The recent advances in large language models (LLMs) have significantly expanded their applications across various fields such as language generation, summarization, and complex question answering. However, their application to privacy compliance and technical privacy reviews remains under-explored, raising critical concerns about their ability to adhere to global privacy standards and protect sensitive user data. This paper seeks to address this gap by providing a comprehensive case study evaluating LLMs' performance in privacy-related tasks such as privacy information extraction (PIE), legal and regulatory key point detection (KPD), and question answering (QA) with respect to privacy policies and data protection regulations. We introduce a Privacy Technical Review (PTR) framework, highlighting its role in mitigating privacy risks during the software development life-cycle. Through an empirical assessment, we investigate the capacity of several prominent LLMs, including BERT, GPT-3.5, GPT-4, and custom models, in executing privacy compliance checks and technical privacy reviews. Our experiments benchmark the models across multiple dimensions, focusing on their precision, recall, and F1-scores in extracting privacy-sensitive information and detecting key regulatory compliance points. While LLMs show promise in automating privacy reviews and identifying regulatory discrepancies, significant gaps persist in their ability to fully comply with evolving legal standards. We provide actionable recommendations for enhancing LLMs' capabilities in privacy compliance, emphasizing the need for robust model improvements and better integration with legal and regulatory requirements. This study underscores the growing importance of developing privacy-aware LLMs that can both support businesses in compliance efforts and safeguard user privacy rights.

Machine learning is much more than a buzzword -- it has become a major player for many businesses. More and more companies are implementing machine learning and other AI tools to supplement or streamline their activities. This is especially true after the Covid-19 pandemic accelerated the adoption of machine learning. The way that your company implements machine learning can have a direct impact on its performance in the year ahead, especially as AI tools become utilized in a broader range of business activities. By understanding the areas where machine learning is poised to have the greatest impact, you can move proactively to adopt these tools for your own entrepreneurial efforts.

Business-software maker SAP SE this month plans to start offering a partner company's artificial-intelligence software to help customers comply with data-privacy regulations, including the sweeping California law that takes effect in January. Germany-based SAP, which makes software that supports core corporate functions such as accounting, supply chains and human resources, said Monday that it would resell privacy-management software from BigID Inc., a startup with headquarters in New York and Tel Aviv.

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. On 26 October 2016, I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will not own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. On 26 October 2016, I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will not own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will not own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. On 26 October 2016, I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will now own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".