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Book Your Discovery CallA systematic record of events, decisions, and actions taken by or upon an AI system. Audit trails provide traceability for compliance, facilitate investigations, and support continuous improvement in AI governance.
A type of attack where inputs are subtly manipulated to deceive AI models into making incorrect predictions or decisions. Protecting AI systems from adversarial attacks is a key component of AI Security.
Use of software tools and frameworks to automatically enforce compliance requirements, generate audit logs, and monitor AI systems for regulatory adherence. Automated compliance reduces manual effort and ensures consistent control application.
Adherence of AI systems to applicable laws, regulations, and standards, such as the EU AI Act, GDPR, or ISO 42001. Compliance ensures that AI solutions meet legal and ethical obligations related to transparency, accountability, data privacy, human oversight, and more.
Protective measures designed to safeguard AI systems from external attacks, misuse, or compromise. This includes protecting models against adversarial attacks, data poisoning, prompt injections, and securing AI supply chains to prevent manipulation or exploitation.
Practices, tools, and methodologies that ensure AI systems behave as intended without causing harm. AI Safety focuses on preventing unintended actions, managing model uncertainty, and reducing risks like hallucinations, prompt injections, or unsafe autonomous decisions.
Predefined boundaries or controls embedded within AI systems to prevent undesirable outcomes. Examples include blocking sensitive topics in chatbots, restricting personal data sharing, or setting output quality checks. Guardrails help enforce organisational policies and regulatory requirements in real-time.
The systematic identification, assessment, mitigation, and monitoring of risks associated with AI systems. This includes risks like data privacy breaches, algorithmic bias, hallucinations, and misuse of AI, helping organisations make informed, safe, and compliant decisions regarding AI deployment.
A structured framework of policies, processes, and controls that ensures AI systems are designed, deployed, and managed responsibly. It covers ethical principles, operational oversight, risk management, and compliance obligations, enabling organisations to balance innovation with accountability and trust.
Unfair or prejudiced outcomes produced by AI systems due to biased training data, flawed algorithms, or operational processes. Managing bias is essential for ethical AI use, regulatory compliance, and ensuring fairness in automated decisions.
An ongoing process of testing and verifying AI models in production to ensure they meet performance, safety, and compliance criteria over time. Continuous validation mitigates risks from model drift, bias emergence, and data shifts.
Ensuring that AI systems protect personal and sensitive data in accordance with data protection regulations like GDPR. This involves practices like data anonymisation, encryption, access controls, and minimising data use in model training and inference.
The world of AI is filled with complex terms and evolving concepts. Our comprehensive glossary is here to help. Whether you are a business leader, risk professional, or compliance expert, this resource explains essential AI terms in clear, practical language, so you can understand, adopt, and govern AI responsibly.
The ability to understand, interpret, and explain how an AI model reaches its decisions. Explainability is critical for regulatory compliance, building user trust, and enabling human oversight, especially in sensitive or high-risk applications.
The European Union’s landmark regulation designed to ensure AI systems used within the EU are safe, transparent, traceable, non-discriminatory, and environmentally friendly. It categorises AI systems into risk levels (minimal, limited, high, prohibited) and mandates strict requirements for high-risk AI.
Instances where AI models generate false, misleading, or fabricated outputs that appear plausible but are factually incorrect. Hallucinations pose significant safety and reliability risks, especially in customer-facing or high-risk applications.
Involvement of humans in supervising, approving, or intervening in AI decision-making processes. Human oversight ensures that critical decisions are not fully automated and that AI errors or biases can be detected and addressed in time.
As per regulations like the EU AI Act, these are AI systems whose failure or misuse can significantly impact human safety, rights, or critical services. Examples include AI in financial decision-making, biometric identification, healthcare, and infrastructure management.
An international management system standard providing a structured approach to governing and managing AI. ISO 42001 outlines requirements for policies, roles, responsibilities, and controls to ensure AI systems are safe, secure, and compliant.
A phenomenon where an AI model’s performance degrades over time due to changes in data, environment, or usage patterns. Monitoring and managing drift is essential to maintain AI reliability and compliance.
A governance practice that oversees AI models from initial development through deployment to retirement. MLM ensures models remain accurate, reliable, and compliant throughout their operational life.
A discipline focused on managing risks arising from the development, deployment, and use of machine learning and AI models. MRM involves model validation, performance monitoring, documentation, and lifecycle management to ensure models operate reliably and safely.
A voluntary framework developed by the US National Institute of Standards and Technology to help organisations manage risks associated with AI systems. It provides guidelines for identifying, assessing, managing, and monitoring AI risks across system lifecycles.
A governance tool that allows non-technical users to define, apply, and manage AI control policies through simple interfaces without needing programming skills. This empowers compliance and risk teams to enforce AI controls organisation-wide.
A security vulnerability in large language models (LLMs), where malicious inputs are crafted to manipulate the AI’s behaviour or bypass safeguards. Mitigating prompt injections is critical for maintaining AI integrity and security.
Continuous tracking of AI system behaviour, outputs, and inputs in production environments. Real-time monitoring enables quick detection of anomalies, policy breaches, or emerging risks, supporting proactive governance and control.
An approach that ensures AI systems are designed and operated ethically, safely, and transparently. Responsible AI integrates fairness, accountability, explainability, and human oversight, aiming to build public trust and align AI usage with societal values.
A principle requiring AI systems and processes to be open and understandable to stakeholders. Transparency covers areas like data provenance, model logic, training processes, and decision pathways, enabling oversight and accountability.
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