Best Practices for Responsible AI and Data Privacy

#GRC Today I led a session focused on rolling out a new Standard Operating Procedure (SOP) for the use of artificial intelligence tools, including generative AI, within our organization. AI tools offer powerful benefits (faster analysis, automation, improved communication) but without guidance, they can introduce major risks: • Data leakage • IP exposure • Regulatory violations • Inconsistent use across teams That’s why a well-crafted SOP isn’t just nice to have .. it’s a requirement for responsible AI governance. I walked the team through the objective: 1. To outline clear expectations and minimum requirements for engaging with AI tools in a way that protects company data, respects ethical standards, and aligns with core values. We highlighted the dual nature of AI (high value, high risk) and positioned the SOP as a safeguard, not a blocker. 2. Next, I made sure everyone understood who this applied to: • All employees • Contractors • Anyone using or integrating AI into business operations We talked through scenarios like writing reports, drafting code, automating tasks, or summarizing client info using AI. 3. We broke down risk into: • Operational Risk: Using AI tools that aren’t vendor-reviewed • Compliance Risk: Feeding regulated or confidential data into public tools • Reputational Risk: Inaccurate or biased outputs tied to brand use • Legal Risk: Violation of third-party data handling agreements 4. We outlined what “responsible use” looks like: • No uploading of confidential data into public-facing AI tools • Clear tagging of AI-generated content in internal deliverables • Vendor-approved tools only • Security reviews for integrations • Mandatory acknowledgment of the SOP 5. I closed the session with action items: • Review and digitally sign the SOP • Identify all current AI use cases on your team • Flag any tools or workflows that may require deeper evaluation Don’t assume everyone understands the risk just because they use the tools. Frame your SOP rollout as an enablement strategy, not a restriction. Show them how strong governance creates freedom to innovate .. safely. Want a copy of the AI Tool Risk Matrix or the Responsible Use Checklist? Drop a comment below.

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

The European Commission and the European Research Area Forum published "Living guidelines on the responsible use of generative artificial intelligence in research." These guidelines aim to support the responsible integration of #generative #artificialintelligence in research that is consistent across countries and research organizations. The principles behind these guidelines are: • Reliability in ensuring the quality of research and awareness of societal effects (#bias, diversity, non-discrimination, fairness and prevention of harm). • Honesty in developing, carrying out, reviewing, reporting and communicating on research transparently, fairly, thoroughly, and impartially. • Respect for #privacy, confidentiality and #IP rights as well as respect for colleagues, research participants, research subjects, society, ecosystems, cultural heritage, and the environment. • Accountability for the research from idea to publication, for its management, training, supervision and mentoring, underpinned by the notion of human agency and oversight. Key recommendations include: For Researchers • Follow key principles of research integrity, use #GenAI transparently and remain ultimately responsible for scientific output. • Use GenAI preserving privacy, confidentiality, and intellectual property rights on both, inputs and outputs. • Maintain a critical approach to using GenAI and continuously learn how to use it #responsibly to gain and maintain #AI literacy. • Refrain from using GenAI tools in sensitive activities. For Research Organizations • Guide the responsible use of GenAI and actively monitor how they develop and use tools. • Integrate and apply these guidelines, adapting or expanding them when needed. • Deploy their own GenAI tools to ensure #dataprotection and confidentiality. For Funding Organizations • Support the responsible use of GenAI in research. • Use GenAI transparently, ensuring confidentiality and fairness. • Facilitate the transparent use of GenAI by applicants. https://lnkd.in/eyCBhJYF

Future of Privacy Forum enters the Chat(GPT) and publishes helpful checklist for the development of organizational generative AI policies. Key points (broken down into simple action items): 1) Use in Compliance with Existing Laws and Policies for Data Protection and Security TO DO: - Assess whether your internal policies account for planned and permitted use of AI; regularly update - Subject sharing data with vendors to requirements that ensure compliance with relevant US state laws (including the "sale/share" issue). - Ensure (through diligence, contractual provisions, and audit) that vendors support any required access and deletion requests - Designate personnel responsible for staying abreast of regulatory and technical developments. WHY: US regulators said they are already enforcing existing legal violations when AI is used to carry them out 2) Employee Training TO DO: - Remind employees that all existing legal obligations remain; especially in regulated industries - Provider training re: the implications and consequences of using generative AI tools in the workplace and specifically re: responsible use, risk, ethics, bias - Advise employees to avoid inputting sensitive or confidential information into a generative AI prompt unless data is processed locally and/or subject to appropriate controls - Establish a system (pop ups?) to regularly remind individuals of legal restrictions on profiling and automated decision-making, as well as key data protection principles - Provide employee with the contact information for personnel that are responsible for AI and data protection 3) Disclosure TO DO: - Provide employees with clear guidance on (a) when and whether to use organizational accounts for generative AI tools, (b) permitted and prohibited uses of those tools in the workplace - Provide employees with an easy to use system to document their use of these tools for business purposes. Such tools should enable employees to add context around any use, and provide a method to indicate how that use fits into the organizations’ policies - Address whether you require or prohibit the use of organizational email accounts for particular AI services or uses. - Communicate when and how the organization will require employees to disclose whether use of AI tools for internal and/or external work product - Update internal documentation, including employee handbooks and policies, to reflect policies regarding Generative AI use 4) Outputs of Generative AI TO DO: - Implement systems to remind employees of issues with generative AI and remind them to verify outputs of generative AI, including for issues regarding accuracy, timeliness, bias, or possible infringement of intellectual property rights - Check and validate coding outputs by generative AI should for security vulnerabilities. #dataprivacy #dataprotection #AIregulation #AIgovernance #AIPrivacy #privacyFOMO https://lnkd.in/dYwgZ33i