Sensitive Health Information Handling

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

Let’s say you’re a newly hired Third-Party Risk Analyst at a mid-sized healthcare company. During your onboarding, you realize that while they have dozens of vendors handling sensitive patient data (think billing companies, cloud services, and telehealth providers), they have no formal third-party risk assessments documented. First, you would start by building a basic Third-Party Inventory. You’d gather a list of all vendors, what services they provide, and what kind of data they have access to. You would focus on vendors that touch patient records (Protected Health Information, or PHI) because HIPAA requires stricter handling for that kind of data. Next, you would create a simple vendor risk rating system. For example, any vendor handling PHI = High Risk, vendors with financial data = Medium Risk, vendors with only public data = Low Risk. You’d organize vendors into those categories so leadership can prioritize attention. Then, you would prepare a basic Due Diligence Questionnaire to send out. It would ask things like: • Do you encrypt PHI data in transit and at rest? • Do you have a current SOC 2 report? • Have you had any breaches in the last 12 months? After collecting responses, you would review them and flag any vendors who seem high-risk (like no encryption, no audit reports, or recent breaches). You’d recommend follow-ups, like contract updates, requiring security improvements, or even switching providers if needed. Finally, you would propose setting up a recurring third-party review schedule — maybe every 6 or 12 months — so that vendor risk stays managed continuously, not just one time.

𝐀𝐫𝐞 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐥𝐞𝐚𝐤𝐬 𝐭𝐡𝐞 𝐥𝐚𝐭𝐞𝐬𝐭 ‘𝐟𝐫𝐞𝐞 𝐠𝐢𝐟𝐭’ 𝐰𝐢𝐭𝐡 𝐞𝐯𝐞𝐫𝐲 𝐩𝐮𝐫𝐜𝐡𝐚𝐬𝐞? A complainant ordered prescription medicine from a pharmacy (the respondent). She claimed that the package displayed her sensitive health data (including an inaccurate medical diagnosis) and that this data was shared with an insurance company without her consent. The complainant also alleged that her personal information was improperly exposed to third parties through the delivery process. The Respondent argued that her information was used solely for processing her prescriptions, arranging delivery, and insurance claims. It denied sharing her data with third parties beyond necessary employees (like the delivery rider) and insisted that no promotional or irrelevant use of her data occurred. 𝐓𝐡𝐞 𝐎𝐃𝐏𝐂 𝐇𝐄𝐋𝐃  ➤The complainant’s right to privacy was violated as her medical diagnosis was unnecessarily exposed on the outside of the package, breaching Section 25(a) of the Act, which mandates protecting personal data. ➤The pharmacy failed to minimise data exposure, violating the principle of data minimisation under Section 25(d). ➤No evidence supported the claim that third parties, apart from the pharmacy's rider, had access to the package during delivery. ➤The complaint regarding the incorrect diagnosis was inconclusive, as the complainant did not provide evidence from her primary doctor to verify that the pharmacy's diagnosis was wrong. ➤The complainant awarded 𝐊𝐬𝐡𝐬. 700,000 as compensation for the breach. 𝐓𝐀𝐊𝐄 𝐀𝐖𝐀𝐘𝐒: 𝐃𝐚𝐭𝐚 𝐁𝐫𝐞𝐚𝐜𝐡 𝐑𝐢𝐬𝐤𝐬 𝐑𝐞𝐥𝐚𝐭𝐞𝐝 𝐭𝐨 𝐏𝐚𝐜𝐤𝐚𝐠𝐢𝐧𝐠 & 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬   Packaging materials can pose real data breach risks if sensitive information is not managed securely. Businesses must be aware of potential 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬  associated with packaging and implement robust measures to safeguard personal data and prevent breaches. Here are 8 𝐓𝐇𝐈𝐍𝐆𝐒 𝐓𝐎 𝐃𝐎: ▶Use secure, non-transparent packaging. Implement tamper-evident labels to prevent unauthorised access. ▶Store packaging materials securely and limit access to authorised personnel only. ▶Communicate clearly with customers about packaging practices and provide support channels for reporting concerns. ▶Regularly train employees on data protection practices and secure packaging procedures. ▶Use secure printing systems and consider encrypting sensitive data on labels. ▶Develop a robust incident response plan and conduct regular audits of packaging and handling procedures. ▶Adhere to relevant regulations and maintain documentation of data protection practices. ▶Vet vendors for data protection compliance and establish clear data protection agreements. 📘Every case tells a story. For insights into the precedents that shape the narratives of justice, follow my page share my posts and connect. #CustomerPrivacy #DataProtection #PackagingRisks #PersonalData

"Collecting, storing, using, and sharing people’s sensitive information without their informed consent violates their privacy, and exposes them to substantial secondary harms like stigma, discrimination, physical violence, and emotional distress. The Federal Trade Commission will not stand for it" - says FTC in new blog post recapping its actions in Avast, X-Mode and InMarket. Key points re some common themes: 🔹 Browsing and location data are sensitive. Full stop. 🔹 Browsing and location data paint an intimate picture of a person’s life, including their religious affiliations, health and medical conditions, financial status, and sexual orientation. 🔹 What makes the underlying data sensitive springs from the insights they reveal and the ease with which those insights can be attributed to particular people. 🔹 Years of research shows that datasets often contain sensitive and personally identifiable information even when they do not contain any traditional standalone elements of PII, and re-identification gets easier every day—especially for datasets with the precision of those at issue 🔹 People have no way to object to—let alone control—how their data is collected, retained, used, and disclosed when these practices are hidden from them. 🔹 When a developer incorporates a company’s code into their app through an SDK, that developer amplifies any privacy risks inherent in the SDK by exposing their app’s users to it. 🔹 Data handling must align with the purposes for which it was collected. 🔹 Purpose matters: Firms do not have free license to market, sell, and monetize people’s information beyond purposes to provide their requested product or service. 🔹 Any safeguards used to maintain people’s privacy are often outstripped by companies’ incentives and abilities to match data to particular people - make sure that you control the sharing and use of data by your downstream. 🔹 Promises and contract clauses are important, but they must be backed up by action. 🔹 Firms should not let business model incentives that focus on the bottom line outweigh the need for meaningful privacy safeguards. #dataprivacy #dataprotection #privacyFOMO https://lnkd.in/eAuTmutG

The NY Attorney General's Office (NYAG) fined a HIPAA "covered entity" $4.5 million after a hack resulted in loss of 1.4 terabytes of data, some of which contained patient information. See https://lnkd.in/d3vXcTFe. Here's what the company did wrong according to the NYAG:   1. ACCESS CONTROLS AND AUTHENTICATION: The Company failed to implement and maintain appropriate controls to limit access to sensitive data, including failing to use multi-factor authentication for remote access to email, failing to delete or disable unused accounts, failing to rotate account credentials, sharing account credentials among multiple individuals, and failing to restrict employees' access to only those resources and data necessary for their business functions.   2. PROTECTION OF SENSITIVE INFORMATION: The Company failed to encrypt all sensitive patient data maintained at rest.   3. AUDIT CONTROLS AND MONITORING: The Company failed to implement appropriate controls for recording, and reviewing records of, user activity on its network.   4. RISK MANAGEMENT AND TESTING: The Company failed to regularly conduct appropriate risk management analyses and testing of the security of its systems.   5. SECURITY POLICIES: The Company failed to adequately maintain and adhere to written policies governing information security, asset management, identity and access management, encryption, risk management, network management, vulnerability management, and the retention of patient data.   OF NOTE: In fining the Company, the NYAG noted specific violations of HIPAA's Security Rule and Breach Notification Rule, demonstrating that the NYAG will enforce violations of HIPAA, at least where other rules have purportedly been broken (here, NY Executive Law § 63(12) and NY General Business Law § 899-bb).   ALSO: In fining the Company, the NYAG seems to have found it significant that the Company's 2021 Security Risk Assessment, resulted in numerous findings that had not been remediated in the approximately two years prior to this incident. The failure to remediate these findings may have contributed to the incident.   KEY TAKEAWAYS:   A. Most (if not all) of these deficiencies are often considered table stakes. If your organization stores sensitive PII, and suffers from similar deficiencies, these are gaps you will want to close sooner rather than later.   B. If you have not done so recently, consider engaging an independent expert to conduct a thorough cyber program assessment (CPA). See https://lnkd.in/eQ9wYbxT.   B. Once you get the results of the CPA, prepare a detailed roadmap to timely implement any suggested improvements. See https://lnkd.in/e3f5yXnN.   C. If you are considering acquiring or investing in an entity, consider conducting thorough cyber diligence, including technical testing, and quickly implementing a roadmap to address any significant findings. See https://lnkd.in/e3b43d4c.   Congrats to my former colleagues at the NYAG's office, Jordan Adler and Clark Russell! 

Compliance & Security Concerns in Healthcare 𝗦𝗶𝘁𝘂𝗮𝘁𝗶𝗼𝗻: A medical tech startup required advanced compliance measures (HIPAA and additional data protection) and had reservations about entrusting sensitive patient data to a remote development partner—particularly one outside the U.S. 𝗖𝗼𝗻𝗰𝗲𝗿𝗻: 👉Fear of data leaks or compliance breaches 👉Difficulty in monitoring security protocols from a distance 👉Unsure if nearshore talent would match the specialized healthcare tech knowledge required 𝗢𝘂𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: 👉Clearly outlined our stringent security policies and compliance certifications—demonstrating both on paper and in practice 👉Established a secure development environment with strict access controls, data encryption, and frequent audits to align with HIPAA standards 👉Introduced our nearshore engineers who specialized in healthcare solutions, showcasing a strong portfolio of similar projects 𝗥𝗲𝘀𝘂𝗹𝘁: The startup’s legal and compliance teams felt confident after reviewing our security measures. The nearshore team not only delivered on the technical front but also proactively advised on best practices for healthcare software, reinforcing trust and long-term partnership.

7 security and governance steps I recommend for AI-powered health-tech startups to avoid hacks and fines: 1. Pick a framework -> The Health Insurance Portability and Accountability Act (HIPAA) is non-negotiable if you handle protected health information (PHI). Look at the security, privacy, and data breach notification rule requirements. -> If you want a certification (incl. addressing HIPAA requirements), HITRUST is a good place to start due to origins in healthcare. The AI security certification gives you solid controls for these types of systems. -> If you are looking to cover responsible AI as well as security/privacy, ISO 42001 is a good option. Consider adding HIPAA requirements as additional Annex A controls. 2. Publish policies Longer != better. Use prescriptive statements like "Employees must XYZ." If there are detailed steps, delegate responsibility for creating a procedure to the relevant person. Note that ISO 42001 requires an "AI Policy." 3. Classify data Focus on handling requirements rather than sensitivity. Here are the classifications I use: -> Public: self-explanatory -> Public-Personal Data: still regulated by GDPR/CCPA -> Confidential-Internal: business plans, IP, etc. -> Confidential-External: under NDA with other party -> Confidential-Personal Data: SSNs, addresses, etc. -> Confidential-PHI: regulated by HIPAA, needs BAA 4. Assign owners Every type of data - and system processing it - needs a single accountable person. Assigning names clarifies roles and responsibilities. Never accept "shared accountability." 5. Apply basic internal controls This starts with: -> Asset inventory -> Basic logging and monitoring -> Multi-factor authentication (MFA) -> Vulnerability scanning and patching -> Rate limiting on externally-facing chatbots Focus on the 20% of controls than manage 80% of risk. 6. Manage 3rd party risk This includes both vendors and open source software. Measures include: -> Check terms/conditions (do they train on your data?) -> Software composition analysis (SCA) -> Service level agreements (SLA) 7. Prepare for incidents If your plan to deal with an imminent or actual breach is "start a Slack channel," you're going to have a hard time. At a minimum, determine in advance: -> What starts/ends an incident and who is in charge -> Types of incidents you'll communicate about -> Timelines & methods for disclosure -> Which (if any) authorities to notify -> Root cause analysis procedure TL;DR - here are 7 basic security and governance controls for AI-powered healthcare companies: 1. Pick a framework 2. Publish policies 3. Classify data 4. Assign owners 5. Apply basic controls 6. Manage 3rd party risk 7. Prepare for incidents What else?

The biggest privacy risks often hide in plain sight 👀 Over the past few weeks, Timothy Nobles has been diving deep into quasi-identifiers - those seemingly harmless data points that become privacy landmines when combined. ZIP codes, age ranges, visit dates - individually safe, collectively dangerous. This challenge keeps coming up in conversations with teams across healthcare, fintech, and consumer analytics. Organizations are drowning in complex privacy regulations while trying to maintain data utility for critical insights. That's why our team at Integral Privacy Technologies created this comprehensive Pocket Guide to Quasi-Identifiers 📋 What's packed inside: ✔️ Real-world industry scenarios - from the "rare disease specialist" healthcare dilemma to financial "transaction fingerprints" ✔️ Practical risk assessment frameworks - no PhD in statistics required ✔️ Actionable implementation strategies - statistical safeguards, technical controls, and governance best practices ✔️ The privacy-aware mindset - how to spot risks before they become compliance nightmares The guide breaks down complex concepts like Dr. Latanya Sweeney's research showing 87% of Americans can be uniquely identified using just ZIP code, birth date, and gender - insights that fundamentally change how we think about "anonymous" data. For teams navigating: - Healthcare data with rare conditions creating small cohorts - Financial transaction patterns that reveal individual behaviors - Consumer research combining household demographics with purchase data We’re excited to share this practical guidance born from working with teams who need to balance privacy protection with business value every day. Download the complete pocket guide: https://lnkd.in/eQuuxhzH Ready to transform your approach to sensitive data compliance? Let's connect: useintegral.com

In healthcare leveraging sensitive data for AI and ML is crucial, but privacy concerns often hinder progress. In this walkthrough we outline a step-by-step guide to generating high-quality, privacy-safe synthetic patient data that maintains utility while preserving patient confidentiality. We get into how to protect against privacy attacks, and demonstrate how to work with complex, multi-modal health data including numeric values, categorical information, free text, and time-series data. This approach goes beyond simple anonymization, creating new records not based on any single individual.

⚠️ CRITICAL AI SECURITY ALERT FOR MEDICAL WRITERS The recent Fortune investigation into Microsoft Copilot's "EchoLeak" vulnerability should be a wake-up call for the medical writing industry. As medical writers increasingly rely on AI tools like Copilot to draft clinical study reports, regulatory submissions, and other documents containing sensitive patient data, we need to address some uncomfortable truths. The Reality Check: ⚠️ A "zero-click" attack could expose patient data without any user interaction ⚠️ Hackers could access clinical trial data, patient information, and proprietary research simply by sending an email ⚠️ The vulnerability bypassed Copilot's built-in protections designed to secure user files Why This Matters for Medical Writing: ✅ We handle HIPAA-protected patient data daily ✅ Clinical study reports contain sensitive efficacy and safety information ✅ Regulatory submissions include proprietary drug development data ✅ Competitive intelligence could be compromised through document access While Microsoft has reportedly fixed this specific flaw, the researchers warn this represents a "fundamental design flaw" in AI agents similar to vulnerabilities that plagued software for decades. Questions We Need to Ask: ⁉️ Are our current AI tool policies adequate for protecting patient privacy? ⁉️ Do we have sufficient oversight when AI assistants access clinical databases? ⁉️ Are we creating audit trails for AI interactions with sensitive documents? ⁉️ Have we assessed the security posture of ALL AI tools in our workflows? The pharmaceutical industry has been cautiously adopting AI agents, and frankly, this caution appears justified. As one researcher noted: "Every Fortune 500 I know is terrified of getting agents to production." Moving Forward: We can't abandon AI innovation, but we must demand transparency about security measures, implement robust data governance, and maintain human oversight of AI interactions with sensitive clinical data. ❓ What security protocols has your organization implemented for AI tool usage? How are you balancing innovation with patient data protection? #MedicalWriting #AIethics #DataSecurity #ClinicalTrials #HIPAA #PharmaSecurity #RegulatoryAffairs https://lnkd.in/eEX2pJ6d