AI-related Regulations and Standards Implementation and Assurance is a set of workbenches from the AI Assurance Institute. It helps organisations implement and assess compliance with the Cyber Resilience Act, DORA, NIS2 and EN, ETSI, ISO and NIST standards. Process owners work from action lists. They identify requirements, risks and controls. They then build a roadmap, produce artefacts and assess compliance. Dedicated flows also cover AI agent control and multi-agent systems.

CREST's nine Principles for AI-enabled Activities provide a practical foundation for responsible AI use in cybersecurity. They establish shared expectations for the governance and oversight of AI-enabled activities, covering accountability, transparency, documentation, human oversight, data handling, security, secure development, supply chain assurance and resilience. The Principles support cybersecurity service providers in addressing the opportunities, risks and responsibilities associated with AI use and contribute to trust, transparency and accountability in AI-enabled cybersecurity services.

The CREST AI Charter is a voluntary commitment supporting responsible AI use across the cybersecurity profession. It brings together organisations that recognise the importance of shared principles, professional standards and industry collaboration. Charter Signatories publicly support CREST's nine Principles for AI-enabled Activities and contribute to an international industry initiative promoting trust, transparency, accountability and assurance in AI-enabled cybersecurity services.

The MLCommons Jailbreak Benchmark v1.0 is a benchmark for evaluating how well large language models withstand jailbreak attacks, which are prompts designed to bypass safety safeguards. It was published by MLCommons, an industry and academic consortium. The benchmark applies known jailbreak techniques to prompts across eleven hazard categories. Results are compared with a safety baseline to show how much attacks weaken a system’s safety. Systems are graded on how often they produce content that violates safety standards.

The CREST Accreditation Standards set requirements for organisations that provide cyber security services, verified through independent assessment. They are published by CREST, an international not-for-profit body. Three components address AI. Domain 7 requires all accredited organisations to govern their use of AI, including validating AI-supported outputs. Annex B sets requirements for using AI in penetration testing, with human validation of findings. A third standard covers security testing of generative AI and large language model-enabled systems.

The VeritasChain Protocol (VCP) is an open specification for cryptographically verifiable audit trails in algorithmic and AI-driven trading. It is maintained by the VeritasChain Standards Organization. VCP lets regulators and auditors verify mathematically that records of trading decisions, orders, risk controls and AI model use are complete and unaltered. A privacy module allows personal data to be erased while keeping the audit trail intact. VCP is designed to support MiFID II, EU AI Act and GDPR compliance.

FAIAS (Financial AI Independent Assurance Standard) is a baseline for reviewing generative, agentic and predictive AI systems in financial institutions. It was developed by Wonder Attah. FAIAS sets out 109 review questions across nine domains, such as data, security and fairness, each with a safe, non-production test procedure. Systems are reviewed in proportion to their risk. The author describes it as emerging work, not a certification scheme. It is released under a CC BY 4.0 licence.

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.

KSAFE-MM is a Korean-language benchmark for evaluating safety risks in multimodal large language models. It was developed by K-intelligence at KT in South Korea. The benchmark contains 14,135 query-image pairs across 11 risk categories, including hate, violence, privacy and weaponisation. One subset tests Korean culture-specific risks and applies jailbreak strategies such as role-play. Researchers use it to check whether models respond safely. It is available for research under a CC BY-NC 4.0 licence.

F2Bench is a benchmark for evaluating fairness in large language models that also considers factual accuracy. It was developed by researchers at Inner Mongolia University and Xiamen University. The benchmark contains 2 568 test instances across ten demographic categories, including intersectional combinations. One task tests whether models accept biased conclusions after gradual stereotypical suggestions in a conversation. A second task tests whether models avoid stereotypes while still reflecting real statistics accurately. The code and dataset are publicly available.

FLEX is a benchmark for testing whether large language models stay fair when prompts are designed to induce bias. It was developed by researchers at Korea University. The benchmark contains 3,145 multiple-choice questions from established fairness datasets, each combined with an adversarial prompt. These include assigning negative personas, forbidding refusals and introducing small text changes. Results show that models appearing fair on standard benchmarks can still be easily manipulated. The data and code are available for research purposes.

Gate AI: LLM Security Benchmark Evaluation Methodology and Results is a technical report on evaluating detectors of prompt injection and jailbreak attacks. It was published by Constellation Network. The method tests a detector across 16 public benchmarks with more than 12 000 samples. It uses a single detection threshold for all benchmarks and prevents near-duplicate prompts from inflating results. Competing detectors are compared at the same false-alarm rate. The report applies the method to Gate AI, Constellation Network's commercial security gateway.

FairnessEval is an open-source framework for evaluating the fairness of machine learning models. It was developed by the University of Modena and Reggio Emilia with Microsoft. Users can load real or synthetic datasets and run fairness-aware models from toolkits such as Fairlearn and AIF360. The framework compares models on fairness, accuracy and training time, and presents the trade-offs in charts. It supports both selecting a suitable model and validating its performance under changing conditions.

The Safety Evaluation Benchmark for Vision LLMs is an open dataset for testing the safety and robustness of models that answer questions about images. It was developed by researchers led by the University of California, Santa Cruz. One part tests how models handle unusual images, such as sketches, and misleading questions. A second part tests resistance to attacks, including altered images and jailbreak prompts. The dataset contains about 2 000 samples and 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.

MANILA is a web-based, low-code application for benchmarking machine learning models and methods that reduce bias. It helps users find the combination that offers the best balance between fairness and performance. MANILA models a fairness benchmarking workflow as a set of options, such as datasets, models and metrics. Rules between these options guide users and prevent experiments that would fail. This makes fairness benchmarking accessible to users with little or no programming experience.

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.

RecourseBench is a modular evaluation framework for algorithmic recourse methods that emphasises reproducibility when assessing user-facing counterfactual explanations. It enables practitioners to systematically compare recourse methods based on how they support actionable changes in decision-making systems. It is intended for researchers and developers who validate and improve the trustworthiness of explanation and human-agency features in AI used for consequential decisions.

ModelBench is a benchmarking tool for running safety-focused evaluations on AI models and producing detailed reports on performance against the benchmark suite. It is intended for developers and researchers who need to verify and compare safety/robustness behavior before deployment and during ongoing evaluation. The outputs support auditing and model validation for safety-related trustworthiness objectives.

OpenART is an open-source framework designed to evaluate the security of AI agents in dynamic, long-horizon, and stateful environments. It stress-tests agent runtimes against multi-step state poisoning, privilege escalation, and tool-use vulnerabilities across more than 10,000 benchmark scenarios.