Doug Hubbard

Site Behind Major SSN Leak Returns With Detailed Data on Millions: How to Opt Out - PCMag National Public Data is back with new owners, joining the ranks of other creepy, people-finding services. Here's how to get your profile removed from the site. National Public Data, a website infamous for its role in leaking millions of Social Security numbers last year, has returned with the ability to look up anyone's personal information.  The site shut down in December amid a wave of lawsuits against parent company Jericho Pictures after a breach exposed an estimated 272 million unique SSNs and 600 million phone numbers. Since then, the site has been relatively dormant. But today, we spotted the nationalpublicdata.com domain springing back to life with a new interface. Still, what makes the site disturbing is how you can freely look up people’s data and sometimes find accurate addresses, phone numbers, and dates of birth, along with a person’s relatives. None of the data is behind a paywall. That said, the new site isn’t much different from other people-finding services, which serve up similar data for free.  The new National Public Data adds: "On our people search website, you can find pretty much anything you need, from someone’s contact info, location, age, and birthday to workplace, relatives, and criminal records. No need to sift through different web pages." How to Remove Your Profile From National Public Data The new National Public Data site has an opt-out form to delete your profile data. However, many users are likely unaware of such an option, enabling these type of sites to continue trafficking their information. 🚨 Here's how to delete your data: Search your name on nationalpublicdata.com When you find your profile, click View Full Profile - Copy that URL - Go to https://lnkd.in/gCeBvE99 - Drop the URL into the Your Profile Link field and click Request Removal - Enter an email address, and the site will send you a message and ask you to click to confirm deletion. You need a separate email address for each profile you want to delete. - Consider submitting a throwaway email account you have access to in order to protect your privacy. We tried the opt-out function, and the site appears to remove your data immediately. Some in the comments say they haven't received the confirmation email. Check your spam folder and make sure you're dropping the full URL for your profile into the link box, not just the search results page. We contacted nationalpublicdata.com and Perfect Privacy, and we’ll update the story if we hear back. In the meantime, sites such as Atlas Privacy have emerged to help people remove their data from the open web and people-finding services. You can also check out PCMag's roundup of the Best Personal Data Removal Services. #cybersecurity #privacy #PII #databrokers #scum #cybercrime #fraud

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The problem is decades ago it was established that secure parts do not make for secure system. Peter Neumann's 2004 report is one good source for that, https://lnkd.in/gGPeqyx7. But systems thinkers would realize that without reading Neumann - security is an emergent property. Bottom line is that judging some component such as software as secure can only be done in context of use. Just another way compliance is not secure. Compliance (at least as practiced) does not account for the individual contexts. https://lnkd.in/g-5BBfwR

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When Code Compliance Fails at the Lashing: A $3.8M Lesson in "Systemic Integrity." For those of us in the building and fire code industry, we know that a "rated system" is only as good as its weakest connection. The NTSB just released its final report on the Genius Star XI cargo ship fires (DCA24FM013). While the headline says "Lithium-Ion Battery Fire," the true culprit wasn't the chemistry—it was a failure of securing methods. The Incident: During a storm in the North Pacific, 41 Battery Energy Storage System (BESS) units broke free. The failure wasn't in the batteries themselves, but in the lashing belts. The resulting mechanical damage caused a thermal runaway that burned for days. Why this is a "Code Concepts Group" Masterclass: The "Listed" vs. "Installed" Gap: The BESS units were likely built to rigorous standards, but the method of securement (the "installation") failed to account for the dynamic loads of a North Pacific winter. In our world, this is the equivalent of a fire-rated door being undercut or a sprinkler head being obstructed. The component is fine; the assembly is compromised. Redundancy isn't enough if the Physics is wrong: The NTSB noted that the lashing belts weren't properly seated in the winches. This is a classic means and methods failure. Even if you have the right "code-compliant" materials, if the execution deviates from our expected practices and code compliance, the safety rating is effectively zero. As we see more BESS installations (per IFC or NFPA 855), this incident reminds us that mechanical protection is just as vital as thermal monitoring. If a structural shift or impact can bypass the internal safety of a battery, our fire codes must prioritize the "envelope" as much as the "energy." The Question for the Group: In your inspections or plan reviews, where are you seeing the biggest gap between the Engineered Safety Design and the Physical Reality on-site? Are we focusing too much on the "listing" of the device and not enough on the "lashing" of the system? #FireCode #LifeSafety #BESS #BuildingCodes #NTSB #FireProtection #NFPA855 #RiskManagement https://lnkd.in/gmRZjs47

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This new white paper "Steps Toward AI Governance" summarizes insights from the 2024 EqualAI Summit, cosponsored by RAND in D.C. in July 2024, where senior executives discussed AI development and deployment, challenges in AI governance, and solutions for these issues across government and industry sectors. Link: https://lnkd.in/giDiaCA3 * * * The white paper outlines several technical and organizational challenges that impact effective AI governance: Technical Challenges: 1) Evaluation of External Models:  Difficulties arise in assessing externally sourced AI models due to unclear testing standards and development transparency, in contrast to in-house models, which can be customized and fine-tuned to fit specific organizational needs. 2) High-Risk Use Cases: Prioritizing the evaluation of AI use cases with high risks is challenging due to the diverse and unpredictable outputs of AI, particularly generative AI. Traditional evaluation metrics may not capture all vulnerabilities, suggesting a need for flexible frameworks like red teaming. Organizational Challenges: 1) Misaligned Incentives: Organizational goals often conflict with the resource-intensive demands of implementing effective AI governance, particularly when not legally required. Lack of incentives for employees to raise concerns and the absence of whistleblower protections can lead to risks being overlooked. 2) Company Culture and Leadership: Establishing a culture that values AI governance is crucial but challenging. Effective governance requires authority and buy-in from leadership, including the board and C-suite executives. 3) Employee Buy-In: Employee resistance, driven by job security concerns, complicates AI adoption, highlighting the need for targeted training. 4) Vendor Relations: Effective AI governance is also impacted by gaps in technical knowledge between companies and vendors, leading to challenges in ensuring appropriate AI model evaluation and transparency. * * * Recommendations for Companies: 1) Catalog AI Use Cases: Maintain a centralized catalog of AI tools and applications, updated regularly to track usage and document specifications for risk assessment. 2) Standardize Vendor Questions: Develop a standardized questionnaire for vendors to ensure evaluations are based on consistent metrics, promoting better integration and governance in vendor relationships. 3) Create an AI Information Tool: Implement a chatbot or similar tool to provide clear, accessible answers to AI governance questions for employees, using diverse informational sources. 4) Foster Multistakeholder Engagement: Engage both internal stakeholders, such as C-suite executives, and external groups, including end users and marginalized communities. 5) Leverage Existing Processes: Utilize established organizational processes, such as crisis management and technical risk management, to integrate AI governance more efficiently into current frameworks.

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Please find attached a compendium of files from the US Pacific Northwest National Laboratories (PNNL). (Thanks to the Jeffrey Taft Substack!) The documents cover the following topics and are especially pertinent if you are involved in the AEMO CER data exchange work... 1. Networking Paradigms for High-DER Distribution Grids – illustrates modern communication network structures for system control, distribution feeder to substation aggregation, intra-substation networks, laminar networks, distributed computing infrastructure, and network for transactive networks. 2. DER Telemetry Communication Architecture for ESOs, DSOs, and System Operators – communication and data storage issues for systems with multiple DER aggregators with inter-penetrated service areas. 3. Roles and Responsibilities for Distribution Grids (note US centric observations here): DER Sensing and Communication Networks – an analysis of the appropriate roles and responsibilities for distribution grid operator sensing and communications vs. energy services organization (e.g. DER aggregators) sensing and communications. 4. Sensor Network Issues for Advanced Power Grids – this paper includes diagrams for distributed data collection in distribution grids using wireless field area communication networks and substation aggregation. 5. Connectivity, Centrality, and Bottleneckedness: On Graph Theoretic Methods for Power Systems – describes the use of basic graph theory concepts to evaluate structural properties of communication networks. 6. Determining Data Transportability for Grid Observability Tool – this slide deck shows how to use advanced graph theory to assess the suitability of a communication network to transport grid telemetry data without the need to resort to use cases. 7. Recommended Approach to a NASPInet Architecture. 8. IEC 61850-90-5 An Overview – PAC World article by Herb Falk on the use of IEC 61850-90-5 for PMU communications. 9. A Comparison of Phasor Communication Protocols 10. Network Timing Distribution – this slide deck shows how to use communication networks to distribute timing across organizational or other boundaries using IEEE 1588 (Precision Time Protocol). 11. The Impact of 5G Telecommunications Technology on US Grid Modernization 2017–2025. Also included are WiFi, Bluetooth, Zigbee, Z-wave, CEBus, and LTE. Explains why 5G had little impact on electric utilities through 2025.

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TL;DR: The USG’s NTM bias leads to over-reliance on classified assets for routine tasks, slow down support, and commercial imagery can handle 97% of needs, freeing those resources for sensitive ops and boosting efficiency with partners. After several government meetings in recent weeks, and since SkyFi has existed, feedback shows how this NTM bias creates bottlenecks for warfighters and intel professionals. They default to national technical means out of habit, even when unclassified options would suffice, which fragments data across sources and slows decisions in contested environments. Data and imagery providers, both government and commercial, compete for market share/job security without focusing on what customers need to succeed in combat. Bureaucracy slows down information flow. Go ask any warfighter how satisfied they are with speed of imagery delivery from government agencies. Key takeaways: Sharing unclassified imagery with partner nations builds stronger alliances without restrictions. We fight in joint environments, but try sharing an image in the INDOPACOM region with partners for targeting and tell me how simple it is. I’ll wait. Tools like TAK must work in denied areas where standard channels fail. The environment we train in differs from the one we fight in. Trust me on that. If you’ve communicated in a heavy EW setting, even a basic radio transmission among a team becomes impossible. Cut file sizes for rapid transfers over limited bandwidth. Deliver targeted data to avoid overload; an FPV drone pilot needs just their lane, not the full picture. Shift to commercial imagery for most tasks to ease the NTM load. Encouraging/training partner nations to use unclassified imagery aligns with burden-sharing in the National Defense Strategy. We need a unified platform that handles both classified and unclassified data. To be clear, it has to function well, not sit unused and disliked like some $615.7m system built by a prime 😉. This ends constant switching and streamlines access for all users. That’s the end goal. And of course, we are addressing all of this at SkyFi :)

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