Information Security, Privacy, Data, and AI Model Governance

Data flows into every compliance function. How your organization collects, controls, shares, and protects that data is increasingly central to how examiners evaluate your program. As AI-driven processes become embedded in lending, fraud detection, and customer decisioning, regulatory expectations now extend beyond traditional information security into privacy, data governance, and model risk. For banks, fintechs, and crypto companies, these four disciplines work together to form a single governance layer within your Compliance Management System.

Information security covers how you protect sensitive data from unauthorized access, breaches, and leakage. This includes access controls, encryption, network security, endpoint protection, and incident response. Examiners expect documented policies that define who can access what, how access is granted and revoked, and what happens when a breach occurs.

Privacy governance addresses how your organization collects, uses, stores, and shares personal information in compliance with applicable regulations. This includes consumer disclosures, consent management, data retention policies, and the rights of individuals to access or delete their data. As privacy regulations evolve, examiners are looking for documented programs that go beyond a posted privacy policy.

Data governance is the framework for managing data quality, classification, lineage, and lifecycle across your organization. It answers the questions: What data do we have? Where does it live? Who owns it? How long do we keep it? Strong data governance supports every other compliance function, from monitoring and testing to complaint management and reporting, by making sure the underlying data is accurate, accessible, and controlled.

AI model governance covers the controls around how your organization develops, validates, deploys, and monitors AI and machine learning models used in regulated processes. This includes model risk management, bias testing, explainability documentation, ongoing performance monitoring, and clear accountability for model outputs. As regulators increase their focus on algorithmic decision-making, examiners expect documented evidence that models are tested, validated, and subject to the same rigor as any other compliance control.