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6 Ways That Information Governance Can Improve AI Compliance

Information governance plays a critical role in ensuring that the artificial intelligence (AI) that you develop is created and used in a compliant and responsible manner. Here are 6 ways that information governance can improve AI compliance:

Data quality management:  Companies using AI models must ensure that the data that they use to train these models are accurate, complete, and reliable – and failing to do this can create both legal and practical risks.  IG tools such as uniform categorization of data, data quality controls, and eliminating excess “junk” data help to promote the use of high-quality data.

Data protection and security: Information governance can help ensure that sensitive data used for AI analysis is appropriately protected and secured. IG includes implementing access controls to suppress sensitive information or personal data that you no longer need (but are required to keep because of retention laws), which can help to shield you from potential liability.

Ethical and legal compliance: Information governance is a lifestyle that includes regular maintenance and data quality, privacy, and retention obligation audits. In the context of AI, these tools help to promote and sustain the success of AI models by ensuring that the information that they use complies with applicable law.

Privacy: Information governance helps to protect records throughout their entire lifecycle and can help ensure that personal data used for AI analysis is handled (and disposed of) in accordance with applicable privacy laws and regulations.

Transparency: Information governance tools can help companies to create the types of policies and training mechanisms that help to ensure that AI decisions are transparent and explainable and help ensure that AI models are properly documented, understood, and communicated.

Risk management: Information governance can help companies to ensure that AI-related risks are identified and managed appropriately. Relevant IG tools include implementing risk management and disaster recovery processes, such as risk assessments and risk mitigation plans, to ensure that AI-related risks are properly identified and addressed.


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