Cyber, Privacy, Risk & Compliance Insights

Organizations should include AI in cyber risk assessments by identifying approved and unapproved AI use, mapping sensitive data flows, evaluating access controls and reviewing third-party tools and models. Assessments should consider data leakage, prompt injection, model manipulation, insecure integrations, identity and permission risks, and AI-enabled threats. Findings should inform prioritized controls, responsible owners, testing and ongoing monitoring as technologies and use cases change.

An AI governance program should define accountable leadership, decision rights, policies and risk-based controls across the AI lifecycle. Core elements typically include an inventory of AI systems and use cases; risk classification; data privacy, security and quality requirements; model testing and validation; human oversight; transparency and documentation; third-party due diligence; regulatory monitoring; incident and escalation procedures; workforce education; and ongoing performance monitoring. Governance should be proportionate to each use case and integrated with existing risk, compliance, privacy, cybersecurity and data governance programs.

Organizations should govern AI through a cross-functional, risk-based framework that connects business objectives with clear accountability and practical controls. Begin by identifying where AI is used, who owns each use case and what data and decisions are involved. Apply stronger review to higher-impact uses, document approvals and limitations, test systems before deployment and monitor them throughout their lifecycle. Legal, privacy, security, compliance, technology, audit and business teams should coordinate so responsible AI requirements become part of day-to-day operations rather than a stand-alone policy.

A data privacy program should establish clear accountability for how personal information is collected, used, shared, retained and deleted. Core elements typically include data inventories and mapping, privacy notices, consent and preference management, data subject rights processes, privacy impact assessments, third-party oversight, security safeguards, incident response, employee training and regulatory monitoring. Organizations should integrate privacy into product, technology and business processes from design through retirement.

Organizations should evaluate what personal and sensitive data AI systems access, why the data is needed and whether its use is permitted and transparent. Controls may include data minimization, access restrictions, retention limits, vendor due diligence, privacy impact assessments, human oversight and testing for inappropriate disclosure or bias. Privacy teams should participate throughout the AI lifecycle, including use-case approval, development, deployment, monitoring and retirement.

Enterprise risk management helps organizations evaluate cyber, privacy and AI risks alongside strategic, operational, financial and compliance priorities. An effective ERM approach defines risk ownership, appetite and escalation criteria; assesses likelihood and business impact; and connects mitigation plans to resources and decision-making. Integrating these technology risks into enterprise reporting can reduce siloed oversight, clarify interdependencies and help leaders prioritize actions based on their potential effect on business objectives.

Organizations should perform a comprehensive enterprise risk assessment at least annually and refresh it when material changes occur, such as acquisitions, new technologies, regulatory developments, market disruption, or significant incidents. Higher-priority risks should be monitored more frequently through indicators, scenario analysis, and management reporting. The process should capture emerging cyber, privacy and AI risks, test assumptions and update response plans, ownership and resource priorities as conditions evolve.