How to fine-tune LLMs with unstructured data in healthcare and finance

HIPAA for AI. Most compliance talks never leave the abstract. “Make sure your AI is HIPAA compliant.” But what does that actually mean inside a mental health clinic? We broke it down — line by line, regulation by regulation — into daily workflow. Not theory. Not legalese. Just what you need to actually stay compliant when using AI transcription and note generation. This carousel walks you through: ✅ Patient consent before every recording ✅ Role-based access and data permissions ✅ Minimum necessary prompts for AI ✅ PHI flow mapping and encryption standards ✅ Audit trails you actually review Every point references the exact CFR clause — in plain English. At CliniScripts, HIPAA isn’t a feature — it’s our foundation. From explicit consent to end-to-end encryption, our AI scribe is built for clinical compliance. 📘 Swipe through → Then share this with someone setting up AI in their practice. #HIPAA #AIinHealthcare #MentalHealthTech #EHR #Compliance #HealthcareAI #CliniScripts MarkiTech.AI

Access GPT, Claude, and Gemini with zero data risk. Here’s the thing. AI has the power to transform healthcare, but most organizations still hesitate to use it where it matters most. Patient data is sensitive, compliance rules are strict, and one privacy slip can cost more than money which is trust. That’s where Secure AIs comes in. We remove PHI and sensitive identifiers, keep the clinical context intact, and keep you HIPAA-compliant. Healthcare teams use Secure AIs to safely automate documentation, support diagnostics, and make faster, data-informed decisions without exposing patient data. What this really means is hospitals, payers, and research teams can finally use AI confidently, without fear of breaches or compliance issues and actually see measurable ROI from it. Ready to see how secure your AI workflows really are? 👉 Get your Free AI Leak Audit: https://lnkd.in/eY9QNM-e #HealthcareAI #DataPrivacy #HIPAACompliance #AIsafety #SecureAIs #HealthTech #PatientData

📌 Amid conflicting interpretations and guidelines that are not always clear, the European Commission has published the official FAQs on the AI Act, managed by the #AIActServiceDesk. This is the first single reference point that brings clarity and explains: * Timeline: prohibitions, definitions and AI literacy apply from 2 February 2025; GPAI and governance rules from 2 August 2025; high-risk obligations and enforcement from 2 August 2026; full roll-out by 2 August 2027; * the prohibitions already in force since 2 February 2025 (Art. 5); * the obligation of AI literacy for providers, deployers and affected persons (Art. 4); * the classification and requirements for high-risk AI systems (Art. 6 and Annex III); * the transparency rules for chatbots, deepfakes and interactive systems (Art. 50); * the obligations for general-purpose AI models and those with systemic risk, including compute thresholds and mandatory notification to the AI Office (Arts. 51–55); * the support measures for SMEs and the activation of regulatory sandboxes by 2 August 2026 (Art. 62). It is a document worth reading and keeping at hand: it helps to separate urgent obligations from those applying later, while also highlighting spaces for experimentation. For companies, public administrations and professionals, it becomes a true compass in navigating the AI Act, with the #AIOffice acting as the central authority for oversight and enforcement. 👉 Direct link here: https://lnkd.in/dQPWf_jW Great work Lucilla Sioli, Kilian Gross and the whole Team. #AIAct #Governance #Compliance #AILiteracy #RiskManagement #AIOffice

On Monday, at the RAID (REGULATION - AI - INTERNET - DATA) Conference, CIPL Director of Privacy and Data Policy, Natascha Gerlach CIPP/E, moderated a panel on 'AI and Data in Healthcare and Pharma: Balancing Innovation and Ethics' with speakers Andrea Batalla Eguidazu (Baxter International Inc.), Karolina Mojzesowicz (European Commission) and Christopher Foreman (CIPL).   The panel explored how the current regulatory framework enables access to health data for research and development. Key takeaways from the conversation include:  1. Regulatory Complexity - For companies with a global footprint, diverging international laws can make the global deployment of AI-driven medical devices particularly challenging. Within Europe, healthcare organizations must navigate the intersection of three complex frameworks: GDPR, EHDS, and the AI Act. Harmonizing these regimes is essential, yet fragmentation in definitions, scope, and regulatory interpretations continues to pose significant challenges.   2. Building Trust - Patients, hospitals, and doctors often approach AI cautiously. Patients may hesitate to share data, hospitals worry about compliance and operational risks, and doctors are reluctant to rely on AI when it incorporates data from unknown patients or other practitioners. Building trust requires a strong foundation of organizational education in AI literacy, data governance, and ethical use of AI.   3. EDPS vs. SRB CJEU ruling - The recent CJEU ruling clarified that pseudonymized data may not be considered personal for recipients who cannot reidentify individuals or share the data with those who can. This opens the door for AI-driven healthcare innovation using historical or external datasets, while EHDS rules ensure controllers remain transparent with patients about possible secondary uses.   Thank you to speakers for sharing your insights! #RAID2025 #AIandData #HealthTech #AIRegulation #DataGovernance #Innovation #ResponsibleAI

The new NHS Communications Artificial Intelligence Operating Framework is my weekend reading. The NHS is made up of multiple parts to match local needs so the framework is designed to provide a "shared foundation that NHS communicators can adapt and apply based on local needs, in partnership with other key stakeholders within their organisation." The UK Government Communication Service is already at the forefront globally of integrating AI into its work, so it's great to see the NHS doing the same. Crucial to its success will be ensuring the right policies, processes, tools and training are identified and implemented locally. Different trusts will need to move at different speeds as each will have its own challenges. The key to success is ensuring social licence for AI and winning the trust of all its stakeholders including employees and patients to ensure trust in AI to be used effectively, efficiently and responsibly. https://sbpr.cc/SgnCf

Navigating the integration of AI, especially Large Language Models (LLMs), in healthcare demands meticulous attention to data preparation and compliance. At PPLabs, we understand the immense potential LLMs hold for transforming patient care, but we also recognize the critical need to safeguard sensitive Protected Health Information (PHI). Our latest article dives deep into the strategic steps healthcare organizations must take to prepare their healthcare data for LLMs while ensuring strict adherence to regulations like HIPAA. It’s about unlocking innovation responsibly. Key Insights from Our Latest Article: 🔒 Understand Compliance First: Learn why data security isn't an afterthought, with 92% of healthcare organizations experiencing a cyberattack in the past year, costing an average of $9.77million. We detail how to integrate LLMs without risking HIPAA violations. 📊 Tackle Data Silos & Complexity: Discover strategies to unify fragmented data across EHRs, lab systems, and more. Fragmented data undermines AI effectiveness; a holistic view is crucial for robust LLM insights. 🛡️ Master PHI De-identification & Protection: Explore critical techniques for stripping or masking personal identifiers, or deploying LLMs in secure, controlled environments to prevent breaches. 🧠 Optimize LLM Performance with Domain Adaptation: Understand why fine-tuning general LLMs or utilizing pre-trained medical LLMs (like Google's Med-PaLM 2, which scored 85% on medical exams) is essential for accurate clinical applications. ✅ Pilot & Validate Safely: Learn the importance of piloting LLMs on contained, low-risk use cases to iron out kinks, ensure compliance, and build stakeholder trust before scaling. Read the full article to master your approach to healthcare data for LLMs and innovate with confidence. 🔗 https://lnkd.in/dxQZqeeu #HealthcareAI #LLMs #DataCompliance #HIPAA #DigitalHealth #PPLabs #HealthTech #AIinHealthcare

What Happens When Evidence is AI-Generated #artificialintelligence #technology Source: https://ift.tt/yBOkxU6 In the rapidly evolving landscape of artificial intelligence, the integrity of evidence is facing unprecedented challenges. Our latest blog post delves into the implications of AI-generated evidence on the justice system and beyond. With deepfakes that can deceive even the most trained experts, the reliability of what we once regarded as proof is now under scrutiny. This article explores various facets of synthetic media, highlighting recent legal cases, the inadequacies of current detection technologies, and the ethical dilemmas that arise in this new reality. With courts struggling to establish precedent for AI-generated content, we are at a crucial intersection of technology and law that demands urgent attention. As we navigate this intricate issue, our insights aim to illuminate the pressing concerns for legal professionals, journalists, and organizations alike, emphasizing the need for robust systems that ensure authenticity in a world where "truth" is increasingly subjective. Discover the complexities and potential solutions in our comprehensive blog post by reading more here: [What Happens When Evidence is AI-Generated](https://ift.tt/yBOkxU6). #rswebsols #A…

Serious Incident Reporting under the EU AI Act: draft guidance expands obligations to deployers The European Commission has just published draft guidance and a template for reporting serious incidents involving high-risk AI systems. This is not just a technical detail, it reshapes responsibilities under the AI Act. ➡️ One striking element: the draft expects not only providers of AI systems to report incidents, but also deployers (users/organizations applying AI systems). Deployers would have to notify providers if an incident occurs, creating a two-way flow of accountability. This approach broadens the compliance burden: - Deployers may need internal monitoring and escalation procedures, not just providers. - Providers must be ready to receive, assess, and potentially escalate reports to regulators. - Legal questions arise: what counts as a “serious incident,” who makes the call, and how will liability be allocated between providers and deployers? The draft is still open for consultation until 7 November 2025. 🔍 For legal and compliance professionals, now is the time to: - Review the draft guidance and reporting template. - Assess how your organization (as a provider, deployer, or both) would operationalize such reporting duties. - Consider submitting input before the consultation deadline. #AIAct #HighRiskAI #IncidentReporting #Compliance #LegalTech

California Enacts Landmark Regulation for Advanced AI Models: The Transparency in Frontier AI Act The California Senate has enacted SB 53, now known as the Transparency in Frontier Artificial Intelligence Act (TFAIA), establishing new rules for the safety and transparency of foundation AI models. The legislation focuses on "large frontier developers" and requires: 🛡️ Safety Framework: Implementation and publication of a "frontier AI framework" detailing how safety standards and best practices are incorporated into model development. ⚠️ Catastrophic Risk: Mandatory submission of internal catastrophic risk assessment summaries (risks that could cause significant harm to life or property) to the Office of Emergency Services (OES). 🗣️ Whistleblower Protection: Guarantees protection for employees (whistleblowers) who report catastrophic risks or TFAIA violations, requiring an anonymous internal reporting process for large developers. ✅ Public Innovation: Establishment of a consortium to develop "CalCompute," a public cloud computing cluster focused on promoting the advancement of safe and equitable AI. The goal is to increase accountability in the most advanced segment of the AI sector, with civil penalties outlined for non-compliance. A significant precedent in technology governance. #AIRegulation #California #Technology #GovTech #SB53 #TFAIA

🔔 California: AI Transparency Act Signed Into Law [Full Data Guidance News with Q&A in the comments] On October 13, 2025, Governor Newsom signed the California AI Transparency Act — setting new standards for AI-generated content and transparency obligations. 📆 While the law won't take effect until August 2, 2026, organizations should begin preparing now. Key Takeaways 🧠 From Jan 1, 2027: Large online platforms must detect and disclose provenance data for AI-generated or altered content. 📷 From Jan 1, 2028: Capture device manufacturers must provide an option for latent disclosures in captured content. 💻 From Jan 1, 2027: GenAI hosting platforms must ensure embedded AI systems include the required disclosures. Why it matters AI transparency is no longer just an ethical aspiration — it’s a legal requirement. Businesses operating or serving users in California will need to: ✅ Review AI and content pipelines ✅ Ensure disclosure mechanisms are in place ✅ Prepare for platform- and device-specific obligations ⏳ Proactive readiness = lower risk + stronger digital trust. #AIRegulation #Transparency #California #AICompliance #ContentGovernance #DigitalTrust #AIGovernance #ResponsibleAI #GenAI #RiskAndCompliance