Privacy Risks of Corporate Data Collection in the Age of Agentic Artificial Intelligence (AI): A Qualitative Document Analysis of Security and Individual Autonomy

Abstract

Agentic artificial intelligence (AI) changes corporate data collection because automated systems can retrieve, interpret, combine, and act on information across digital environments with reduced human intervention (National Institute of Standards and Technology [NIST], 2023, 2024). This qualitative document analysis examines how selected corporate privacy and AI governance documents frame data collection, security, autonomy, consent, retention, and user control (Bowen, 2009; Saldaña, 2021). Public privacy policies, AI principles, responsible AI standards, data processing addenda, and acceptable use documents from technology companies were reviewed as qualitative data because corporate documents can function as organizational evidence (Bowen, 2009; Microsoft, 2022; Salesforce, 2023). The analysis used purposive document sampling and descriptive coding to identify patterns in how organizations justify data collection while describing safeguards for privacy and security (Creswell & Creswell, 2023; Saldaña, 2021). Findings suggest that corporate documents often connect data collection to service delivery, safety, fraud prevention, system performance, and responsible AI, while public disclosures vary in detail about automated action and user remedies (Federal Trade Commission [FTC], 2024; NIST, 2024). The paper contributes a concise qualitative framework for reviewing public corporate documents as evidence of privacy risk governance in agentic AI environments (Bowen, 2009; NIST, 2020, 2023).