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Stop risking problems with your AI data processing. There are three main legal bases to use. When you feed personal data into your AI system, the GDPR requires you to have a legal basis. Three key options stand out: 1. Consent Obtain clear, informed consent from users before using their personal data. They need to know exactly how their data will be used—and they must actively agree to it. This is a good starting point when training AI models after deployment. 2. Contractual Necessity You may use personal data when it’s necessary for a contract with the user. For example, if a customer signs up for an AI-driven service, you might need their data to personalize or fine-tune your system to meet their requirements. 3. Legitimate Interests This one’s all about balance. Your interest in using data should not override the users' rights and interests. An example: deploying an internal AI tool that enhances company-wide productivity must balance company needs and employee data protection. 🔎 Which legal basis best fits your AI use?   Here's a quick checklist of what to consider:   - Assess which legal basis fits your specific use case.   - Document and justify why it's the right choice.   - Ensure regular reviews of your data processing activities to ensure they keep respecting data protection principles. 🔒 Compliance isn’t just about avoiding penalties—it’s an opportunity to build trust and gain a competitive edge. Want to turn GDPR compliance into a competitive advantage? Let’s talk about how to make that happen. Which legal basis do you find most challenging for your AI projects? Drop a comment below!

🚀 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗔𝗜/𝗠𝗟 𝗥𝗶𝘀𝗸𝘀 𝘄𝗶𝘁𝗵 𝗚𝗥𝗖: 𝗔 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 The rise of AI and Machine Learning (ML) has transformed industries, unlocking new efficiencies and insights. But with these advancements come new risks that enterprises must address proactively. Here are some key risks associated with AI/ML adoption and how Governance, Risk, and Compliance (GRC) frameworks can play a pivotal role in mitigating them: 𝗧𝗼𝗽 𝗔𝗜/𝗠𝗟 𝗥𝗶𝘀𝗸𝘀: 1. Algorithmic Bias Example: A recruitment AI tool trained on biased datasets unintentionally favors certain demographic groups over others. 2. Data Privacy Violations Example: AI-powered marketing tools over-collect customer data, breaching GDPR or other data protection laws. 3. Regulatory Non-Compliance Example: Financial institutions leveraging AI for fraud detection risk non-compliance if their systems lack transparency and auditability. 𝗛𝗼𝘄 𝗚𝗥𝗖 𝗛𝗲𝗹𝗽𝘀 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗲 𝗧𝗵𝗲𝘀𝗲 𝗥𝗶𝘀𝗸𝘀: 🔹 Bias Detection and Ethical Audits Implementing periodic AI model audits to identify and correct biases. GRC ensures that ethics and fairness remain at the forefront of AI development. 🔹 Data Governance Policies GRC frameworks enforce data privacy standards and ensure compliance with evolving regulations like GDPR, HIPAA, or CCPA. 🔹 Transparency and Accountability Enabling a clear chain of responsibility with audit trails and real-time monitoring ensures regulatory compliance and stakeholder trust. 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝗱𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 At Integrade Solutions, we specialize in crafting tailored GRC frameworks that align with your organization’s AI strategy. Whether it’s compliance audits, policy design, or risk assessments, we ensure your AI adoption is both innovative and secure. AI adoption doesn’t have to be a leap of faith—with GRC, it becomes a structured and sustainable journey. #GRC #AIML #Governance #RiskManagement #Compliance #IntegradeSolutions #TechRisks

The EU formally adopted the AI Act last week with potential for GDPR sized fines of up to 6% of global revenue. What does it mean for businesses building AI based tools? There was fear that the EU would regulate away innovation in the space. I don’t see that the Act will result in meaningfully lessen innovation out of the EU and am happy to see the extra controls on certain use cases with material potential for harm of individuals. The Act itself is fairly common sensical and most businesses won’t see much in the way of direct impact to their strategy or implementation unless they’re working in the areas defined as either unacceptable or high risk which I'll touch on a bit here: ☠️ Unacceptable risk is fairly narrowly scoped. Your product is unlikely to be classified here unless your TAM includes authoritarian regimes requiring assistance suppressing the proles. That leaves high risk AI systems as the main area with requirements. These would be systems like those described below: 🛫 Safety systems based on AI - Autopilot, either the real kind or the “Definitely need you to still keep your eyes on the road” type means you’re high risk. Industrial automation AI that monitors for safe levels could be another case. 🕵️ Systems that profile individuals automatically to assess aspects of a person’s life are high risk due to the potential impact of mistakes. 🧑⚖️ Legal assistance - assessing evidence reliability, assessing individuals, profiling, any uses involving interpreting the law or immigration assessments. It should go without saying that unattended ChatGPT is an awful defense counsel. 🏗️ Critical infrastructure - If your system controls the flow of water, electricity or cars, you’re in! 🧑💼 Employment and education - AI powered admissions, hiring, firing, promoting and deciding who gets the best snacks based on performance are all high risk. If your product fits any of the above high risk use cases, you have some work to do. Below are some of the major points: 📋 Risk management (Article 9)- You need a continuous process to assess and address the risk that the product potentially poses, including cases of misuse. NIST AI RMF anyone? 🏛️ Data governance (Article 10)- Do you know where your data came from? Thought through potential biases? Are your training and validation sets sufficient to identify bias? Can you prove it? 🪵 Auditability and logging (Article 12)- Can you show why your system made the decision it did? Are you holding those logs for long enough (6 months!) Is there human oversight? Do you have an escalation path back to the vendor in case of issues? 👁️ Oversight capabilities (Article 14)- Can humans tell what the system is doing and why? 🔒 Security (Article 15)- It says you need to secure it appropriate to the risk. What a terrific idea! Overall, I expect this to have a much lower impact than something like GDPR with more restraint on the part of the EU to focus on what's important. #ai #regulation

Have you noticed how, in the AI space, approaches to data protection can sometimes feel a bit... underwhelming? Whether it’s being brushed off entirely or treated like a checkbox exercise, it’s a challenge many face. But here’s why it’s worth rethinking. Doing the bare minimum for data protection is like building a house with holes for doors: it might look fine at first, but sooner or later, some problem will wander in uninvited. When you prioritize privacy, however, you make sure there are doors to keep them out. Consider these advantages: 1️⃣ Trust is currency: Whether it’s customers, partners, or regulators, people notice when you play fair. By being transparent and accountable, you’ll stand out as the provider they want to rely on. 2️⃣ Laws evolve; principles last: Setting up compliant processes today can save you from scrambling later. (The EU AI Act, for example, is already bringing changes. Are you prepared?) 3️⃣ Stand out by standing up for privacy: In a world where buyers are becoming more privacy-savvy, your data protection practices can be your differentiator. Think of it like a playlist: if others are playing the same old tracks, is your playlist the one with the hits people want to hear? If you’re wondering where to start, one approach to consider is dividing your AI lifecycle into two phases: ⚙️ Development: When training your AI model with large datasets, the direct impact on individuals may seem low, but compliance still matters if personal data is involved. 🤖 Deployment: This is when you start using AI, whether for simple things like chatbots or more complex tasks like automated decisions and customer profiling. You need to pay attention to what data you use and for what. Treating these phases separately can give you greater control, help you match the applicable legal basis for data processing to each phase, and build a foundation for trust and scalability. Of course, this is just one perspective. There are plenty of ways to tackle these challenges. What’s worked for you? How do you handle data protection in your AI lifecycle? I’d love to hear your thoughts and experiences. By taking intentional, thoughtful steps, you’re not just staying compliant, you’re setting yourself apart in the AI market!

With the EU AI Act around the corner, AI regulations are about to shift in a big way. Many businesses see this as a challenge, but companies that start preparing now could gain a critical edge over the competition. Here’s why early preparation matters: 1. Compliance Builds Trust Starting compliance work now doesn’t just demonstrate commitment to ethical AI use—it also builds trust with customers and partners. Transparency and ethics are becoming increasingly important for consumers, and companies that lead in these areas can build a stronger reputation. 2. Optimize Processes & Manage Risks The Act will require ongoing monitoring, data management, and risk evaluation for AI systems. Businesses that act early will have the time to optimize their processes and manage risks more effectively. This reduces the risk of fines and makes AI systems more reliable and efficient. 3. Stay Ahead of the Competition New regulations can be costly and time-consuming to implement, especially last-minute. Companies that invest in compliance early will be a step ahead of competitors that wait—and will enjoy a smoother transition to meet the new standards. Bottom line: The EU AI Act may seem distant, but taking action now can not only protect your business but set it apart. Make compliance an opportunity and strengthen your competitive edge by starting early! Here's the link to the full article: https://buff.ly/3NO7Jje Curious about how your business can be ready for the EU AI Act? Share your questions or insights below! 👇

Compliance Self-Evaluation Framework for Limited-Risk AI Models in the EU Objective: Ensuring adherence to the EU AI Act for limited-risk AI deployments. Scope: Applicable to companies within the EU utilizing limited-risk AI technologies. Definitions: AI System (AIS): Software generating outputs like predictions, influencing users or environments. High-Risk AI System (HRAIS): AIS that significantly impacts individual rights. Limited-Risk AI Model: AIS not classified as HRAIS. Provider & Deployer: Entities developing or using AIS under their brand. Framework Steps: Risk Assessment Determine if your model is HRAIS or limited-risk. Consider use, impact, and oversight. Legal Compliance Align with EU AI Act mandates: data governance, transparency, robustness, privacy, and more. Internal Controls Implement policies, training, and monitoring to uphold compliance. External Audit Engage third-party auditors to validate adherence to regulations. Continuous Improvement Regularly update your framework to match evolving EU standards. Benefits of Compliance: Minimize legal risks. Boost reputation and trust. Enhance operational efficiency and competitive edge in the EU. Conclusion: Adopting this framework helps ensure ethical and compliant AI use, safeguarding your company’s standing and success in the EU market. 🔄 Feel free to share this framework with your network! 💡Tip: Regular updates and external audits are crucial for keeping pace with regulatory changes. What strategies do you use to ensure compliance in your AI deployments? Share your thoughts below!

7 key points you need to know to develop AI in compliance with the GDPR❗❗ The French Data Protection Authority (CNIL) published its recommendations on how to comply with GDPR when developing AI: You need to: 1️⃣ Define Clear Objectives Set a clear objective and purpose for your AI system's goal from the start. 2️⃣ Determine Roles and Responsibilities Are you a data controller, joint controller, or processor? Know your GDPR obligations. 3️⃣ Establish a Lawful Basis for Processing Document the legal basis (e.g. consent, contract, legitimate interests) that allows you to process personal data for training the AI model. 4️⃣ Minimize Personal Data Usage Only use the minimum personal data necessary for the purpose. Implement techniques like anonymization, pseudonymization, or synthetic data generation where possible. 5️⃣ Conduct Data Protection Impact Assessments (DPIAs) Conduct a DPIA to control risks for high-risk AI systems dealing with sensitive data. 6️⃣ Data Governance and Accountability Adopt data protection by design and by default, implement strong data governance measures. 7️⃣ Transparency and Explainability Be transparent about the AI system's methodology, training data sources, and potential limitations or biases. Easy? Cumbersome? Confusing? Let me know in the comments! Link to the recommendations is in the first comment. Please share this post with your network and let's empower more people to develop AI responsibly.🙏 ——————————————————- 🙋♀️ I'm Tanya, Founder of Privacy Rules. ⚡ I help companies maintain the highest standards of privacy. Like this post? Want to see more? Follow / connect with me 🔔

Is the future of supply chain due diligence AI-powered? AI has irrevocably changed the workplace with its ability to streamline the automation of repetitive tasks, analysing large data sets, predictive analytics and workflow optimisations. These applications, when employed adeptly can save significant time and resources, drive productivity and unlock business efficiencies. However, the question arises: Is it able to deliver defensible due diligence? If not, how can this be achieved? 🌎 Over the last couple of years, a raft of sustainability and human rights legislation has been enacted that has profoundly impacted the way in which organisations operate globally. The German Supply Act (LkSG), EU Deforestation Regulation (EUDR) and Uyghur Forced Labor Prevention Act (UFLPA), to name but a few, require organisations to provide irrefutable evidence that their supply chains are forced labour-free or products ethically sourced.  📌 The penalties for non-compliance are significant. Over $1.4Bn worth of goods was detained at the U.S. border in 2023 for being suspected of foul play (Source: CBP) and the fines for non-adherence to the LkSG can go up to 2% of annual revenue (Source: BMAS). AI can, of course, assist in mapping the provenance of a product using publicly available records and open source data, but when the stakes are so high for getting it wrong, can you really trust that publicly available data will ensure compliance? Defensible due diligence and peace of mind requires irrefutable and verified evidence. 💡 AI is best suited when managed and augmented by a human hand. At SUPPLIERASSURANCE, we use human validation to prevent potential mistakes and authenticate documentation - in 2023 alone, our in-house team validated almost 90,000 SAQ responses. By adopting a comprehensive approach to supply chain mapping and due diligence, that comprises both human validation and continuous sustainability improvement, together we can deliver the demonstrable legislative compliance that AI falls short of and work towards a more sustainable and ethical future. Head to SUPPLIERASSURANCE to learn more about how our platform can help you meet the demands of evolving global supply chain legislation: https://lnkd.in/ezm-K5q9

With just past our half year mark, thought I would share some insights I've been gathering on the legal developments around the hangover buzzword, #AI: Regulatory Landscape Divergent approaches among global leaders will mark the regulatory landscape for AI in 2024 despite calls for a unified strategy. The Bletchley Declaration and G7 commitments highlight the desire for coordinated regulation. However, individual countries are likely to continue developing their own AI regulations. EU Initiatives EU is expected to advance its AI Act and AI Liability Directive, to create a comprehensive framework for AI governance. Liability Directive will address legal challenges related to AI-induced harm. UK Developments UK will continue refining its AI regulatory approach, emphasizing innovation and safety. Key initiatives include the AI Safety Summit. International Initiatives International collaboration on AI governance will be crucial, with various countries contributing to developing standards and best practices. The OECD and UNESCO are actively working on AI ethics and governance frameworks. Commercial Sector AI adoption here will accelerate, driven by advancements in automation, data analytics, and ML. Data Protection Remains a critical concern, with regulations like the GDPR influencing AI development. Organizations must prioritize data privacy and security. Dispute Resolution AI's role is expanding, offering new tools for legal professionals. Analytics and predictive modeling can enhance case management and decision-making, although ethical considerations and transparency will be paramount. Employment Impact on employment will be significant, with opportunities and challenges. Automation and tools may lead to job displacement, necessitating reskilling initiatives, and policy interventions to support the workforce. ESG AI can contribute to ESG goals by enabling better data analysis and decision-making. However, ethical AI development and usage will be crucial to avoid biases and ensure a positive social impact. Financial Services The sector will see increased AI integration for fraud detection, risk management, and customer service. Regulatory compliance and ethical considerations will be essential to maintain trust and security. IP&IT AI-driven innovation will challenge existing IP frameworks. To protect IP effectively, legal professionals must stay abreast of developments. Generative AI These technologies, such as LLMs, will continue evolving, offering new content creation and problem-solving possibilities. Legal frameworks must adapt to address potential misuse and IP issues. Practice Compliance Management AI tools for compliance management will become more sophisticated, helping organizations meet regulatory requirements efficiently. Public Sector AI adoption in the public sector will enhance service delivery and operational efficiency. Governments must ensure transparency, accountability, and ethical use of AI in public services.

Leading the way in EU AI Act Compliance and Data Governance! 🚀    In this pioneering role, we have now even been mentioned in the 2024 Gartner® Top Whitespace Opportunities in GenAI Services for Tech CEOs Report. 🙌   We believe that our commitment, values and innovative approaches have made us a mention by Gartner. As the go-to specialists, we understand the complexities and challenges companies face with the EU AI Act.  In addition to our leadership in AI compliance and data governance, our partnership with MULTIPLAI has enabled us to further enhance our offerings. This collaboration underscores our commitment to providing comprehensive, end-to-end solutions that address the unique needs of our clients in navigating the complexities of AI regulation.   Work with the recognized number one in tailored services to ensure your company not only understands but excels in implementing the necessary regulations.    For an in-depth understanding, don’t miss our whitepaper on the EU AI Act, which covers all you need to navigate this regulation. Dive into it here: https://lnkd.in/e_3kyAK9 Check also casebase.ai, a solution designed to simplify AI adoption while ensuring full compliance with the EU AI Act. Casebase not only integrates seamlessly with existing IT infrastructures but also provides a platform for managing AI governance, risk, and compliance. 💡