What are the most effective data access control policies for machine learning in contract management?

1 Define data roles and permissions

One of the first steps to implement data access control policies for ML is to define the roles and permissions of different users and groups who interact with the data. For example, you may have data owners, data stewards, data analysts, data scientists, and data consumers. Each role should have a clear description of its responsibilities, rights, and obligations regarding the data. You should also establish a data governance framework that sets the rules and standards for data quality, security, privacy, and ethics.

2 Encrypt and anonymize data

Another important policy for data access control is to encrypt and anonymize the data that is used for ML. Encryption is the process of transforming data into a code that can only be deciphered by authorized parties with a key. Anonymization is the process of removing or masking any identifying or sensitive information from the data, such as names, addresses, or social security numbers. Both encryption and anonymization can help protect the data from unauthorized access, leakage, or misuse.

3 Use differential privacy techniques

Differential privacy is a technique that adds noise or randomness to the data or the ML model to preserve the privacy of individual data points. For example, if you want to use ML to analyze the performance of different contracts, you can use differential privacy to prevent revealing the details of any specific contract or party. Differential privacy can also help prevent attacks that try to infer sensitive information from the ML model outputs or queries.

4 Implement data auditing and logging

Data auditing and logging are policies that track and record the data activities and transactions for ML. For example, you can use data auditing and logging to monitor who accessed, modified, or shared the data, when and why they did so, and what impact they had on the data quality or integrity. Data auditing and logging can help ensure accountability, transparency, and compliance for ML. They can also help detect and prevent data breaches, errors, or anomalies.

5 Adopt ethical principles and standards

Ethical principles and standards are policies that guide the design, development, and deployment of ML for contract management. For example, you can adopt ethical principles and standards that promote fairness, accountability, transparency, and human dignity for ML. You can also follow industry best practices, regulations, and codes of conduct that address the ethical implications of ML for contract management. Ethical principles and standards can help ensure that your ML models and data are aligned with your values, goals, and stakeholders.

6 Train and educate data users

Training and education are policies that aim to increase the awareness, knowledge, and skills of the data users who work with ML for contract management. For example, you can train and educate your data users on how to use the ML tools and platforms, how to interpret and communicate the ML results, and how to handle and protect the data. You can also educate your data users on the data access control policies, the data roles and permissions, and the ethical principles and standards for ML.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?