🔗 Privacy and Cybersecurity: The Intersection in AI
🔍 Key Insights: The intersection of privacy and cybersecurity in AI highlights technical and regulatory challenges.
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1️⃣ Privacy Risks in Data Processing
AI models are vulnerable to data poisoning, where manipulated inputs distort outputs and expose sensitive information. Flaws in data pipelines can propagate these risks, leading to privacy violations.
2️⃣ Generative AI and Privacy
Generative AI can re-identify individuals in anonymized datasets and produce synthetic data mimicking real identities, increasing risks like identity theft and fraud.
3️⃣ Cybersecurity as a Privacy Enabler
Strong technical controls, such as encryption and secure data pipelines, are essential to protect sensitive data throughout the AI lifecycle.
4️⃣ Unified Privacy and Security Regulation
Privacy laws often lack cybersecurity mandates, while security regulations overlook privacy implications. An integrated framework is needed to ensure joint accountability among stakeholders.
Thank you, Dmitrii Filatov for sharing this interesting study 🙏
🔗 Read the full paper “Artificial Intelligence and Privacy” on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4713111