Green Auditing

1. Monitor and interpret relevant laws, regulations, and standards to ensure the organization’s policies comply with all legal requirements. 2. Develop and enforce governance frameworks that ensure accountability and compliance with company policies, integrating data privacy, risk management, and business compliance processes. 3. Collaborate with stakeholders to ensure compliance risks, including data privacy and business risks, are considered and mitigated in new projects and initiatives, with appropriate controls and processes in place. 4. Conduct regular audits and assessments to evaluate the effectiveness of compliance controls, ensuring adherence to internal policies, regulations, and risk appetite limits. 5. Prepare and submit compliance and governance reports to regulatory bodies and internal stakeholders, ensuring timely communication of updates, exceptions, or emerging risks. 6. Develop and deliver training programs to promote awareness of regulatory requirements, data privacy, and governance policies among employees across the organization. 7. Maintain accurate records of all compliance and governance activities, including records of processing activities, policy exceptions, and audit findings, ensuring accessibility and accuracy. 8. Act as the primary point of contact for regulatory authorities and external auditors, managing responses to data privacy incidents and providing oversight in compliance matters. 9. Responsible for drafting, agreeing, and issuing compliance-related policies, including privacy notices, and managing governance structures to monitor compliance and escalate risks when necessary. 10. Build strong relationships with internal teams, such as GBS and risk management, to gain an understanding of business initiatives, influence scope and architecture, and support compliance in ongoing and new activities.

1. Defining data elements and establishing policies and procedures for data collection 2. Coordinating with different departments to identify and understand their data requirements 3. Maintaining and ensuring the quality, integrity, and compliance of data across the organization 4. Overseeing data migration, transformation, and validation processes 5. Implementing data standards, rules, and quality requirements 6. Providing support for data warehousing, mining, and distribution 7. Resolving data issues and discrepancies and ensuring data consistency 8. Developing and maintaining data catalogs, dictionaries, and taxonomies 9. Conducting regular data audits to verify data accuracy and integrity 10. Advising and training staff on data management and quality principles 11. Implementing data privacy policies and ensuring compliance with regulations

1.Plan and conduct internal audits of organisational processes, controls, and financial statements. 2.Assess risks and evaluate the effectiveness of internal controls to mitigate risks and ensure compliance. 3.Prepare audit reports detailing findings, recommendations, and corrective actions for management review. 4.Conduct compliance audits to ensure adherence to regulatory requirements and company policies. 5.Review and evaluate operational procedures and systems to identify potential areas of improvement and efficiency. 6.Collaborate with management to develop audit schedules and prioritise audit activities based on risk assessments. 7.Monitor and follow up on the implementation of audit recommendations to ensure corrective actions are effective. 8.Stay updated on changes in auditing standards, regulations, and best practices to enhance audit methodologies. 9.Provide guidance and training to staff on audit processes, standards, and best practices. 10.Maintain confidentiality and objectivity throughout the audit process to ensure impartial and unbiased reporting.

1. Prepare payment runs, including checks, electronic transfers, and wire payments. 2. Execute payment transactions accurately and timely. 3. Reconcile processed work by verifying entries and comparing system reports to balances. 4. Monitor payment statuses and address any issues that arise during the payment process. 5. Ensure all payments comply with company policies and relevant regulations. 6. Generate and analyse payment-related reports to track performance and identify improvement areas. 7. Collaborate with finance, accounting, and treasury teams to optimise payment processes and controls. 8. Implement fraud detection measures and security protocols to safeguard payment transactions. 9. Provide support and guidance to internal stakeholders on payment processing procedures and policies. 10. Continuously improve payment processing efficiency through automation and process enhancements.

1. Develop optimized and secure prompts to ensure AI systems generate 2 Identify and mitigate vulnerabilities in AI language models by testing for prompt injection attacks and other adversarial inputs. 2. Conduct risk assessments for language models and AI systems to evaluate exposure to prompt injection attacks and recommend improvements. 3. Develop and run adversarial prompt scenarios to probe for weaknesses in AI model responses, with the goal of enhancing the system’s resilience against malicious prompts. 4. Work closely with AI developers, machine learning engineers, and security teams to integrate prompt protection mechanisms in AI models. 5. Assist in refining the training data and fine-tuning AI models to ensure that they handle complex, ambiguous, or malicious prompts effectively. 6. Ethical AI and Safety Standards: Ensure that the design and implementation of prompt strategies align with ethical guidelines and safety standards, avoiding harmful or unintended outputs. 7. Automated Defense Systems: Design and develop automated systems that can detect and respond to potential prompt injection attacks in real-time 8. Continuous Improvement and Monitoring: Monitor deployed AI systems for signs of prompt injection vulnerabilities, providing continuous improvement updates as new attack vectors emerge. 9. Research and Innovation in AI Security: Stay updated with the latest research in AI safety, prompt engineering, and adversarial attacks, and apply innovative solutions to enhance system security. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–5198. https://doi.org/10.18653/v1/2020.acl-main.463 Wallace, E., Feng, S., Kandpal, N., Singh, S., & Gardner, M. (2019). Universal adversarial triggers for attacking and analyzing NLP. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2153-2162. https://doi.org/10.18653/v1/D19-1221 Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., & Choi, Y. (2019). Defending against neural fake news. Advances in Neural Information Processing Systems, 32, 9051–9062. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901. Goodfellow, I., McDaniel, P., & Papernot, N. (2018). Making machine learning robust against adversarial inputs. Communications of the ACM, 61(7), 56-66. https://doi.org/10.1145/3134599