PIERSTONE | Brussels’ Post

One week on, I remain deeply inspired by the discussions held at #CPDP2024 regarding AI, privacy, and regulatory frameworks. Make sure to explore our insights!

𝙐𝙣𝙡𝙤𝙘𝙠𝙞𝙣𝙜 𝙩𝙝𝙚 𝙃𝙞𝙙𝙙𝙚𝙣 𝙋𝙤𝙩𝙚𝙣𝙩𝙞𝙖𝙡 𝙤𝙛 𝘿𝙖𝙩𝙖 𝙞𝙣 𝘼𝙄: 𝙈𝙤𝙫𝙞𝙣𝙜 𝘽𝙚𝙮𝙤𝙣𝙙 𝘾𝙤𝙢𝙥𝙡𝙞𝙖𝙣𝙘𝙚 𝙩𝙤 𝙎𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙘 𝘼𝙙𝙫𝙖𝙣𝙩𝙖𝙜𝙚 "In today's AI-driven world, most organizations are focused on compliance—ensuring that their AI systems meet data privacy regulations like GDPR. But what if we looked beyond compliance and started treating data privacy as a strategic asset rather than just a requirement? The key is 𝗗𝗮𝘁𝗮 𝗔𝗻𝗼𝗻𝘆𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻—an often underutilized but powerful technique that allows organizations to leverage user data for insights and innovation while maintaining privacy. When done right, anonymization can unlock new business opportunities without compromising user trust or running afoul of regulations. Here are three under-the-radar benefits of data anonymization that could change the way we think about AI and privacy: 1. 𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗛𝗶𝗱𝗱𝗲𝗻 𝗗𝗮𝘁𝗮 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Anonymized data can be used for advanced analytics without violating privacy, enabling new product development and customer insights. 2. 𝗖𝗿𝗼𝘀𝘀-𝗕𝗼𝗿𝗱𝗲𝗿 𝗗𝗮𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗥𝗲𝘀𝘁𝗿𝗶𝗰𝘁𝗶𝗼𝗻𝘀: Since anonymized data is no longer personal data, it can be shared globally without triggering strict compliance barriers. 3. 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Leveraging larger, anonymized datasets allows for training more accurate and diverse AI models while protecting user privacy. Don't just comply with regulations—use privacy as a way to innovate, differentiate, and stay ahead of the curve in AI. #𝗔𝗜 #𝗗𝗮𝘁𝗮𝗔𝗻𝗼𝗻𝘆𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 #𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 #𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 #𝗔𝗜𝗣𝗿𝗶𝘃𝗮𝗰𝘆"

🚀 Balancing Innovation with Data Privacy in AI 🛡️ As businesses dive deeper into AI innovation, the spotlight on data privacy grows brighter. Regulations like GDPR and CCPA are reshaping how we handle personal information, emphasizing the need for consent and protection. 🤝 To navigate this landscape, safe data collection and ethical AI practices are non-negotiable. By weaving privacy into the fabric of AI systems from the get-go, conducting regular audits, and staying compliant, businesses can thrive while safeguarding data. 🔒 Success stories underscore the value of building trust through transparent data practices. The key? Making privacy a top priority throughout the AI development journey. 🌟 In a world where data is king, mastering the delicate dance between innovation and privacy is the key to unlocking AI's full potential. Are you ready to lead the wa https://lnkd.in/g6FiiH47? 💼 #AIDataPrivacy #Innovation #EthicalAI

🔐🌐 Balancing Information Privacy and the Power of AI! 🤖💪 As AI and machine learning technologies continue to advance at a rapid pace, they are fundamentally challenging the core principles of information privacy that have long stood as the foundation of data protection. 🚀🔒💻 The three pillars of privacy - 🌟 collection limitation 🌟purpose specification 🌟use limitation - are being put to the test by the insatiable data appetite of AI systems. 📚🔍🤔 Mass data collection, vague privacy policies, and blurred lines between primary and secondary uses are creating a crisis of transparency and consent. 😨🙅♂️🔍 Yet, AI also brings potential solutions. Preference-based privacy models, explainable AI, and heightened accountability could revolutionize how we uphold privacy in the digital age. 🌟⚙️🔒 But the path forward remains unclear. ⏳🔎❓ Will AI's disruption of privacy principles lead to a degradation of privacy protections, or spur a new era of user-centric, privacy-preserving technologies? The future of the individual's right to privacy hangs in the balance. ⚖️🔐🌍 #AI #DataPrivacy #InformationPrivacy #MachineLearning #GDPR #Transparency #DataEthics 🤖🔒🔍

Key takeaways from the inaugural IAPP - International Association of Privacy Professionals #AIGG23 Conference on AI Governance: - Governance and operational tools that enable both demonstrability, measurement, and data/model development and disgorgement are not broadly available (yet). There seems to be explicit acknowledgement that data and/or model disgorgement will be a real, ongoing operational challenge. - AI inventories will be just as important and a complement to data maps, data inventories and records of processing. - AIAs and DPIAs are increasingly seen as important and necessary risk mitigation tools. - Consensus on #nist AI Risk as the framework to build around (at least in North America). - Consensus that the #euaiact, once finalized, will be highly influential and tightly woven into the EU #gdpr , while acknowledging that a lot will change between now and a 2026 effective date. - Any company that sells to or is "plugged into" the USGov will be expected to meet the U.S. EO on Safe, Secure and Trustworthy AI, passed down as procurement standards. Others will see that as the bar to meet. - The The Information Accountability Foundation's work on AIAs and demonstrable accountability supports data sustainability and business resiliency, fundamental to the responsible development of models and deployment of AI. #privacy #accountability #dataprotection #ai

The integration of AI into business processes is a double-edged sword. On one hand, it offers unprecedented opportunities for efficiency, innovation, and customer engagement. On the other hand, it introduces significant challenges in ensuring data privacy and compliance with evolving regulatory frameworks. The use of machine learning and generative AI models, which require processing vast amounts of personal data, raises concerns about the security and privacy of consumer information. These concerns are exacerbated by the complexity and opacity of AI models, making it difficult for organizations to guarantee compliance with stringent data privacy laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and others globally. The rapid development and deployment of AI technologies further complicate this issue, potentially outpacing the ability of organisations to implement necessary privacy controls. This situation calls for a proactive approach to privacy engineering, including the adoption of explainable AI (XAI) and privacy-by-design principles. XAI can help demystify AI decision-making processes, making it easier to identify and address potential privacy issues. Privacy-by-design, a principle that calls for privacy to be considered throughout the technology development process, ensures that privacy controls are integrated into AI systems from the outset. To address these challenges, organisations should invest in upskilling their workforce to understand and implement advanced privacy controls and compliance measures. Additionally, adopting a secure-by-design model, which includes practices like zero-trust architecture and shifted-left security, can help in proactively identifying and mitigating security and privacy risks early in the development process. By taking these steps, organisations can not only ensure compliance with current data privacy laws but also build trust with their customers, a critical asset in the digital age. #ArtificialIntelligence #DataPrivacy #ExplainableAI #PrivacyByDesign #ZeroTrustArchitecture #ShiftedLeftSecurity #GDPR #CCPA

As the AI regulatory debate continues, it’s no secret that it’s front and center for data protection authorities. Across the globe, complex questions and compliance challenges are arising for organizations seeking to assess their AI technologies against the current data privacy frameworks. In our latest expert analysis for Law360, co-authors Eduardo Ustaran, Alyssa Golay, and Mark Brennan unpack the key areas data protection regulators are focused on and how today’s AI revolution is testing existing privacy concepts and principles. Click here for the full article: https://lnkd.in/gbtuyw5b Follow Hogan Lovells on LinkedIn to catch our next update on AI trends across jurisdictions and sectors. Visit our interactive AI Hub for more resources and news in this space: https://lnkd.in/epX3gqjw #AI #ArtificialIntelligence #Privacy #DataProtection

Here are my takeways from last week's IAPP Conference on AI Governance.

This is a very insightful article that looks at the data privacy considerations that should be looked at when businesses decide to use AI systems in their operations. #dataprotection #dataprivacy

As AI becomes commonplace in our daily lives, the Personal Data Protection Commission (PDPC) has released a set of guidelines on the use of personal data in AI systems to strike a balance between the development of AI systems and the protection of consumer rights to privacy, effective 1 March 2024.   In today’s digital economy, vast amounts of data are required for AI analytics making it impractical for organisations to seek consent for every new purpose. The PDPA allows for meaningful consent in such cases. Find out how your business can safely use personal data in the development of AI systems with consumer consent, and about the exceptions from consent in our overview of the new guidelines. If you need clarification on how these guidelines apply to your business or want to know if you can rely on an exception for your use case, please reach out to us for a discussion. Key Contact Managing Director Samuel Yuen #AI #DataProtection #Privacy #PDPC #BusinessCompliance #AIGuidelines #PersonalData #DigitalEconomy #SingaporeLaw #LegalUpdate