Matthias Fraunhofer’s Post

BMLL works with the world’s most sophisticated financial institutions to deliver the historical data they need - accurately, reliably, and at scale. We’ve distilled that experience into ‘A Guide for Historical Market Data Buyers’. Inside, we break down: ✔️ How to assess vendor quality ✔️ What type of data suits your needs ✔️ Why standardisation and derived insights matter Download your guide here: https://hubs.li/Q03vnvjv0 #HistoricalDataDoneProperly

⚠️ As data grows at record pace, how can government agencies keep an eye on truly all IT activity and identify potential risks? 🔍 The leading challenges with implementing Zero Trust may surprise you: https://hubs.la/Q03xKj6G0

Between the lines, a story flows, Of cause and change the data shows. A map of trust, a guide so smart— The pulse of stability: control chart.

Here's the situation: your corporate data isn't included in LLMs because it's not publicly available online, though many providers want access to it. You have a decision—either let others use your data to their advantage or leverage it as your own competitive edge. 

With Colorado and Connecticut amending to their definitions of sensitive data, it's time to update our sensitive data comparison chart. Here's the latest:

This chart provides an intriguing comparison between states. Key Takeaways: Core Sensitive Data Types: Most states consistently protect categories such as: ▪️ Health-related data (diagnosis, treatment, condition, disability) ▪️ Sexual orientation and activity ▪️ Religious, racial, or ethnic origin ▪️ Genetic/biometric data ▪️ Government-issued IDs (Social Security, driver’s license, passport numbers) ▪️ Financial account information combined with access credentials ▪️ Precise geolocation data Variable Protections: Some data types are only considered sensitive in a few states: ▪️ Union membership ▪️ Contents of consumer communication (e.g., mail, email, text messages) ▪️ Status as a transgender or non-binary individual ▪️ Citizenship/immigration status ▪️ Neural data and biological data ▪️ Child Data Protections: Several states distinguish between known child data and data knowingly collected from a child. States with the Broadest Definitions: ▪️ California (CA), Colorado (CO), Connecticut (CT), and Virginia (VA) include many sensitive data types, covering health, identity, biometric, financial, and communication data. ▪️ Oregon (OR), Utah (UT), and Texas (TX) also include broad protections but with some differences in scope. States with Narrower or Incomplete Definitions: ▪️ Indiana (IN), Iowa (IA), Kentucky (KY), Nebraska (NE), and New Hampshire (NH) appear to have narrower sensitive data definitions, leaving out several categories covered in other states. Notable Emerging Categories: ▪️ Neural and biological data are newly emerging categories, referenced explicitly only in select states. ▪️ Consumer health data (not just clinical records) is highlighted as a distinct sensitive category in a few states. Practical Implications: Organizations operating across multiple states need to account for: ▪️ Differences in what qualifies as "sensitive data" ▪️ Varying consent requirements and data use limitations ▪️ Expanded definitions in progressive privacy laws like those in CA and CO Navigating the patchwork of state privacy laws is complex and ever-changing. Each state defines sensitive data differently, and these variations directly impact how organizations must handle consent for data sharing. That's why Converge Health plays a critical role in helping healthcare organizations, HIEs, and technology providers identify applicable state requirements, map them to consent workflows, and ensure compliant data sharing practices. By partnering with experts during the consent implementation process, organizations can reduce legal risk, build trust with patients and partners, and create a scalable, future-proof interoperability strategy that respects privacy across state lines.

Why most CTI reports get ignored? You spent hours crafting a detailed PDF. You quoted MITRE, mapped the kill chain, listed 32 IOCs. But it lands in a mailbox… …and nobody uses it. Why? Because most CTI fails to be usable. ✅ Translate risk in business terms ✅ Tell the reader what to do next ✅ Cut the fluff and speak their language Good intelligence is decision support. Not data dumping.

In this article, we will talk about the aspects you should take into account when building a comprehensive data #observability system: from the right framework, the fundamental pillars, to modern best practices and how to prevent problems. https://buff.ly/7MCeTXx

Data is meaningless unless you can translate it into insights that drive better credit and fraud decisions. But what’s the best way to achieve that? By integrating all the data, analytics tools and decisioning software you need to power faster, more accurate credit decisions – read our latest report to find out more. Click here for your complimentary copy: https://lnkd.in/efJWkAv6

Thomson Reuters CLEAR is a powerful database tool. But raw data alone won’t tell you the full story behind a borrower, partner, or vendor. You still have to interpret hundreds of public records and legal filings yourself — and hope you catch every red flag. That’s why more teams are turning to Business Screen. ✅ Investigator-verified reports ✅ Human-sourced intelligence ✅ Tailored packages for every deal stage Swipe through to see how Business Screen compares to CLEAR and why context beats raw data every time.