Web application that allows organizations to assess their level of maturity in artificial intelligence governance and automatically generate a customized roadmap to meet national and international standards

Cloud platform for evaluating AI system performance on private data and presenting results

Amazon Nova Premier is a multimodal foundation model that was evaluated under Amazon’s Frontier Model Safety Framework to assess and mitigate risks related to Chemical, Biological, Radiological, and Nuclear (CBRN) weapons proliferation, offensive cyber operations, and automated AI research and development.

GovLLM is an open-source runtime governance framework for LLM systems. It treats regulatory compliance as a continuous signal derived from production observability rather than a static audit verdict. A panel of small language model judges evaluates each response against governance profiles.

The AI red team service exposes hidden safety and security threats across the entire lifecycle of artificial intelligence (AI) systems by applying an adversarial mindset to assess AI systems during design, development, deployment, and operations stages.

FlowMS is an AI-powered utility efficiency tool built on AWS that analyses metering data to detect anomalies in water use and help conserve water in Amazon buildings.

LLM Vulnerability Scanner and Guardrails provides comprehensive assessment of LLM vulnerabilities and automatic application of optimal defensive techniques to generative AI on LLMs.

The Agentic Benchmark for CRM is a benchmarking framework developed by Salesforce to evaluate the performance of AI agents and models in enterprise customer relationship management (CRM) use cases using metrics such as accuracy, cost, speed, trust and safety, and sustainability.

The Moderation endpoint allows developers to classify text and image inputs to determine whether they may violate OpenAI’s safety policies.

The Google Responsible Generative AI Toolkit provides tools and guidance to design, build and evaluate open AI models responsibly.

ShieldGemma is a set of instruction tuned models for evaluating the safety of text and images against a set of defined safety policies.

FACTS Grounding is a comprehensive benchmark for evaluating the ability of LLMs to generate responses that are not only factually accurate with respect to given inputs, but also sufficiently detailed to provide satisfactory answers to user queries.

The OWASP Top 10 for Large Language Model (LLM) Applications is a written guidance document developed by the OWASP community to identify the most critical security risks affecting applications that use large language models.

VERA-MH (Validation of Ethical and Responsible AI in Mental Health) is a comprehensive framework for evaluating AI chatbots in a mental health context.

The MLCommons AILuminate benchmark evaluates an AI system-under-test (SUT) by inputting a set of prompts, recording the SUT’s responses, and then using a specialized set of “safety evaluators models” to determine which of the responses are violations according to the AILuminate Assessment Standard guidelines. Findings are summarized in a human-readable report.

garak is an open-source LLM vulnerability scanner developed by NVIDIA that probes large language models for security weaknesses including prompt injection, jailbreaks, hallucination, toxicity, data leakage, and misinformation.

AuditNLG is an open-source toolkit for auditing the trustworthiness of generative AI text. It evaluates outputs across three key dimensions: factualness (consistency with knowledge), safety (harmful or biased content), and constraint adherence (compliance with instructions). The tool aggregates multiple state-of-the-art methods and provides scores, explanations, and improved text suggestions via self-refinement prompts. It supports both API-based and local models, enabling flexible integration into evaluation pipelines and governance frameworks.

OpenEnv is a framework for evaluating AI agents against real systems rather than simulations. It provides a standardised way to connect agents to real tools and workflows while preserving the structure needed for consistent and reliable evaluation.

The AI Inherent Risk Scale (AIIRS) is a task-based classification instrument that helps organisations assess the inherent risk of generative AI use. It evaluates tasks against three criteria—epistemic dependence, verifiability, and consequences of error—and assigns a LOW, MEDIUM, or HIGH risk rating using a max-dominant model. AIIRS supports proportionate safeguards, accountable oversight, and governance-aligned decision-making without determining whether AI use is permitted in a given context.

WasItAI is an image-checker designed to detect AI-generated photos.