AI Risk and Readiness Assessment

Which data aspect you are intrigued to test your preparedness?

Data Security

1. We have robust data security measures, encompassing encryption, access controls, and regular audits, to safeguard against unauthorised access and data breaches.
2. We have proactive steps to assess and mitigate security risks associated with AI applications, particularly those involving sensitive or personal data.
3. We frequently provide security training to staff to ensure awareness and compliance with data security protocols.
4. In the event of a security incident, we respond and recover quickly. We have measures in place for continuous improvement.
5. We ensure that third-party vendors and partners adhere to the same rigorous data security standards as our organisation.
   

Data Storage

1. Our current data storage and infrastructure is scalable and resilient, ensuring it can accommodate the increasing demands associated with AI initiatives.
2. Our data storage capacity is monitored and optimised to avoid unnecessary costs while ensuring accessibility and performance.
3. We have redundancy measures in place to mitigate the risk of data loss or disruption, and they are frequently tested.
4. We prioritise data archiving and retrieval, and align these with regulatory requirements and business priorities.
5. We balance the utilisation of on-premises and cloud- based storage solutions to optimise cost and performance.
   

Data Quality

1. We have a rigorous approach to ensuring data accuracy, completeness, and consistency, particularly in the datasets utilised for AI training and decision-making.
2. We have tools and processes in place to detect and rectify data quality issues. These are integrated into our broader data management strategy.
3. We involve end-users and data stakeholders in the data quality assurance process to capture diverse perspectives.
4. We ensure the ongoing alignment of data quality standards with evolving business needs, industry best practices, and technological advancements.
5. We have measures in place to address biases and ensure fairness in our data quality assessment processes.
   

Data Architecture

1. We have well-defined data architecture, considering factors like data lakes, warehouses, and integration points, to support the scalability and flexibility required for AI initiatives.
2. Our data architecture is designed to facilitate seamless integration between various data sources, ensuring a comprehensive view for analytical purposes.
3. We regularly review and update data architecture to accommodate evolving business requirements and technological advancements.
4. We have measures in place to ensure that our data architecture aligns with industry standards and emerging best practices in the field.
5. We ensure that data architecture decisions are communicated effectively across the organisation, fostering collaboration and understanding.
6. We have internal reviews of privacy and data protection laws when designing and updating our data architecture.
   

AI Ethics

1. We ensure ethical considerations are embedded in our AI development and deployment processes, avoiding biases and promoting fairness.
2. Our AI algorithms are transparent and explainable, allowing stakeholders to understand and trust the decision-making process.
3. We have mechanisms in place to regularly audit and monitor AI systems for ethical compliance and adherence to established principles.
4. We involve diverse stakeholders, including ethicists and representatives from impacted communities, in shaping our AI policies and practices.
5. We communicate our commitment to AI ethics both internally and externally, fostering trust among employees, customers, and the broader community.
6. We ensure ongoing training and awareness programs for employees regarding ethical considerations in AI development and usage.
7. We have measures in place to address ethical challenges that may arise in the integration of AI into various business processes and decision-making.