AI Sensitive Information Exposure Assessment
Understand where employee use of generative AI may expose confidential, personal or commercially sensitive information — and what controls can realistically reduce that risk.
Generative AI can create an information-exposure channel that sits outside traditional data-loss assumptions. Employees may paste text, upload documents or connect tools containing personal data, client information, intellectual property, investigation material or other confidential content. The assessment starts by mapping approved and shadow AI use, relevant business processes and the information types most likely to be exposed.\n\nR2N Partner reviews the risk across three layers: governance and policy, employee behaviour and technical controls. The work can include interviews, controlled use-case testing, review of data classification and AI policies, assessment of upload and prompt scenarios, and evaluation of available monitoring or restriction mechanisms.\n\nThe objective is not simply to prohibit AI. It is to distinguish acceptable use from situations where the sensitivity, retention requirements or business impact of the information requires stronger safeguards. The output can include prioritised exposure scenarios, control gaps, preventive measures, detection logic and response procedures for suspected disclosure.\n\nWhere appropriate, the assessment can support the design of a practical Assess → Prevent → Detect → Respond operating model, giving management a proportionate way to govern AI use without treating every scenario as a conventional cybersecurity incident.