Resources
A collection of tools, guides, and reference links for software testing professionals and recruiters including certification lookups, training materials, and industry bodies/standards.
Use this form to quickly verify ANZTB certified testers by name. Alternatively you can go to the ISTQB Successful Candidates Register to search internationally. This tool is provided here for completeness.
This tool checks to see if the submitted agency is on the preferred supplier list/panel for that government. Links to relevant government portals are provided at the bottom of search results or directly where data is unavailable.
Two AI related certifications are offered by ISTQB at time of writing. The first is CT-GenAI (V1), which is all about AI assisted testing, the second is CT-AI (V2), which is about placing AI under test. Please check the ISTQB website for the most up-to-date information on these certifications.
CT-GenAI
Certified Tester Testing with Generative AI (CT-GenAI) is about using generative AI (like ChatGPT-style tools) as a helper for testing everyday software. It covers practical skills like writing good prompts to generate test cases and test data, using AI to speed up test design and reporting, and knowing the risks that come with relying on AI output like hallucinated or biased suggestions so testers can use these tools safely and efficiently. Grab the syllabus and a mock exam to learn more.
CT-AI
Certified Tester AI Testing (CT-AI) is about how to test AI systems themselves – think machine learning models, chatbots, or other AI-driven software. Because AI systems don’t behave like normal software (their outputs can be probabilistic, they learn from data, and they can be biased or hard to explain), CT-AI teaches testers how to design test strategies for those quirks: checking for bias, handling non-deterministic results, testing the underlying data quality, and setting up the right test infrastructure for AI-based products. Grab the syllabus and a mock exam to learn more.
NIST AI Risk Management Framework (AI RMF)
A voluntary framework from the US National Institute of Standards and Technology for managing risk across the AI lifecycle. It’s built around four functions; Govern, Map, Measure, and Manage; covering AI risk governance, understanding context and impact, assessing risk, and prioritising mitigation actions. (nist.gov)
Google’s Secure AI Framework (SAIF)
A conceptual framework from Google for securing AI systems against threats such as data poisoning, model theft, and prompt injection. It structures controls across the AI development lifecycle (secure development, deployment, execution, monitoring) and across system components (data, infrastructure, model, application). (safety.google)
National Framework for the Assurance of AI in Government
An Australian framework agreed by federal, state, and territory governments in June 2024, providing a nationally consistent approach to assuring AI use in the public sector. It sets out five assurance mechanisms; governance, data governance, a risk-based approach, standards, and procurement; aligned to Australia’s AI Ethics Principles. (finance.gov.au)
Australian Government AI Impact Assessment Tool
A structured, fillable assessment published by the Digital Transformation Agency for Commonwealth agencies to identify, assess, and manage the risks of an AI use case. It combines an initial threshold risk check with a deeper review against each of Australia’s AI Ethics Principles; fairness, safety, transparency, and more. (digital.gov.au)
Foundational AI Risk Assessment Framework (FAIRA) – QLD
A risk assessment tool used by Queensland Government agencies to evaluate AI solutions before adoption, focusing on transparency, technical risk identification, and alignment with organisational values. It sits alongside standards like ISO 42001 as an accepted assessment approach under the state’s AI governance policy. (forgov.qld.gov.au)
AI Assessment Framework (AIAF) – NSW
A mandatory, risk-based assessment that all NSW Government use of AI must apply, developed with CSIRO’s Data61. It operationalises the state’s AI Ethics Principles; trust, transparency, customer benefit, fairness, privacy, and accountability; into a structured, standards-aligned review. (digital.nsw.gov.au)
OWASP Top 10 for LLM Applications
A community-maintained list of the ten most critical security risks specific to large-language-model applications, covering issues like prompt injection, sensitive information disclosure, supply chain risk, and excessive agency. It’s widely used as a baseline checklist for securing and testing LLM-based applications. (owasp.org)
OWASP GenAI Red Teaming Guide
A practical guide for planning and running red team exercises against generative AI systems, covering model-level evaluation, implementation testing, infrastructure assessment, and runtime behaviour analysis. It gives security and testing teams a structured, risk-based methodology for probing GenAI systems for real-world vulnerabilities. (genai.owasp.org)
MITRE SAFE-AI
A MITRE framework for securing AI-enabled systems that maps known adversarial threats (from MITRE’s ATLAS knowledge base) against four system elements; environment, AI platform, AI model, and AI data. At each threat/element intersection it identifies relevant NIST SP 800-53 security controls, giving organisations a practical way to translate AI threat intelligence into concrete safeguards. (atlas.mitre.org)