Patient Data Security

From a simple SMS to exposing patient's invoices - Cloud Security in hospitals. During a visit to a hospital. After my appointment, I received a physical invoice, and an e-bill was sent to my phone via a system-generated SMS. The SMS included a link that, when clicked, downloaded my bill directly to my device. I started to investigate how it works and was shocked by the results! The link in the SMS was a long URL, seemingly auto-generated by the SMS provider. It redirected to an AWS S3 bucket link managed by the hospital. However, the file name was a simple, consecutive 6-digit number followed by ".pdf"—no signature token, no authentication required. This meant that anyone with the knowledge could easily manipulate the URL to download other patients' bills, completely bypassing authentication. A serious breach of privacy! Key Takeaways: - Always use hashed filenames that aren’t predictable to prevent unauthorized access. - Implement file signatures and ensure they match only the files intended for specific users. - Secure your cloud policies and IAM settings to restrict access to sensitive data. #Cybersecurity #Infosec #DataPrivacy #cloudsecurity

Patient data is the new jackpot for attackers. Without HSMs, you’re handing them the keys. ICMR’s 81 crore record breach showed what happens when data and keys live together. Most hospitals in India still keep patient PII — often Aadhaar-linked — in standard databases where data and encryption keys sit side by side. That design flaw makes a breach inevitable, not hypothetical. The ICMR incident proved the scale: health records of over 81 crore Indians for sale online, placing India among the top three in global healthcare leaks. Under the DPDP Act 2023, penalties can touch ₹250 crore per breach. That’s before factoring in reputational damage and regulatory scrutiny. Hardware Security Modules (HSMs) change the game — by isolating encryption keys in tamper-proof hardware, they render stolen databases useless. The truth is simple: the way hospitals secure patient data today is insufficient. HSMs are not an upgrade. They are the minimum standard of trust.

One of our clients — a statewide healthcare organization in Australia — was operating multiple hospitals and clinical systems. Logs existed everywhere. Visibility existed nowhere. The challenge: • Security logs scattered across clinical systems, identity platforms, and infrastructure • No single view for threat detection • Compliance audits required manual effort • Incidents were hard to correlate across hospitals This wasn’t a tooling issue. It was a visibility and structure issue. How Cybernara helped: • Designed a centralized security monitoring approach • Integrated clinical applications, identity systems, infrastructure, and databases • Standardized logs to support investigations and audits • Built SOC workflows focused on real clinical risk • Aligned monitoring and retention with healthcare compliance needs The outcome: • Real-time visibility across hospitals and systems • Faster detection of suspicious access to sensitive health data • Centralized investigations with full context • Audit readiness without last-minute pressure Good security in healthcare should be invisible to patients and invaluable to teams behind the scenes. #CaseStudy #HealthcareSecurity #SOC #SecurityMonitoring #Compliance #Cybernara

Cybersecurity must become a procurement criterion, not an afterthought. European Union Agency for Cybersecurity (ENISA)’s #Procurement #Guidelines for the #Cybersecurity of #Hospitals and #Healthcare #Providers offer a practical #framework for embedding security across the full procurement lifecycle. Key #takeaways: • Treat cybersecurity as a strategic #supplychain priority, supported by #governance, adequate resources and an up-to-date register of critical suppliers. • Embed security from the planning stage: assess #risks, legacy-system dependencies, #interoperability, privacy and regulatory requirements before launching a tender. • Translate risks into enforceable requirements, covering access control, #encryption, MFA, logging, secure configuration, patching, incident notification and end-of-life support. • Evaluate suppliers beyond price and functionality. Assess certifications, secure development practices, vulnerability management, third-party components and long-term support capacity. • Use multidisciplinary procurement teams, bringing together procurement, IT, cybersecurity, clinical engineering, data protection and legal expertise. • Make contracts operationally precise: define responsibilities, response times, disclosure obligations, audit rights, liability, data return and secure deletion. • Manage security throughout the contract lifecycle through continuous monitoring, supplier-performance reviews, incident coordination and corrective actions. • Address #AI explicitly. Require transparency on models, providers, data use, subprocessors, human oversight, logging and material changes. • Plan secure exit and decommissioning from day one, ensuring sensitive health data cannot remain exposed after a product or service is retired. The central message is clear: secure healthcare procurement is a continuous lifecycle discipline spanning Plan, Source and Manage, not a one-time compliance check. https://lnkd.in/dsNTKRbV

Patient Records, AI-Generated Code, and Zero Trust — A Data Engineering Experiment Healthcare data engineering is unforgiving. You're moving patient records — names, SSNs, lab results, diagnosis codes — through pipelines that must stay HIPAA-compliant at every step. Validation, normalization, PHI de-identification, clinical enrichment, risk scoring. Miss one field, leak one record, and you have a real problem. Now add AI agents to this. An LLM generates the Python code that processes your patient data. That code runs on your infrastructure. What stops it from reading files, hitting the network, or dumping environment variables with your database credentials? This is the question I kept coming back to. In healthcare data engineering, you can't just trust AI-generated code. You have to prove it can't escape. Pydantic just released Monty*— a Rust-based Python interpreter built for exactly this. No filesystem access. No network. No env vars. No dangerous builtins. Pure computation only. It pushed me to try something I've been thinking about. I built a 6-step healthcare data pipeline — validate patient records, normalize formats, de-identify PHI, enrich with clinical flags, compute risk scores, aggregate stats. Every step: Claude generates the code, Monty sandboxes and executes it. 35 seconds for Claude to generate code across six steps. 1.3 milliseconds for Monty to execute all of it. That's 27,000x faster. The bottleneck is the LLM, not the sandbox. Security doesn't have to slow you down. Monty is experimental, but the direction is right. If you're running AI agents against regulated data — healthcare, finance, compliance — sandboxed execution isn't optional. It's the baseline. Code is open source. Link in comments. 👇

𝗜 𝘄𝗼𝗿𝗸𝗲𝗱 𝗼𝗻 𝗮 𝗿𝗶𝘀𝗸 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗼𝗿 𝗮 𝗺𝗼𝗰𝗸 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗰𝗮𝗹𝗹𝗲𝗱 𝗠𝗲𝗱𝗶𝗰𝗮𝗿𝗲 𝗖𝗹𝗶𝗻𝗶𝗰. So, Medicare is a private hospital that uses cloud based electronic health records. My job here as a GRC Analyst was to assess how patient data could be exposed, altered, or become unavailable across critical hospital workflows, while making sure that patient care doesn't stop because security failed. So, here is the full context of Medicare: Medicare had adopted a cloud based EHR system to improve efficiency, but a few issues started to show up almost immediately: → Doctors were using personal email accounts to send lab results when the official system was slow → Cloud hosting had been outsourced to a third party vendor without clear visibility into their security controls → Regulatory scrutiny around data privacy was increasing, especially under NDPR and healthcare data handling expectations As a GRC Analyst, this raised immediate concerns around confidentiality, integrity, availability, vendor risk, and compliance exposure. To assess the environment, I looked at: → Critical business assets and patient data flows → Risk scenarios tied to daily operations → Likelihood and impact scoring → Risk prioritisation using a structured matrix → Control recommendations that made sense in a healthcare setting Through the analysis I made, I discover a few high priority risks: → Sensitive patient data being shared through unmanaged channels → Weak visibility into third party cloud security controls → Compliance gaps that could lead to regulatory findings and reputational damage → Delays in accessing clinical records when systems were unavailable To reduce these risks, I recommended: → Restricting patient data sharing to approved communication channels only → Tightening third party oversight with clearer security assurance requirements → Improving access control and audit visibility across sensitive workflows → Putting secure fallback procedures in place for clinical teams during system delays → Closing compliance gaps tied to NDPR and healthcare data privacy obligations What is the biggest takeaway from this task? In the healthcare industry, security cannot be treated in isolation. Controls have to protect sensitive data without getting in the way of care delivery. If you are learning GRC, take note of this.👇 Risk is not just only about systems. It is usually about people, process, technology, and business processes. I documented this entire task on GitHub. On there, you will see my entire documentation, technical evidence, and also relevant screenshots. You can find this in the comments.👇 And also links to the labs I have done previous weeks. #GRC #Cybersecurity #RiskManagement #ThirdPartyRisk #Compliance

New York hospitals face a strict cybersecurity deadline on October 2, 2025. Failure to meet the standards could trigger fines and jeopardize licenses. * Written risk‑based program covering identify, protect, detect, respond, recover. * Designated CISO, either internal or qualified external, must report annually. * Technical controls include multi‑factor authentication, encryption or approved alternatives, identity‑access management, yearly penetration testing. * Incident‑response plan requires a 72‑hour breach notification. Regulators expect thorough documentation and regular testing to demonstrate readiness. Effective budgeting focuses on high‑risk systems first, then expands to lower‑priority areas. By prioritizing high‑impact assets and allocating budget wisely helps institutions protect patient data while staying compliant. #cybersecurity #healthcare #NY