domain is what separates observability tools from pure GRC

15%

GenAI & Agentic Oversight

LLM evaluation and guardrails (hallucination, toxicity, PII leakage, prompt-injection and jailbreak defense); agent discovery, runtime policy enforcement, and a logged trail of agent actions and the authority behind them; red-teaming and continuous evaluation of agent traces

10%

Integration & ML-Stack Fit

Connectors to your model platforms (SageMaker, Vertex AI, Databricks, Dataiku, MLflow, Bedrock), CI/CD and registries; API and policy-as-code coverage; identity (SSO/RBAC); fit with existing GRC and data-security tooling rather than yet another silo

Human-in-the-Loop & Accountability

Cross-functional workflows that reach risk, legal, and business owners (not just data scientists); reviewer sign-off and attestations; issue and exception tracking; reporting that a board or regulator can read, with a defensible audit trail

Other AI Governance & Safety Vendors

Related Buyer Guides

Our buyer guides across AI & ML Platforms. Each one compares the main vendors in its category and what buyers weigh up.

AI Agent & Agentic AI Platforms

Compare LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK, AWS Bedrock AgentCore, LlamaIndex, and Temporal — where production operability, not the slickest multi-agent demo, is the deciding criterion.

AI Governance & Responsible AI

Evaluate IBM watsonx.governance, Credo AI, Microsoft Purview, ServiceNow, Holistic AI, Fiddler, Arthur, and Monitaur — and decide first whether your gap is governance and compliance or ML observability, because the EU AI Act and NIST AI RMF reward the platform wired into how models are built and run.

AI Model Serving & Inference Platforms

Six ways to buy inference, and they meter you differently: per token, per GPU-second, per host-hour, per image, per provisioned unit, or not at all. Pick the wrong unit for your traffic shape and the bill outruns the workload.

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .