AI Labs

AI Assurance services

Best practice services for the selection and implementation of state-of-the-art AI assurance techniques and tools

AI Assurance Services

Our AI Assurance practice encompasses skills across regulation and standards with a deep analytics capability. We harness these to provide AI Assurance solutions to those charged with governance and serving the public interest.

Within our AI Assurance practice, we integrate expertise in regulation and emerging safety standards with a advanced analytics capability. This synergy enables us to deliver AI and data due diligence services to Private Equity firms seeking assurance for their AI investments and transactions.

In the face of evolving challenges and opportunities, businesses engaging with AI must be prepared to adapt. There is a growing demand for increased transparency in AI reporting to foster trust and confidence in the market, as well as to safeguard the broader public interest.

Our online legal and compliance risk solutions are designed to cultivate, sustain, and enhance confidence. Our independent assurance services involve assessing and evaluating AI risks and underlying processes, offering insights into the management and mitigation of risks in line with risk appetite. Advanced Analytica's assurance solutions assist governance leaders in comprehending their business and instilling trust among key stakeholders, end users, and regulators.

With analytics at the core, we bring together essential skills and experience from our global team to address the evolving assurance needs of the AI market. Our teams collaborate to review and provide recommendations across the first and second lines of defence, delivering meaningful, high-quality outcomes and information to those responsible for governance.

Our collaborative team of subject matter experts comprises industry practitioners, digital specialists, legal professionals and treasury, international affair advisers and capital market experts. This collective expertise is tailored to serve businesses, providing specialised industry knowledge.

Six Principles of Assured AI

Appropriate transparency and explainability

Appropriate transparency and explainability refers to duties  to document and allow access to relevant information about AI systems and to present this information in a way that is comprehensible to stakeholders including developers, end-users, and affected parties.

Safety, security and robustness

Safety, security and robustness are essential RTAI components as the performance of AI systems must be robust enough to function as intended, both in testing stages and in real-world settings. If an AI system does not function as intended, the system could cause harm.


Non-maleficence requires AI systems to do no harm against individuals, communities, society at large and the environment. Harm includes violations of human dignity and human rights as well as violations of mental, physical, and environmental integrity and wellbeing and the potential for clandestine surveillance.

Fairness and justice

Fairness and justice refer to duties to promote equality, equity and nondiscrimination. It is crucial due to the harmful biases AI systems have the potential to perpetuate. Biases in AI can lead to entrenching structural social inequalities and stereotypes affecting people in vulnerable situations.

Privacy and data protection

Privacy refers to the right to limit access to, and augment a person’s control over, personal information. Data protection and cybersecurity regulations and best practices help protect the right to privacy. Strongly related to concerns about the mass collection of digital information and the potential for blanket surveillance.


Accountability requires individuals or organisations to take ownership of their actions or conduct and to explain reasons for which decisions and actions were taken. When mistakes or errors are made, accountability also implies establishing accessible avenues for contestability and redress.

AI Assurance Solutions

AI use case assessments and strategy

The development of use case assessment tools, due diligence frameworks, and strategic planning. Detailed analysis and prioritisations of use cases to ensure optimal deployment, integration and performance of AI systems, products and services.

Impact assessments and evaluations

Implementing layered assessment frameworks to anticipate and measure new systems, and retrospectively evaluate the effects of existing AI systems on various outcomes, including environmental, equality, human rights, and privacy impact assesments.

Bias and compliance audits

Rigorous audits to identify and rectify unfair biases within algorithmic decision-making systems. Additionally, conducting compliance audits to review adherence to internal policies, external regulations, and legal requirements.

Certification and performance testing

Verification processes and regulator audits of products, services, or organisations, against objective quality or performance standards. Comprehensive evaluation methods to gauge AI system functionality against predefined benchmarks and performance requirements.

Formal verifications and conformity assessments

Employing mathematical techniques and logical analysis to ensure AI systems meet specified requirements. Assurance that products, services, or systems meet specified expectations before market entry, involving activities such as testing and inspection.