Seclea Platform provides a cross-functional platform to manage your AI application projects, meeting the needs of your data science, compliance, risk management, and technical audit teams’ requirements to build and deploy robust, responsible and regulatory compliant machine learning solutions.
Compliance and Ethics
Machine learning and deep learning applications involve diverse stakeholders that belong to various roles and functional departments/groups, internal or external to an organisation. These stakeholders can range from, but are not limited to, data scientists, machine learning engineers, AI project managers, regulatory compliance managers, ethics leads and risk & governance managers.
These roles have stakes in successfully developing and deploying an AI application and require specific and timely information about pre-deployment and post-deployment AI applications – something that the cross-functional nature of Seclea Platform provides.
Data scientists and machine learning engineers
They Aim to develop machine learning applications that meet the business requirements. They need to ensure that the development applications are fair and have no bias in the data or the trained AI model. All development activities were in line with the Responsible AI guidelines and within the scope of the relevant AI regulations and standards. Data scientists and machine learning engineers want to make sure that all activities are recorded and traceable to identify the issue and rectify them quickly.
Seclea empowers data scientists with an automated and easy-to-integrate platform, focusing on developing robust and best-performing applications. And leave the AI fairness, explainability, accountability and regulatory compliance to Seclea Platform. Thus saving considerable time and effort and reducing the time to production for the respective AI applications.
AI project and product managers
Al project managers want to:
- Monitor the AI project activities taken and their impacts on the respective AI application’s fairness, explainability and regulatory compliance.
- Report to stakeholders about AI projects, their robustness and performance, and regulatory compliance and risk management activities.
- Ensure all AI applications pre-deployment and post-deployment are regulatory compliant and does not pose any risk to the organisation.
Seclea provides necessary tools and configurable dashboards and reports so AI project managers can efficiently perform their tasks. Seclea enables project managers to specify unique requirements for their AI projects. For example, they can set different regulatory compliance requirements for two different AI projects.
Regulatory compliance manager
Seclea Platform enables compliance managers to specify the regulatory compliance requirements for individual AI projects and then track them over time from their development to deployment stages. Individual regulatory compliance requirements can be configured to have ownerships, and what activity (aka check) needs to be carried out to satisfy the requirements. Seclea Platform has pre-configured compliance requirements and associated checks that compliance managers can tailor to their unique requirements.
Risk & governance manager
Seclea Platform allows risk & governance managers to specify the risks, their impacts and potential mitigations. Individual risk mitigations can be associated with automated or manual checks that are automatically triggered when the AI application reaches the specified stage. For example, model bias analysis can be configured as a check for a specific risk “Model training might result in a biased outcome”. Seclea Platform will automatically run the bias analysis at model training and report the results – which can be attached to the stated risks. If the results are acceptable, the risk is mitigated. Otherwise, additional actions might be required.
Ethics managers can use the Seclea Platform to monitor responsible AI practices and fairness assurances during the development and deployment of an AI application. Seclea provides ethics managers with the full provenance of all the activities carried out as part of an AI application development and their impacts. So they can make an informed decision about the practices carried out by the AI development and operations teams.
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