Managing Security, Privacy and Ethical Risks Associated with Big Data and Predictive Analytics Applications

Abstract

Big data analytics offers valuable insights but also poses risks surrounding security, privacy, and ethics that must be addressed. This research reviews the nature of big data and predictive analytics, then analyzes key security risks including increased attack surfaces, data breaches, weak access controls, and poor encryption. Technical security controls are proposed such as network segmentation, role-based access, encryption, API security, data auditing, and penetration testing. Privacy risks are also examined, including personal data exposure, behavioral profiling, lack of consent, and erosion of anonymity. Privacy controls include consent, anonymization, data minimization, and transparency about practices. On the ethics front, issues like opacity of models, flawed data, reinforcing bias, discrimination, manipulation of users, and loss of human discretion are discussed. Ethical principles are suggested, including explain ability, fairness, accountability, empowerment, auditability, and human oversight of analytics. Tables summarize the security, privacy, and ethics controls. In conclusion, while big data analytics delivers value, managing the accompanying security, privacy and ethical risks is crucial for its responsible use. Diligent technical and policy measures on security, consent-based privacy, and ethical oversight will allow organizations to harness big data analytics in a socially beneficial way.