#critical infrastructure

[epicstoriestime]

Why Aren’t We Using Counter-Drone Tech in NJ?

Counter-Unmanned Aerial Systems (C-UAS) Clusters of unidentified drones have been buzzing around New Jersey, raising eyebrows and concerns, especially near critical infrastructure. The U.S. has top-tier counter-drone systems—tech designed to track and neutralize UAVs—yet they aren’t being deployed here. Instead, officials are focused on monitoring and investigating, leaving the public wondering:…

[gwydionmisha]

China’s cyber army is invading critical U.S. services

[jcmarchi]

Derek Streat, CEO and Founder of DexCare – Interview Series

New Post has been published on https://thedigitalinsider.com/derek-streat-ceo-and-founder-of-dexcare-interview-series/

Derek Streat, CEO and Founder of DexCare – Interview Series

Derek Streat, CEO and Founder of DexCare, is an experienced entrepreneur with a track record of founding and leading six venture-backed companies, four of which have achieved successful acquisitions. His ventures have included scaling businesses to over $100 million in revenue and establishing non-profits that benefit more than half of all children with kidney transplants. Streat focuses on solving large-scale, impactful problems by leveraging innovative data solutions to bring transparency and efficiency to markets, reducing costs and delivering societal benefits.

DexCare is a care orchestration platform that optimizes healthcare delivery and workforce capacity while enhancing patient convenience. It integrates with existing systems to unify data, forecast demand, allocate resources, and guide patients to the most appropriate care, delivering actionable insights and streamlined operations.

DexCare was born out of your personal journey with healthcare, specifically in helping your child access critical care. How did this experience shape your vision for DexCare, and how does it continue to influence the company’s mission today?

Fifteen years ago, my three-year-old child needed a lifesaving kidney transplant. It was an arduous journey filled with sleepless nights as my wife and I struggled to navigate a fragmented healthcare system. We watched as our little one moved between specialists, surgeries, and intensive care, ultimately receiving a transplant. Through it all, I realized just how fortunate I was to have unfettered access to care. For many Americans, that’s not the case.

Over 37% of Americans live in healthcare deserts. My own experience, combined with years of working closely with healthcare systems, revealed a clear need to bridge the access gap for everyone. In fact, not every patient needs to see a physician – they need the right care, in the right place, at the right time. And that insight led me to found DexCare, a platform designed to orchestrate where and how care is delivered. By reducing provider burnout, creating capacity, and expanding access, we aim to serve more patients effectively. Incubated at Providence, DexCare spun out in 2021 and now proudly partners with leading health systems across the country, including Texas Health Resources, Tampa General, and Piedmont Healthcare.

You’ve successfully founded several healthcare-focused companies. What specific challenges did you encounter in founding DexCare, and how did your prior ventures prepare you for launching this care orchestration platform?

From idea to prototype, to raising capital and scaling, every startup faces familiar hurdles. In healthcare, these challenges are amplified by talent wars, long sales cycles, cautious capital markets, and an ever-shifting regulatory landscape. Success demands a careful balancing act. Having founded and exited multiple companies, I’ve been in the trenches and gained firsthand insight into what it takes to build resilient teams and products capable of thriving under pressure. These lessons became essential when launching DexCare and crafting a strategy to succeed amid the complexities of healthcare.

My foray into healthcare began with Medify, an intelligence company that used NLP technology to create structured data from the vast, global repository of medical literature. The platform made a real difference for patients with rare diseases, bringing together small, scattered populations into larger groups with meaningful insights. At its peak, one in ten doctors across the U.S. relied on our knowledge base to find treatments and therapies that could make a difference for their patients. Eventually, Medify became part of Alliance Health, a leading health network.

After Medify, I began tackling a different set of challenges, focusing on how technology could directly influence clinical practice through C-SATS.

An AI-powered platform, C-SATS leveraged robotics and machine learning to evaluate surgical performance, providing surgeons with actionable insights to improve their skills and patient outcomes. This work with AI—long before today’s hype— opened my eyes to the uncharted complexities of integrating advanced technology into a high-stakes environment like healthcare. While the platform sidestepped privacy concerns by using anonymized surgical footage, it surfaced deeper issues, as surgeons were apprehensive about being credentialed based on technology, as it had direct implications for their careers and livelihoods. This experience taught me that introducing innovation in healthcare requires more than technical expertise—it demands building trust with stakeholders and proactively addressing the unintended consequences that can emerge when technology intersects with human lives.

Throughout my career, I’ve focused on dismantling systemic barriers—scarce resources, disconnected data, and inequitable access—by leveraging technology rooted in practicality, not hype. When building DexCare, I prioritized data intelligence as the cornerstone of our AI applications. And this focus ensures clean, reliable, and unified data that powers how care is orchestrated, routed, and delivered. By exposing capacity imbalances—identifying overburdened providers and underutilized resources—we’re reimagining healthcare to optimize operations and to deliver better outcomes for patients.

DexCare was incubated within the Providence Health system. Could you talk about the advantages of developing a startup from within a large healthcare organization, and how that shaped DexCare’s growth?

DexCare was born within Providence to solve a key challenge in healthcare: balancing supply and demand by leveraging existing marketing, IT, and operational infrastructure. Being built inside a health system gave us an intimate understanding of the dual challenges facing healthcare today. For organizations, it’s the constant struggle to meet growing care demands with limited resources. And for patients, it’s the frustration of finding care when and where it’s needed. This perspective uniquely positions us to empower health systems with critical infrastructure for more effective digital discovery and access, while simultaneously optimizing system capacity. And our incubation within Providence allowed us to refine the platform before scaling to health systems nationwide.

AI in healthcare has been heralded as revolutionary, but it has also faced significant hurdles. How have you seen AI evolve in healthcare over the years, and where do you think it has fallen short of its potential?

The rise of AI in healthcare has sparked both excitement and caution. While AI is becoming more mainstream, significant hurdles remain before it can transform the industry. A recent survey revealed that 96% of healthcare CIOs see AI adoption as a competitive advantage, yet integration challenges—like system interoperability and workflow alignment—often stand in the way. And without seamless integration into the daily process, clinicians, physicians, and administrators are unlikely to embrace these tools.

The crowded landscape of over 14,000 AI-focused companies adds to the complexity, making it difficult for health systems to separate hype from solutions that deliver real value. Choosing the right technology partner requires more than evaluating features—it demands solutions that integrate smoothly, enhance existing workflows, and address real-world challenges.

But the core issue isn’t just finding the next tool—it’s unlocking the potential within healthcare’s existing data. Sustainable systems depend on harmonizing data across care records, workflows, and third-party platforms. Only then can we tackle real priorities, like freeing clinicians to focus on people over paperwork and closing critical care gaps. And this is precisely where DexCare fits in.

DexCare uses AI to optimize healthcare delivery by predicting and distributing care resources. Can you walk us through how the platform’s AI works and how it has impacted care delivery at scale?

DexCare’s care orchestration platform harnesses advanced data intelligence by consolidating key inputs—scheduling, modalities, utilization, locations, and costs—to determine where, when, and how care should be accessed. Our AI not only ingests and organizes massive data sets but also dynamically aligns care delivery with patient needs. For instance, the platform categorizes content—whether it’s an article on seasonal flu, preventive care, or specialized services—and matches it to the most appropriate pathways to care, all while understanding complex taxonomies and synonyms. The result? By linking relevant content to the most suitable venues of care, the platform ensures patients are guided seamlessly to the services they need, enhancing both access and outcomes.

The results speak for themselves. DexCare enables 40% more patients to receive care using the same clinical resources, drives a 24% increase in new patient acquisition, and saves over 34,000 hours of physician time. By eliminating unnecessary steps and presenting clear, actionable choices from the start, we’re transforming patient access and operational efficiency at scale—delivering measurable improvements for patients and providers.

AI has the power to automate tasks and streamline processes, but it can also create fear around job displacement in healthcare. How do you see AI impacting the healthcare workforce, and what strategies can mitigate these concerns?

Addressing fears of job displacement in healthcare begins with clarity. AI isn’t here to replace the human touch in care delivery—it’s here to complement it. Technology, including AI, augments the capabilities of healthcare professionals, but it’s not a silver bullet for addressing the growing gap between increasing patient needs and a shrinking physician workforce.

Platforms like DexCare demonstrate how AI can be a critical tool in extending the capacity of limited healthcare resources. By intelligently balancing workforce demands, controlling costs, and optimizing capacity, AI helps health systems operate more efficiently. This not only ensures patients receive the care they need but also alleviates burdens on providers, reducing burnout and creating a more sustainable healthcare environment. It’s about building smarter, more resilient systems.

What are some of the unintended consequences you’ve observed in the implementation of AI in healthcare, particularly in terms of accountability for AI-driven mistakes? How does DexCare address these ethical challenges?

When I was at C-SATS, we used robotics and machine learning to train surgeons and to improve patient outcomes. While innovative, this approach raised important questions about privacy, consent, surgeon autonomy, and the ethical use of data. These challenges highlighted a crucial truth: implementing AI in healthcare requires rigorous, standardized policies to ensure the safe and ethical use of the technology.

In healthcare, there is no margin for error—lives are at stake. This makes it imperative to establish clear guidelines and frameworks that can serve as a ‘North Star’ to navigate uncharted legal and ethical questions. And accountability and transparency must be at the heart of AI applications in healthcare. By focusing on data integrity and designing systems to enhance, not overshadow, human decision-making, we can advance innovation responsibly while addressing the needs of the industry.

While AI offers tremendous potential for improving access to care, what steps do you think healthcare systems need to take to ensure equitable AI adoption, especially for underserved populations?

AI adoption in healthcare, especially for underserved populations, requires a focus on data fidelity, diversity, and aggregation. In an industry beset by fragmented data silos, the ability to unify and analyze information is crucial. Generative AI has the potential to create life-saving connections by integrating patient records, population health disparities, and propensity models to improve diagnosis, treatment, and care outcomes. However, these advancements depend on using bias-free datasets at scale to avoid perpetuating inequities.

Responsibility doesn’t rest solely with health systems. A unified approach is needed, starting with standardizing AI deployment at scale. Sensible, national-level regulations can ensure AI improves our collective healthcare while, at the same time, must avoid overreach that stifles innovation. Overly restrictive measures risk hampering progress, but clear guidelines on infrastructure, usage, and data governance are essential. These standards can help address bias, mitigate risks, and foster a system where AI elevates care quality for all patients, not just the privileged few.

From a founder’s perspective, what advice would you give to entrepreneurs looking to bring AI into healthcare, considering the unique regulatory and ethical challenges of the industry?

Successful entrepreneurs, particularly in healthcare, must not only challenge the status quo but also reject the notion that the system is beyond fixing. The opportunities to improve healthcare are immense, but once you dive deep into the self-imposed complexities and the hurdles the industry presents, the scale of the problems can seem overwhelming. True innovation requires resilience—the ability to confront these challenges head-on and to remain steadfast in your mission. Your vision to improve care and outcomes must always outweigh the obstacles of scaling technology.

Success in healthcare isn’t just about the technology – it’s also about aligning with the needs of patients, providers, and systems, and having the resolve to smile even when the path gets steep. My advice: Stay adaptable, embrace setbacks, and focus on building solutions that solve for immediate, real-world problems.

Looking ahead, what are the most exciting AI advancements you foresee in the next 5–10 years for healthcare, and what specific areas do you think AI will struggle to penetrate?

Predicting the future is tricky—it’s uncertain and ever-changing. With thousands of companies exploring AI from every angle, the potential is incredible, but so are the challenges. What we do know, however, is that AI is poised to fundamentally reshape how care is accessed, delivered, and experienced. One of the most exciting advancements I foresee over the horizon is truly personalized medicine—tailored treatment plans and unique therapeutic “cocktails” designed to give each patient exactly what they need to heal and thrive.

Healthcare – long hamstrung by fragmented data and outdated systems – is on the brink of breaking free. And by connecting patient records, addressing population disparities, and using predictive models, AI has the power to create life-saving solutions while shifting the focus of healthcare toward greater access and consumer-centric care.

We’re still in the early stages of this journey and navigating unknowns. While we can’t predict the exact breakthroughs ahead, we know AI is steadily improving how care is delivered—driving better outcomes for patients and empowering providers. The progress already being made is inspiring, and I’m proud to contribute to this transformation.

Thank you for the great interview, readers who wish to learn more should visit DexCare. 

[code-of-conflict]

AI, Cybersecurity, and National Sovereignty

Introduction: The Role of AI in Cybersecurity

As artificial intelligence (AI) becomes integral to national security, cyber threats increasingly exploit AI-driven vulnerabilities. Both India and China face the challenge of securing their cyber infrastructure while mitigating espionage and offensive cyber operations. The risks include large-scale data breaches, intellectual property theft, and attacks on critical infrastructure. With AI enhancing the scope and speed of cyberattacks, national sovereignty is increasingly threatened by cyber vulnerabilities that transcend borders.

AI-Driven Cyber Threats and Espionage

China has heavily integrated AI into its cyber capabilities, using it to enhance espionage, cyber warfare, and information manipulation. AI-enabled cyber operations allow China to gather vast amounts of intelligence data through advanced hacking techniques. These tools are often deployed through state-sponsored groups, exploiting zero-day vulnerabilities and penetrating government and corporate networks worldwide​.

For example, in 2021, China was accused of orchestrating a large-scale cyber-attack targeting Microsoft Exchange servers, affecting over 30,000 organizations globally. This attack was designed to facilitate espionage, capturing sensitive information ranging from corporate intellectual property to government data​. China's cyber operations underscore the increasing use of AI in orchestrating sophisticated, large-scale intrusions that threaten national sovereignty.

India, while lagging behind China in offensive cyber capabilities, faces persistent cyber espionage threats from Chinese state-sponsored actors. The most notable incidents occurred during the 2020 India-China border standoff, where Chinese hackers targeted India's critical infrastructure, including power grids and government networks​. These attacks highlight the vulnerabilities in India's cybersecurity architecture and its need to enhance AI-driven defenses.

Vulnerabilities and National Sovereignty

AI-driven cyber threats pose significant risks to national sovereignty. For India, the challenges are magnified by the relatively underdeveloped nature of its cybersecurity infrastructure. Although the establishment of the Defence Cyber Agency in 2018 marked a step forward, India still lacks the offensive cyber capabilities and AI sophistication of China​. India's defensive posture primarily focuses on securing critical infrastructure and mitigating cyber intrusions, but it remains vulnerable to cyber espionage and attacks on its digital economy.

China's integration of AI into both military and civilian cyber systems, through its Military-Civil Fusion policy, has bolstered its ability to conduct large-scale cyber operations with deniability. This fusion allows China to leverage private sector innovations for military purposes, making it a formidable cyber power in the Indo-Pacific region​.

Case Studies: Cyber Confrontations

In 2019, a significant cyberattack targeted India's Kudankulam Nuclear Power Plant, which was traced back to North Korea, but was believed to be part of a broader effort involving Chinese actors. This incident highlighted the potential for AI-enhanced malware to target critical infrastructure, posing severe risks to national security.

Similarly, the 2020 Mumbai blackout, reportedly linked to Chinese hackers, emphasized how AI-driven cyberattacks can disrupt essential services, creating chaos in times of geopolitical tension​. These incidents illustrate how AI-driven cyber capabilities are increasingly weaponized, posing severe risks to India's sovereignty and its ability to protect critical infrastructure.

Implications for Future Conflicts

As AI continues to evolve, the cyber domain will become a primary battleground in future conflicts between India and China. AI-enhanced cyber operations provide both nations with the ability to conduct espionage, sabotage, and information warfare remotely, without direct military engagement. For China, these tools are integral to its broader geopolitical strategy, while India must develop its AI and cybersecurity capabilities to protect its national sovereignty and counteract cyber threats​.

Conclusion

The integration of AI into cybersecurity poses both opportunities and challenges for India and China. While China has aggressively developed AI-driven cyber capabilities, India faces an urgent need to enhance its defenses and develop its offensive cyber tools. As cyberattacks become more sophisticated, driven by AI, both nations will continue to grapple with the implications of these developments on national sovereignty and global security.

[onetechavenue]

The Median Recovery Costs for 2 Critical Infrastructure Sectors, Energy and Water, Quadruples to $3 Million in 1 Year, Sophos Survey Finds

Sophos, a global leader of innovative security solutions for defeating cyberattacks, recently released a sector survey report, “The State of Ransomware in Critical Infrastructure 2024,” which revealed that the median recovery costs for two critical infrastructure sectors, Energy and Water, quadrupled to $3 million over the past year. This is four times higher than the global cross-sector median.…

[ohmyfivedotcom]

When Cyber Attacks Are the Least of Our Worries: 5 Shocking Threats to Critical Infrastructure

Introduction paragraph explaining the significance of the list. Use key phrases related to the topic for SEO optimization. Imagine a world where the things we rely on every day suddenly vanish. No power, no water, no internet—sounds like a bad sci-fi movie, right? But it’s more real than you might think. The importance of critical infrastructure can’t be overstated. These systems are the backbone…

[gauricmi]

Safeguarding Our Nation: The Imperative of Critical Infrastructure Protection

In an interconnected world where technology reigns supreme, the protection of our critical infrastructure is paramount. Critical infrastructure forms the backbone of our society, encompassing systems and assets vital for national security, economic stability, and public health and safety. From power grids to transportation networks, water supplies to telecommunications, each component plays a crucial role in sustaining our way of life. Thus, the concept of Critical Infrastructure Protection (CIP) emerges as a cornerstone in ensuring the resilience and security of our nation.

At its core, Critical Infrastructure Protection (CIP) entails the proactive measures taken to safeguard essential assets and systems against a myriad of threats. These threats encompass a broad spectrum, ranging from natural disasters and cyberattacks to physical sabotage and terrorism. The interconnected nature of modern infrastructure magnifies the potential impact of such threats, underscoring the need for comprehensive and robust protection strategies. By prioritizing CIP efforts, we aim to mitigate vulnerabilities, enhance resilience, and minimize the cascading effects of disruptions across critical sectors.

One of the fundamental challenges in Critical Infrastructure Protection lies in the recognition of interdependencies among various infrastructure sectors. A disruption in one sector can often trigger ripple effects, causing widespread consequences across interconnected systems. For instance, a cyberattack targeting financial institutions can disrupt not only the banking sector but also impact transportation, energy, and communication networks. Therefore, a holistic approach to CIP is essential, encompassing cross-sector collaboration, information sharing, and risk management practices.

Get More Insights On This Topic: Critical Infrastructure Protection

Explore More Related Topic: Singapore Meetings, Incentives, Conferences and Exhibitions (MICE) Market

[pebblemaninoff]

thinking about critical infrastructures today. oh boy

[npi]

Multiple bridges on the Columbia River are vulnerable to ship strike, New York Times story notes

For the opening of our story here on The Cascadia Advocate about the collapse of the Francis Scott Key Bridge in Baltimore last week, I suggested that readers contemplate what would happen if there were a similar disaster on the maritime border between Washington and Oregon, writing: “Imagine if one of the vitally important bridges linking Washington and Oregon was hit by a big cargo ship and…

[little-p-eng-engineering]

Little P.Eng.'s Comprehensive Seismic Structural Services Aligned with ASCE 7-22 and NBCC Standards

In an era where architectural ambition pushes the limits of engineering, safeguarding structural integrity against natural calamities, particularly seismic activities, becomes paramount. This detailed exposé delves into the sophisticated seismic structural engineering services provided by Little P.Eng., a firm renowned for its compliance with the latest American Society of Civil Engineers (ASCE) 7-22 standards and the Canadian National Building Code (NBCC). Their work spans across Canada and the United States, encompassing a diverse range of buildings and non-structural elements, reflecting the pinnacle of safety, reliability, and innovation in modern construction.

1. Introduction

The unpredictable nature of seismic activities has long posed a significant challenge to the realms of construction and civil engineering. Within this volatile environment, Little P.Eng. has emerged as a beacon of reliability, offering cutting-edge seismic structural engineering services across Canada and the United States. Their adherence to the ASCE 7-22 and NBCC codes ensures not only the structural integrity of vast construction undertakings but also the safety and longevity of non-structural elements, affirming their position at the forefront of seismic resilience in contemporary infrastructure.

2. Understanding Seismic Structural Engineering

2.1. The Science of Earthquake Engineering

Before delving into Little P.Eng.'s specialized services, one must understand the core principles of seismic structural engineering. This discipline focuses on making buildings and non-structural components resistant to earthquake shocks through specialized planning, design, detailing, and, subsequently, construction. It encompasses geological science, material engineering, and structural analysis to develop structures capable of withstanding seismic disturbances.

2.2. Evolution of Seismic Codes: From ASCE 7-10 to ASCE 7-22

Seismic building codes are dynamic, evolving in response to the continuous advancements in engineering research and catastrophic lessons learned from each seismic event. The transition from ASCE 7-10 to ASCE 7-22 is a reflection of this evolution, marking significant strides in risk reduction and structural robustness, emphasizing not just human safety but also post-earthquake functionality and rapid recovery for communities.

3. Little P.Eng.’s Integration of ASCE 7-22 in Seismic Structural Engineering

3.1. Innovations in Seismic Design Philosophies

Little P.Eng. employs a forward-thinking approach to integrate the innovations outlined in ASCE 7-22. These include state-of-the-art seismic design philosophies involving base isolation, energy dissipation devices, and performance-based seismic design (PBSD), allowing for structures that are more flexible, absorb and dissipate seismic energy, and maintain structural integrity during earthquakes.

3.2. Site-Specific Hazard Analysis and Geotechnical Considerations

One of the critical aspects of ASCE 7-22 is the emphasis on site-specific hazard analyses. Little P.Eng.'s engineers led by Meena Rezkallah carry out comprehensive geotechnical evaluations, considering soil-structure interaction, liquefaction potential, and site-specific seismic hazard assessments. By understanding the geological variances across different regions in North America, they ensure that each design is intrinsically aligned with its environmental context.

4. Adherence to NBCC Standards: Expanding Safety Parameters Across Canada

4.1. Bridging Policies between Countries

While their services in the United States predominantly adhere to ASCE standards, Little P.Eng. seamlessly bridges engineering policies between the U.S. and Canada by aligning their practices with the NBCC. This code compliance not only underscores their versatility in handling cross-border projects but also reflects their commitment to upholding the highest safety and professional standards in every geographical locale.

4.2. Understanding NBCC’s Seismic Provisions

The NBCC has distinct seismic provisions, necessitating specialized knowledge and an adaptive engineering approach. Little P.Eng.'s expertise in Canadian seismic codes ensures that structural and non-structural components comply with regional regulations, catering to Canada's unique seismic challenges, especially in high-risk provinces.

5. Comprehensive Services for Buildings and Non-Structural Elements

5.1. Diverse Building Typologies

Little P.Eng.'s portfolio encompasses a variety of buildings, from residential high-rises and expansive commercial complexes to critical facilities like hospitals and emergency response centers. Each building type presents unique challenges, and the firm’s nuanced, context-oriented approach to seismic retrofitting and sustainable design practices sets industry standards.

5.2. Protecting Non-Structural Components

Beyond the buildings themselves, Little P.Eng. extends its engineering prowess to safeguard non-structural elements. These components, often overlooked, can pose significant hazards during seismic events. From architectural elements to mechanical and electrical systems, the firm implements exhaustive strategies to enhance the safety of these components, thereby protecting human life and minimizing economic loss.

6. Future Directions and Continuous Advancements

6.1. Embracing Technological Innovations

As the field of seismic structural engineering advances, Little P.Eng. remains committed to incorporating new technologies, including artificial intelligence and machine learning, for predictive analysis, design optimization, and risk management. Their continual investment in technology positions them as a leader in future-proofing structures against earthquakes.

6.2. Contribution to Global Seismic Safety Standards

Harnessing Advanced Engineering: Little P.Eng.'s Comprehensive Seismic Structural Services Aligned with ASCE 7-22 and CNBCC Standards in North America

7. Conclusion

Little P.Eng.’s comprehensive seismic structural engineering services, grounded in the latest ASCE and NBCC standards, represent a confluence of scientific mastery, innovative engineering, and a deep commitment to safeguarding human lives and investments. Their work across diverse building typologies and non-structural components in Canada and the United States cements their stance as a pivotal player in shaping resilient, sustainable, and safe urban landscapes. As seismic activity remains an unpredictable threat, the foresight and innovation of firms like Little P.Eng. are society's best bet for a safer tomorrow.

References

[1] American Society of Civil Engineers. (2022). Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE/SEI 7-22). ASCE.

[2] National Research Council Canada. (2020). National Building Code of Canada.

Tags:

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advanced materials

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CNBCC

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Seismic Bracing Experts

Located in Calgary, Alberta; Vancouver, BC; Toronto, Ontario; Edmonton, Alberta; Houston Texas; Torrance, California; El Segundo, CA; Manhattan Beach, CA; Concord, CA; We offer our engineering consultancy services across Canada and United States. Meena Rezkallah.

[yodasec-expose-news]

Unveiling the Latest Updates on the Chinese Cyber Army's Targeting of the Texas Power Grid

Introduction: Understanding the Threat Posed by the Chinese Cyber ArmyChina’s cyber army, including state-sponsored hacking groups affiliated with the People’s Liberation Army (PLA), such as “Volt Typhoon,” has been reported to target critical infrastructure and military installations in various locations, including Guam, Hawaii, and Texas[1]. The Chinese Ministry of State Security-affiliated…

[jcmarchi]

David Maher, CTO of Intertrust – Interview Series

New Post has been published on https://thedigitalinsider.com/david-maher-cto-of-intertrust-interview-series/

David Maher, CTO of Intertrust – Interview Series

David Maher serves as Intertrust’s Executive Vice President and Chief Technology Officer. With over 30 years of experience in trusted distributed systems, secure systems, and risk management Dave has led R&D efforts and held key leadership positions across the company’s subsidiaries. He was past president of Seacert Corporation, a Certificate Authority for digital media and IoT, and President of whiteCryption Corporation, a developer of systems for software self-defense. He also served as co-chairman of the Marlin Trust Management Organization (MTMO), which oversees the world’s only independent digital rights management ecosystem.

Intertrust developed innovations enabling distributed operating systems to secure and govern data and computations over open networks, resulting in a foundational patent on trusted distributed computing.

Originally rooted in research, Intertrust has evolved into a product-focused company offering trusted computing services that unify device and data operations, particularly for IoT and AI. Its markets include media distribution, device identity/authentication, digital energy management, analytics, and cloud storage security.

How can we close the AI trust gap and address the public’s growing concerns about AI safety and reliability?

Transparency is the most important quality that I believe will help address the growing concerns about AI. Transparency includes features that help both consumers and technologists understand what AI mechanisms are part of systems we interact with, what kind of pedigree they have: how an AI model is trained, what guardrails exist, what policies were applied in the model development, and what other assurances exist for a given mechanism’s safety and security.  With greater transparency, we will be able to address real risks and issues and not be distracted as much by irrational fears and conjectures.

What role does metadata authentication play in ensuring the trustworthiness of AI outputs?

Metadata authentication helps increase our confidence that assurances about an AI model or other mechanism are reliable. An AI model card is an example of a collection of metadata that can assist in evaluating the use of an AI mechanism (model, agent, etc.) for a specific purpose. We need to establish standards for clarity and completeness for model cards with standards for quantitative measurements and authenticated assertions about performance, bias, properties of training data, etc.

How can organizations mitigate the risk of AI bias and hallucinations in large language models (LLMs)?

Red teaming is a general approach to addressing these and other risks during the development and pre-release of models. Originally used to evaluate secure systems, the approach is now becoming standard for AI-based systems. It is a systems approach to risk management that can and should include the entire life cycle of a system from initial development to field deployment, covering the entire development supply chain. Especially critical is the classification and authentication of the training data used for a model.

What steps can companies take to create transparency in AI systems and reduce the risks associated with the “black box” problem?

Understand how the company is going to use the model and what kinds of liabilities it may have in deployment, whether for internal use or use by customers, either directly or indirectly. Then, understand what I call the pedigrees of the AI mechanisms to be deployed, including assertions on a model card, results of red-team trials, differential analysis on the company’s specific use, what has been formally evaluated, and what have been other people’s experience. Internal testing using a comprehensive test plan in a realistic environment is absolutely required. Best practices are evolving in this nascent area, so it is important to keep up.

How can AI systems be designed with ethical guidelines in mind, and what are the challenges in achieving this across different industries?

This is an area of research, and many claim that the notion of ethics and the current versions of AI are incongruous since ethics are conceptually based, and AI mechanisms are mostly data-driven. For example, simple rules that humans understand, like “don’t cheat,” are difficult to ensure. However, careful analysis of interactions and conflicts of goals in goal-based learning, exclusion of sketchy data and disinformation, and building in rules that require the use of output filters that enforce guardrails and test for violations of ethical principles such as advocating or sympathizing with the use of violence in output content should be considered. Similarly, rigorous testing for bias can help align a model more with ethical principles. Again, much of this can be conceptual, so care must be given to test the effects of a given approach since the AI mechanism will not “understand” instructions the way humans do.

What are the key risks and challenges that AI faces in the future, especially as it integrates more with IoT systems?

We want to use AI to automate systems that optimize critical infrastructure processes. For example, we know that we can optimize energy distribution and use using virtual power plants, which coordinate thousands of elements of energy production, storage, and use. This is only practical with massive automation and the use of AI to aid in minute decision-making. Systems will include agents with conflicting optimization objectives (say, for the benefit of the consumer vs the supplier). AI safety and security will be critical in the widescale deployment of such systems.

What type of infrastructure is needed to securely identify and authenticate entities in AI systems?

We will require a robust and efficient infrastructure whereby entities involved in evaluating all aspects of AI systems and their deployment can publish authoritative and authentic claims about AI systems, their pedigree, available training data, the provenance of sensor data, security affecting incidents and events, etc. That infrastructure will also need to make it efficient to verify claims and assertions by users of systems that include AI mechanisms and by elements within automated systems that make decisions based on outputs from AI models and optimizers.

Could you share with us some insights into what you are working on at Intertrust and how it factors into what we have discussed?

We research and design technology that can provide the kind of trust management infrastructure that is required in the previous question. We are specifically addressing issues of scale, latency, security and interoperability that arise in IoT systems that include AI components.

How does Intertrust’s PKI (Public Key Infrastructure) service secure IoT devices, and what makes it scalable for large-scale deployments?

Our PKI was designed specifically for trust management for systems that include the governance of devices and digital content. We have deployed billions of cryptographic keys and certificates that assure compliance. Our current research addresses the scale and assurances that massive industrial automation and critical worldwide infrastructure require, including best practices for “zero-trust” deployments and device and data authentication that can accommodate trillions of sensors and event generators.

What motivated you to join NIST’s AI initiatives, and how does your involvement contribute to developing trustworthy and safe AI standards?

NIST has tremendous experience and success in developing standards and best practices in secure systems. As a Principal Investigator for the US AISIC from Intertrust, I can advocate for important standards and best practices in developing trust management systems that include AI mechanisms. From past experience, I particularly appreciate the approach that NIST takes to promote creativity, progress, and industrial cooperation while helping to formulate and promulgate important technical standards that promote interoperability. These standards can spur the adoption of beneficial technologies while addressing the kinds of risks that society faces.

Thank you for the great interview, readers who wish to learn more should visit Intertrust.

[howdoesone]

How does one prioritize the distribution of vaccines during a pandemic?

Priority Distribution of Vaccines During a Pandemic Introduction During a pandemic, when vaccine supplies are limited, it becomes essential to prioritize the distribution of vaccines to maximize their impact on public health. Determining the order in which different population groups receive vaccines requires careful consideration of various factors, such as vulnerability to the disease, risk of…