AiEnsured is a commercial testing platform for AI products, designed to support the deployment of responsible AI. It is developed in India. The platform brings together fairness and bias evaluation, explainability, adversarial robustness testing and privacy and GDPR compliance checks. It also generates test cases automatically, including rare and extreme inputs. Further features cover experiment management, model comparison and performance testing. After deployment, it monitors models for concept drift, which can reduce accuracy over time.

Noisegate is an open-source demonstration of a gateway that lets AI agents query sensitive data without exposing individual records. Its privacy protections do not depend on the AI being trustworthy. A trusted layer checks every query against a policy, adds calibrated noise to answers and limits each user's privacy budget. Working attack demonstrations show its protections in action. The developer describes it as a demonstration, not a production product. It is released under the Apache 2.0 licence.

Diffprivlib is an open-source Python library for differential privacy, developed by IBM Research in Dublin. It adds calibrated random noise to data analysis and machine learning, so that no individual can be identified. The library provides privacy mechanisms, machine learning models with built-in privacy, data analysis tools and a privacy budget tracker. Its models work like those of scikit-learn, making them easy to adopt. It is intended for research and education, and was archived in September 2026.

pyPANTERA is an open-source Python package for obfuscating text using differential privacy. It was developed at the University of Padua. The package replaces words in a text with substitutes, so the original cannot be directly recovered, while keeping the text useful for tasks such as search. It implements seven published mechanisms in one framework, allowing users to reproduce and compare them. It won the Best Resource Paper Award at CIKM 2024 and is released under the GPL-3.0 licence.

SampCert is an open-source, formally verified implementation of algorithms for differential privacy. Differential privacy protects individuals by adding calibrated random noise to data analysis results. Errors in generating this noise can silently weaken protection. SampCert uses the Lean proof assistant to prove mathematically that its algorithms behave exactly as specified, including a discrete Gaussian sampler. It is used in production in the AWS Clean Rooms Differential Privacy service. It is released under the Apache 2.0 licence.

Google's Differential Privacy libraries are an open-source collection of tools for producing statistics from sensitive data while protecting individuals' privacy. They add calibrated random noise to results such as counts and averages, so no single person can be identified. Building block libraries are available in C++, Go and Java. End-to-end frameworks make the tools usable by non-experts on large-scale data systems. Further tools track privacy budgets and audit privacy guarantees. The libraries are released under the Apache 2.0 licence.

The OpenDP Library is an open-source, modular collection of algorithms for differential privacy. It is the core library of the OpenDP Project, a community effort led from Harvard University. Users build analyses from small components that transform data or add calibrated noise to protect individuals. The library tracks the overall privacy cost of each analysis. It is written in Rust, with bindings for Python and R. It is still under development and released under the MIT licence.

PyDP is an open-source Python library for differential privacy, created by OpenMined. It wraps Google's Differential Privacy library, giving Python users access to tested algorithms. PyDP produces summary statistics, such as means, sums and medians, from sensitive datasets while adding calibrated noise to protect individuals. Users set a privacy parameter that controls the trade-off between privacy and accuracy. PyDP runs on Linux, macOS and Windows. It is released under the Apache 2.0 licence.

dpmm is an open-source Python library for generating synthetic tabular data with differential privacy guarantees. It was developed by SAS. Synthetic data preserves the statistical patterns of real data without reproducing real individuals, so it can be shared or used to develop AI models. The library includes three widely used models: PrivBayes, MST and AIM. It protects privacy across the whole process, including data preparation, and addresses known vulnerabilities in differential privacy software.

DP-CGANs is an open-source Python library for generating synthetic data that protects individual privacy, designed with personal health data in mind. It was developed at Maastricht University. The library uses a conditional generative adversarial network, adapted to capture relationships between variables in the data. Differential privacy can be enabled during training, so no single person strongly influences the result. It works with tabular and RDF data. It is still under development and released under the MIT licence.

The SmartNoise SDK is an open-source toolkit for applying differential privacy to tabular and relational data. It was originally developed by Microsoft with Harvard's OpenDP initiative. SmartNoise SQL runs standard SQL queries and adds calibrated noise to results, so no individual can be identified. SmartNoise Synth generates synthetic data that preserves statistical patterns without reproducing real people. This lets organisations share data and develop AI models while protecting privacy. It is written in Python under the MIT licence.

Meilynx is a software tool for governing large language models (LLMs) and AI agents used in regulated sectors such as financial services, insurance, healthcare, and human resources. It runs as a proxy between applications and model providers, where it redacts sensitive data, restricts model use, and controls agents' tool calls. Each decision is recorded in a tamper-evident audit log that can be mapped to frameworks such as the EU AI Act. The tool provides evidence for controls but does not certify compliance.

GovAI is the official Australian Government AI platform. Announced in July 2025, GovAI offers Australian Public Service (APS) staff secure access to advanced AI capabilities through government-controlled infrastructure. A key aspect of the platform is the Interactive Learning Environment that provides hands-on-training using multiple AI models, enabling staff to build practical AI skills in a secure, risk-free environment.

PrivFair is a library for auditing the fairness of machine learning models while protecting privacy. Fairness audits require sensitive data about attributes such as gender or race, which is protected by law. PrivFair uses Secure Multiparty Computation, a cryptographic technique, so that an external auditor can test a company's model without either party revealing their data or model. It supports group fairness audits on tabular and image data. This enables independent, privacy-preserving checks of proprietary AI models.

Superagent SDK is an open-source toolkit that embeds runtime safety controls into AI agent applications, detecting and blocking prompt injections, redacting sensitive data, scanning repositories for AI-targeted threats, and red-teaming agents before deployment.

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.

The Einstein Trust Layer is a secure AI architecture built into the Salesforce platform that provides guardrails to protect data privacy and security, improve the safety and accuracy of AI outputs, and enable Salesforce customers to use generative AI responsibly within Salesforce applications.

EuroDaT is a European data trustee with a unique data transaction principle that ensures the secure and legally compliant exchange of data between any parties.

A legally enforceable AI-user interaction framework that verifies informed consent through multimodal methods, protects user intellectual property via blockchain-based tracking, and ensures lifetime authorship rights with legal safeguards against unauthorized use or AI training reuse.

mobile Detection, Research, Education, Equipment, Training programme provides a mobile hearing loss detection test for children who are entering schooling (as well as for other life stages) and an AI-enabled application that turns a smartphone into a personalised hearing aid