Trust as a vector in digital systems

This paper examines how trust is built or challenged among patients and healthcare professionals using AI-based triage systems in Swedish primary care. 1️⃣ Trust relies on patients’ ability and willingness to provide accurate information during AI-guided symptom reporting. 2️⃣ Some patients exaggerate symptoms to gain attention, driven by fears the AI might dismiss their concerns. 3️⃣ Patients’ digital skills and prior experience with similar tools influenced how effectively they used the AI application. 4️⃣ Concerns about how symptom data is used and stored shaped how openly patients interacted with the AI system. 5️⃣ AI outputs must align with healthcare professionals’ clinical reasoning, especially in complex or nuanced cases. 6️⃣ Experienced professionals were more skeptical of AI suggestions, using them as checks rather than guides, unlike less experienced peers. 7️⃣ The AI’s rigid, symptom-focused questioning often failed to capture patient complexity, limiting trust and utility. 8️⃣ Emotional responses, especially in vulnerable situations, shaped user trust more than cognitive evaluations alone. 9️⃣ Professional oversight was critical—healthcare workers acted as a safeguard against potential AI errors or oversights. 🔟 Both groups emphasized the need for clear roles, responsibilities, and guidelines for interpreting and acting on AI-generated information. ✍🏻 Emilie Steerling, Petra Svedberg, Per Nilsen, Elin Siira, Jens Nygren. Influences on trust in the use of AI-based triage—an interview study with primary healthcare professionals and patients in Sweden. Frontiers in Digital Health. 2025. DOI: 10.3389/fdgth.2025.1565080

“There will be more AI Agents than people in the world.” – Mark Zuckerberg As AI grows, autonomous agents powered by LLMs (large language models) take on critical tasks without human oversight. While these systems hold incredible potential, they also face significant risks: manipulation through biased data, unreliable information retrieval, and prompt engineering, all of which can result in misleading outputs. At Chaos Labs, we’ve identified a critical risk: AI agents being unknowingly trained on manipulated, low-integrity data. The result? A dangerous erosion of trust in AI systems. In our latest essay, I dive deep with Reah Miyara, Product Lead, Model Evaluations at OpenAI. https://lnkd.in/eB9mPQWW Key insights from our essay -> The Compiler Paradox: Trust in foundational systems can be easily compromised. "No matter how thoroughly the source code is inspected, trust is an illusion if the compilation process is compromised." LLM Poisoning: LLMs are susceptible to “poisoning” through biased training data, unreliable document retrieval, and prompt injection. Once biases are embedded, they taint every output. RAG (Retrieval-Augmented Generation): While designed to make LLMs more accurate, RAG can amplify false information if external sources are compromised. Conflicting Data: LLMs don't verify facts—they generate answers based on probabilities, often leading to inconsistent or inaccurate results. Attack Vectors: LLMs can be attacked through biased data, unreliable retrieval, and prompt engineering—allowing adversaries to manipulate outputs without altering the model. The Path Forward -> Trust in LLMs must go beyond surface-level outputs and address the quality of training data, retrieval sources, and user interactions. At Chaos Labs, we’re actively working on solutions to improve the reliability of AI systems. Our vision for the future is simple: With GenAI data exploding, verified truth and user confidence will be an application’s competitive edge. To get there, we’re developing solutions like AI Councils—a collaborative network of frontier models (e.g., ChatGPT, Claude, LLaMA) working together to counter single-model bias and enhance reliability. If these challenges excite you, we want to hear from you.

Executive Summary This report, “Electronic Signatures: Enabling Trusted Digital Transformation”, co-authored by Chris Tullis, Nay Constantine, and Adam Cooper💯 and supported by The World Bank Group’s Identification for Development (#ID4D) initiative and the Korea-World Bank Partnership Facility (#KWPF), provides a comprehensive overview of #electronicsignatures as essential tools for secure, reliable, and scalable digital transactions. It outlines best practices, trust frameworks, and regulatory considerations that guide #governments and #organizations in implementing robust #electronicsignatureecosystems. Key Themes: 1. Foundational Role of Trust Trust is the cornerstone of #electronictransactions, where electronic signatures ensure #authenticity, intent, and data integrity. These signatures digitally fulfill functions traditionally served by handwritten signatures, thereby enabling secure online transactions without in-person interaction. 2. Risk-Based Trust Frameworks The report emphasizes a tiered approach, recommending multiple levels of trust that balance #security with accessibility. This enables flexibility for low-risk transactions (e.g., #ecommerce) and high-assurance solutions (e.g., #financialcontracts), ensuring practical adoption across varied contexts. 3. Legal and Regulatory Frameworks Legal frameworks must recognize electronic signatures as legally equivalent to handwritten ones, establishing a common trust baseline domestically and internationally. Mutual recognition laws and standards foster cross-border interoperability, essential for #globaldigitalcommerce. 4. Technological Approaches Different technologies, including public key infrastructure (#PKI) and #cryptographictools, support secure electronic signatures. This layered approach to #digitaltrust protects against tampering, ensuring document integrity while aligning with technological neutrality to support ongoing #innovation. 5. Adoption Challenges and Recommendations The report discusses challenges such as high implementation costs, user accessibility, and digital literacy. #governments are encouraged to align electronic signature frameworks with existing digital identity systems to boost trust and adoption, particularly in low-income regions. The report concludes with strategic, legal, and technical guidance, advocating for adaptive, scalable policies that enable secure digital interactions, boost economic inclusion, and advance global #digitaltransformation.

The invisible thread securing the energy transition isn't a molecule—it's a verifiable data point. As we scale up hydrogen, CCS, and low-carbon fuels, the risk of greenwashing and data fraud grows. How can we trust that a "green" molecule is truly green across a global supply chain? A recent UN/CEFACT white paper provides a powerful answer. 🔍 Key Industry Insights From "Push" to "Pull": The future of supply chains is shifting from pushing paper and PDFs to a digital "pull" model. Authorized partners will use Globally Unique Identifiers (GUIs) to access the specific data they need, on demand. This creates a single, trusted source of truth. The D-R-V Standard: For an identifier to be effective, it must be Discoverable, Resolvable, and Verifiable (D-R-V). This isn't just a barcode; it's a cryptographically secure "digital passport" that proves an asset's origin, authenticity, and ESG attributes with certainty. Building Digital Trust: This framework is foundational for verifying the carbon intensity of hydrogen, ensuring the chain of custody for captured CO2, and validating the sustainability of biofuels. It moves ESG from a reporting exercise to a verifiable, operational reality. 🎯 Career Lens This shift creates a massive opportunity for professionals who can bridge physical assets and digital trust. High-Value Skills: The ability to design, manage, and audit these new digital-physical systems is becoming critical. Roles in digital transformation, supply chain analytics, and tech-focused ESG compliance are seeing their strategic value skyrocket. A Tip for Engineers & PMs: Start thinking about how to embed D-R-V principles into your projects. How can you tag a shipment of sustainable aviation fuel (SAF) so its carbon footprint is verifiable from the refinery to the jet engine? That's the billion-dollar question. 🧠 Strategic Reflection This is about more than just tracking; it's about building verifiable integrity at scale. What if you built a 90-day plan to reposition yourself as the expert who ensures the digital integrity of your company's decarbonization claims? AI-powered assessment tools can help map your current skills to these emerging "digital trust" roles. 💡 Action Steps Get fluent: Familiarize yourself with the concepts in the UNECE "Globally Unique Identifiers" white paper and emerging standards like the verifiable Legal Entity Identifier (vLEI). Ask the right question: In your next project meeting, ask: "How do we verifiably prove the origin and attributes of our assets to our stakeholders?" 🚀 Engagement Prompt How is your organization preparing to build this layer of digital trust into its physical supply chains? I'm curious to hear what challenges and opportunities you see. #EnergyTransition #DigitalTransformation #SupplyChain #Hydrogen #ESG #Decarbonization #FutureOfWork #Leadership #CareerDevelopment

Zero Trust Agile Zero Trust (ZT) is a security mindset that assumes no user, device, or system is to be trusted by default, even if inside the network. Instead of granting broad access based on location or credentials, ZT continuously verifies identity, context, and behavior before allowing access to systems, data, or code. ZT applies to Agile teams in two ways: in development (securing the people, processes, and tools used to build software) and in the product (protecting users and data). Agile teams move fast, but without strong security, they may expose sensitive data, development pipelines, or customers to cyber threats. Zero Trust in Development Agile teams work in distributed environments and use cloud-based tools. Traditional security models assume internal networks are safe. ZT doesn’t. Every access request, whether from a developer, an automation script, or a third-party integration, is verified. An unsecured pipeline can introduce vulnerabilities. ZT prevents unauthorized code changes by enforcing strict identity verification for developers pushing code, role-based access control (RBAC) to limit who can modify repositories, and cryptographic verification so only trusted artifacts reach production. Agile developers work across devices and locations. MFA and device posture checks verify that only trusted users and devices access development tools. Just-in-time access grants privileges temporarily. Data encryption protects code and credentials, even if a device is compromised. Agile teams use open-source libraries and third-party tools, which can introduce supply-chain risks. ZT mitigates them with automated dependency scanning, cryptographic verification, and continuous monitoring of integrations. Zero Trust in the Product Security doesn’t stop at development. The product itself must enforce ZT principles to protect customers, data, and integrations. A ZT product never assumes users are who they claim to be. It enforces strong authentication using MFA and passwordless login, continuous verification that checks behavior for anomalies, and granular role-based access so users only access what they need. APIs and microservices are attack vectors. ZT requires that even internal services authenticate and validate requests. API authentication and authorization use OAuth, JWT, and mutual TLS. Rate limiting and anomaly detection prevent abuse. Encryption of data in transit and at rest keeps intercepted data unreadable. ZT means each system, user, and process has the least privilege necessary. Session-based access controls dynamically revalidate permissions. End-to-end encryption secures data, even if intercepted. Data masking and tokenization protect sensitive information. Double Zero Agile teams can’t just build software fast, they have to build it securely. Embedding ZT in development means only the right people, processes, and tools can modify code. Embedding ZT in the product means the software itself protects users and data.

If you’re a hardware enthusiast like me, you’ve probably read articles about #Caliptra in recent years. I recently dove deep into it and found it to be a fascinating intersection of hardware, security, and open-source collaboration. We’re living in an era where AI threats to digital systems are becoming more sophisticated. So, the attackers are not just targeting software anymore, but every hardware as well. So, a group of giants—#AMD, #Google, #Microsoft, and others—got together and said: “Hey, the current Root of Trust (RoT) implementations are all proprietary black boxes. How do we know what’s happening under the hood? Why isn’t there a secure, auditable, and transparent way to implement trust at the silicon level?” That’s where Caliptra was born. A Root of Trust (Commonly known as #RoT), for those new to the term, is the foundational security component of a chip—it’s the first thing that boots up, verifies #firmware, and ensures that everything in the system is authentic and hasn’t been tampered with. But until Caliptra, RoTs were locked down, custom-built for different companies, and often couldn’t be inspected or verified externally. Caliptra changed the game. It’s the first #opensource, silicon-proven Root of Trust IP that’s been co-designed with transparency, auditability, and flexibility in mind. The entire goal? Build a trust anchor that’s vendor-agnostic and usable by anyone designing a chip—be it a #CPU, #GPU, #accelerator, or #SoC. Caliptra seamlessly integrates between hardware and firmware. It comes with: - A RISC-V core that executes secure boot and cryptographic operations. Embedded cryptographic engines for hashing, signing, and verifying firmware blobs. - A lightweight firmware layer, open-sourced and testable, that manages measurements and attestation. - And hooks into the host processor and system management components to ensure that before your OS or hypervisor boots, the system is verified and locked down. So instead of trusting a closed chip and praying it’s secure, now companies are verifying and customizing their trust foundation. As more companies adopt Caliptra, it’s fast becoming the gold standard in hardware-based security for the modern era. Whether it’s booting up a cloud server, authenticating a mobile device, or enabling trusted AI accelerators, Caliptra ensures that the first step is always a secure one—and we can all verify it ourselves. It’s a rare example of collaborative, open innovation in an area that's generally guarded and opaque which I found fascinating.

Here's how we maintain trust in digital interactions when AI can generate convincing people, content, and behavior on demand 👇 During a recent live podcast we held with Charles Walton and Martin Kuppinger, this issue took center stage and a clear insight emerged: As AI agents begin handling tasks such as purchases, bookings, and account management, trust becomes increasingly critical. How can a retailer know that an AI agent is truly acting on behalf of a real customer? Did the customer authorize this specific transaction? Is the agent operating within agreed limits? Verifiable credentials make this possible. These tamper-proof digital files containing identity and permission data act as verifiable delegations of authority, binding a user's consent to an agent's identity and role. They can be used to prove that an agent... ...is tied to a real user or organization ...has been granted explicit permissions to perform specific actions, like "this agent can make purchases up to $200 at this retailer" ...is operating within a defined scope, context, and timeframe These credentials can be attached to every transaction the agent initiates, providing recipient systems with a fast and reliable way to verify not only who the agent is but also what it's allowed to do and on whose behalf. The future won't be human-only. AI will mediate more and more of our interactions, from banking and healthcare to customer service and travel. That makes decentralized identity more than a privacy tool. It's becoming a foundational component of digital infrastructure. Martin Kuppinger emphasized that we must begin thinking in terms of "AIdentity", the intersection of AI and identity. This includes not only verifying AI agents themselves but also managing the relationship between users and their AI-powered assistants and ensuring that personal data remains protected in machine-learning contexts. Done right, this trust layer blocks fraud and unlocks better experiences: > Faster onboarding and fewer drop-offs > Richer personalization with stronger privacy > Confidence in who (or what) you're interacting with across platforms We've recently written a whitepaper on how digital verifiable credentials can solve this problem. We're currently sharing it with a few key partners, but if you'd like to read it, I can send it to you. Just send me a message.

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬: 𝗧𝗿𝘂𝘀𝘁 𝗮𝘀 𝘁𝗵𝗲 𝗖𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 As Industry 5.0 takes shape, trust becomes the defining factor in securing the future of industrial ecosystems. With the convergence of AI, digital twins, IoT, and decentralized networks, organizations must adopt a structured trust architecture to ensure reliability, resilience, and security. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗿𝘂𝘀𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬? With the rise of AI-driven decision-making, digital twins, and decentralized networks, industrial ecosystems need a robust trust architecture to ensure reliability, security, and transparency. 𝗧𝗵𝗲 𝗧𝗿𝘂𝘀𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟱.𝟬 J. Mehnen from the University of Strathclyde defines six progressive trust layers : 𝗦𝗺𝗮𝗿𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 – The foundation of Industry 5.0 trust. This layer ensures secure IoT networks, smart sensors, and seamless machine-to-machine communication for industrial automation. 𝗗𝗮𝘁𝗮-𝘁𝗼-𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 – Moving beyond raw data, this layer integrates AI-driven analytics, real-time insights, and multi-dimensional data correlation to enhance decision-making. 𝗖𝘆𝗯𝗲𝗿 𝗟𝗲𝘃𝗲𝗹 – The backbone of digital security, incorporating digital twins, simulation models, and cyber-trust frameworks to improve system predictability and integrity. 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 𝗟𝗲𝘃𝗲𝗹 – AI-powered diagnostics, decision-making, and remote visualization ensure predictive maintenance and self-learning systems that minimize operational disruptions. 𝗦𝗲𝗹𝗳-𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – AI-driven systems that self-optimize, self-configure, self-repair, and self-organize, reducing dependency on human intervention. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 – The highest level of trust, where decentralized computing, autonomous decision-making, and blockchain-based governance eliminate single points of failure and ensure system-wide resilience. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗔𝗜: 𝗧𝗵𝗲 𝗖𝗼𝗿𝗲 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 To achieve a trusted Industry 5.0 ecosystem, organizations must embrace a structured framework : 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 – Ensuring ethical AI, traceable decision-making, and accountable automation. 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 – Withstanding cyberattacks and operational disruptions. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 – Protecting data, IoT devices, and industrial networks from cyber threats. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 – Ensuring system performance across various conditions. 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Enabling auditability, transparency, and regulatory compliance in automation. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻 – Implementing policy-driven AI and decentralized oversight mechanisms.  𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 As industries embrace AI, smart factories, and autonomous supply chains, trust becomes the new currency of industrial success. Ref :https://lnkd.in/dz998J_6

#blockchain | #digitalidentity | #crossborder | #trade : "Unlocking Trade Data Flows with Digital Trust Using Interoperable Identity Technology" The paper reviews the current challenges in unlocking cross-border data flows, and how interoperability of digital identity regimes using high level types of decentralized technologies can overcome this with active public-private partnerships. Decentralized identity technologies, such as verifiable credentials (VCs) and decentralized identifiers (DIDs), coupled with interoperability protocols can complement the current Web3 infrastructure to enhance interoperability and digital trust . It is noted in the World Economic Forum White Paper that global trust worthiness is an important identity system principle for future supply chains, as this process of dynamically verifying counterparts through digital identity management and verification is a critical step in establishing trust and assurance for organizations participating in digital supply-chain transactions. As the number of digital services, transactions and entities grow, it is crucial to ensure that digitally traded goods and services take place in a secure and trusted network in which each entity can be dynamically verified and authenticated. Web3 describes the next generation of the internet that leverages blockchain to “decentralize” storage, compute and governance of systems and networks, typically using open source software and without a trusted intermediary. With the new iteration of Web3 being the next evolution of digitalized paradigms, several new decentralized identity technologies have become an increasingly important component to complement existing Web3 infrastructure for digital trade. VCs are an open standard for digital credentials, which can be used to represent individuals, organizations, products or documents that are cryptographically verifiable and tamper-evident. The important elements of the design framework of digital identities involves three parties – issuer, holder and verifier. This is commonly referred to the self sovereign identity (SSI) trust triangle. The flow starts with the issuance of decentralized credentials in a standard format. The holder presents these credentials to a service provider in a secure way. The verifier then assesses the authenticity and validity of these credentials. Finally, when the credential is no longer required, the user revokes it. This gives rise to the main applications of digital identities and VCs in business credentials, product credentials and document identifiers in the trade environment involving businesses, goods and services. EmpowerEdge Ventures