# trust Trust in computing and computer security is the measured confidence that a system, device, user, or software will behave as expected under defined policies. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Build Trust on a Short Software Project DevFeed: [Build Trust on a Short Software Project]() Original publisher: [Read original article]() Author: Viviana Rosas Published: 2026-09-17T12:00:17Z Content type: opinion Language: en Sources: [Atomic Object]() Topics: [trust](), [Software](), [Testing]() Tags: [client](), [progress](), [project](), [project-communication](), [project-team-management](), [regression](), [testing](), [the-software-life](), [trust]() ### AI overview This article explains how software teams can build client trust during short projects. It recommends frequent feedback and check-ins, making progress visible, and using a limited regression test suite focused on important functionality. ### Source excerpt On a short project, clients need evidence not only that work is happening, but that the team knows when to continue, when to change direction, when to raise concern, and how to leave others capable of carrying the work forward. 1. Close the Feedback Loop - Show Responsiveness. It's extremely important to get feedback quickly [...] The post Build Trust on a Short Software Project appeared first on Atomic Spin. ## Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features DevFeed: [Stack Overflow for Agents adds a ChatGPT plugin, persistent knowledge sharing, and trust validation features]() Original publisher: [Read original article]() Author: Phoebe Sajor, David Gibson Published: 2026-09-15T17:00:00Z Content type: release Language: en Sources: [Stack Overflow Blog]() Topics: [Stack Overflow](), [Artificial Intelligence](), [API](), [Persistence](), [trust]() Tags: [agentic-ai](), [agents](), [ai](), [api](), [chatgpt](), [coding-agents](), [community](), [persistence](), [se-stackoverflow](), [se-tech](), [stack-overflow](), [trust](), [validation]() ### AI overview Stack Overflow describes updates to Stack Overflow for Agents, its API-first knowledge exchange for agents. The article discusses preserving solutions beyond individual sessions, adding trust scores and validation gateways, and introducing a ChatGPT plugin. ### Source excerpt We've learned a lot in the last three months since launching Stack Overflow for Agents, our API-first knowledge exchange for agents. Here's a few of our findings, what's new on the platform (including our new ChatGPT plugin), and how we're continuing to build Stack Overflow. ## What is CIAM in 2026 and why does it matter? DevFeed: [What is CIAM in 2026 and why does it matter?]() Original publisher: [Read original article]() Author: Ravleen Kaur Published: 2026-09-15T00:00:00Z Content type: tutorial Language: en Sources: [Twilio Blog]() Topics: [identity and access management](), [trust](), [ai-governance](), [Security](), [Artificial Intelligence](), [IAM](), [Generative AI]() Tags: [ai-agents](), [ai-governance](), [email](), [fraud](), [fraud-detection](), [identity](), [identity-and-access-management](), [industry-insights](), [messaging](), [mobile](), [trust]() ### AI overview This article explains how customer identity and access management (CIAM) is evolving from point-in-time password checks into a continuous trust and control layer. It describes CIAM's role in authenticating users, governing delegated authority for AI agents, evaluating risk across channels, and balancing low-friction access with fraud detection. It also distinguishes customer identity from workforce IAM. ### Source excerpt Discover how customer identity and access management (CIAM) uses continuous trust, deepfake defense, and agentic AI governance to protect users. ## The contagion of fear DevFeed: [The contagion of fear]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T21:18:13Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [anthropic](), [Artificial Intelligence](), [trust](), [Large Language Model]() Tags: [ai](), [ai-2-236](), [ai-ethics](), [ai-ethics-343](), [anthropic](), [anthropic-336](), [bryan-cantrill](), [bryan-cantrill-13](), [ethics](), [trust]() ### AI overview Bryan Cantrill argues that sweeping claims about AI causing catastrophic harm rely on vague extrapolation, including unsupported references to critical infrastructure and bioweapons. The article calls on domain experts to make alarmist claims more carefully and avoid abusing public trust. ### Source excerpt The contagion of fear Bryan Cantrill responds to the tweet by former Anthropic employee Jacob Coxon confirming that many Anthropic researchers believe AI "could kill us all by the end of the decade". Bryan shares a story of his own youthful mistakes causing unjustified panic among less technical peers, and warns against doing the same: These ghoulish claims strike brazenly at the hearth, and given the obvious importance of AI, it is unsurprising that they have leapt into the mainstream, with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not an expert on critical infrastructure, nor on bioweapons -- nor, for that matter, on extinction. [...] That said, we should not expect the public to understand LLMs, critical infrastructure, bioweapons, extinction biology, etc. -- that burden must lie with those making the claim. The lesson that I learned (shamefully) decades ago is that domain experts, by way of their expertise, implicitly hold the public's trust -- and we must not abuse it. It is incumbent upon us to be circumspect in our claims -- and maximally so when raising the alarm. Bryan talked about his doubts about the bioweapons concerns in the recent episode of Oxide and Friends that I joined. You can hear more of his thoughts on that starting at 51m44s in that episode. Here's 57m04s: I really think we need to be careful because it's so easy to be overcome with fear when we kind of make up these... it can give you biological weapons. Like, how? I mean, can we please have a biologist weigh in on this? Or can we have like someone who's got experience with bioweapons? [...] The bioweapon thing just gets under my fingernails because it leaves so much to the imagination that we insert with fear. Via Lobste.rs Tags: ai, anthropic, bryan-cantrill, ai-ethics ## From AI Code to Trusted Software: Harness Engineering in Practice DevFeed: [From AI Code to Trusted Software: Harness Engineering in Practice]() Original publisher: [Read original article]() Author: Travis Frisinger Published: 2026-09-04T21:55:00Z Content type: opinion Language: en Sources: [8th Light](), [8th Light Insights]() Topics: [AI-assisted coding](), [Software](), [trust](), [observability](), [Authorization]() Tags: [agent](), [ai](), [ai-and-emerging-tech](), [code](), [engineering](), [engineering-and-devops](), [observability](), [permissions](), [trust](), [verification]() ### AI overview The article presents harness engineering as a repository-centered discipline for making AI-generated software more trustworthy. It argues that prompts and written standards are insufficient, and that permissions, quality gates, evidence, and observability should enforce organizational standards and support verification. ### Source excerpt If the same reasoning path writes the system change and defines the proof of success, you may be setting yourself up for an avoidable failure in the future. Harness engineering is meant to act as an extension of your own organizational guardrails, which were always meant to reduce risk and improve quality. Travis Frisinger, Head of Agentic AI Your team is shipping more AI-written code every quarter. How do you know it is any good? Good means it meets your standards, and you have probably already tried handing your agents the standards: a context file, a style guide, the wiki pasted into the prompt. The agent reads them, agrees, and still breaks them, because instructions to a model are suggestions. What the repository permits is what actually happens. Your people absorb standards through review comments and hallway corrections, and the lessons stick. An agent apologizes and forgets by the next session. The only place its lessons can accumulate is the repository itself. That gap used to be an annoyance. With AI doing real engineering work, the quality gap is the whole game. Harness engineering is the discipline that closes it. It is the process of imbuing a repository with your standards so that the repository itself enforces them: permissions and boundaries that say what any actor may touch, quality gates that fail closed, evidence attached to every change, and observability that spans runs rather than moments. Models supply software delivery capacity. The harness supplies observable accountability: every change carries what was done, which rule allowed it, and what happened as a result, no matter which model, agent, or person did the work. Why now The industry started using the term harness engineering back in February, 2026. Since then, Thoughtworks, LangChain, and others have built serious thought leadership around the same shape. When several firms independently converge on the same word, it usually means they are trying to name the same problem. The real proble ## What I worry about with AI DevFeed: [What I worry about with AI]() Original publisher: [Read original article]() Author: Emmanuel Goossaert Published: 2026-09-03T19:56:54Z Content type: opinion Language: en Sources: [Code Capsule]() Topics: [Artificial Intelligence](), [Software Engineering](), [trust]() Tags: [ai](), [ai-ai-isolation-llm-loneliness](), [covid-19](), [critical-thinking](), [dopamine-loops](), [isolation](), [llm](), [loneliness](), [mental-health](), [relationships](), [self-awareness](), [society](), [technology]() ### AI overview A personal reflection on how AI and addictive technology may weaken human empathy, relationships, and appreciation of in-person connection. The author argues that AI may eventually automate software engineering once code correctness can be ensured reliably at scale, but is more concerned about loneliness, isolation, and self-absorbed social behavior. ### Source excerpt My reflection on societal isolation amplified by technology and COVID-19, and what to do to foster genuine relationships, critical thinking, and self-awareness to combat loneliness. The post What I worry about with AI first appeared on Code Capsule. ## Which AI Personal Agents Can You Trust With Your Data? DevFeed: [Which AI Personal Agents Can You Trust With Your Data?]() Original publisher: [Read original article]() Author: Peter Yang Published: 2026-09-02T15:12:05Z Content type: tutorial Language: en Sources: [Behind the Craft]() Topics: [AI Agent](), [trust](), [Artificial Intelligence](), [ChatGPT]() Tags: [agents](), [ai](), [chatgpt](), [prompt-injection](), [trust]() ### AI overview A 24-minute tutorial compares Instinct, Grok Bot, ChatGPT, and Hermes as personal AI agents. It examines their capabilities, data access, privacy policies, and the risks of prompt injection and data leakage. ### Source excerpt I compared Instinct, Grok Bot, ChatGPT, and Hermes to see what they can access, how they handle your data, and what can go wrong. ## How to take incremental steps towards data democratisation DevFeed: [How to take incremental steps towards data democratisation]() Original publisher: [Read original article]() Author: Andy Scotland Published: 2026-08-17T13:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [data](), [trust](), [decision-making](), [Artificial Intelligence]() Tags: [agent-factory](), [ai](), [ai-agents](), [artificial-intelligence](), [compliance](), [conway-s-law](), [data](), [data-democratisation](), [data-engineering](), [data-platform](), [data-products](), [governance](), [incremental](), [trust]() ### AI overview This article explains how organisations can pursue data democratisation incrementally while preserving control, risk management and governance. It describes potential benefits in financial services and discusses how trusted data, data products, platforms and AI agents could support innovation, decision-making and customer service. ### Source excerpt Organisations increasingly recognise the value of making data more accessible, but concerns around control, risk and governance often stand in the way. In this post, I explore why data democratisation doesn't require organisations to sacrifice oversight, and how data products, platforms and agent factories can unlock innovation while maintaining trust, compliance and accountability. ## Content Governance: Keeping Documentation Trustworthy DevFeed: [Content Governance: Keeping Documentation Trustworthy]() Original publisher: [Read original article]() Author: Selvaraaju Murugesan Published: 2026-08-14T13:14:46Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [Documentation](), [Security](), [audit](), [trust](), [AI Agent](), [Requirements]() Tags: [agent](), [ai](), [audit](), [auditability](), [content](), [documentation](), [governance](), [security](), [technical-writing](), [trust]() ### AI overview This article explains content governance as a structured practice for keeping documentation accurate, secure, compliant, and trustworthy for both human readers and AI agents. It highlights regular audits, quality control, access controls, privacy and compliance checks, and synchronization with software releases. ### Source excerpt Only 21% of business and IT leaders report having a mature governance model ... The post Content Governance: Keeping Documentation Trustworthy appeared first on Document360. ## Agile Leadership DevFeed: [Agile Leadership]() Original publisher: [Read original article]() Author: Dave Ogle Published: 2026-08-10T00:00:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Agile](), [Self-organizing Team](), [trust](), [Development]() Tags: [agile](), [delegation](), [delivery](), [frameworks](), [leadership](), [self-organizing-teams](), [software-development](), [teams](), [trust]() ### AI overview This opinion argues that organisational agility depends on leadership as well as agile frameworks and processes. It highlights empowerment, trust and delegation, and examines Agile2 and the British Army's Mission Command philosophy as relevant perspectives. ### Source excerpt Agile teams are built on more than frameworks and processes. This article explores why effective leadership, trust and delegation are fundamental to true organisational agility, drawing lessons from the British Army's Mission Command philosophy. ## The bot that lied to cricket fans with a straight face: Temporal x San Francisco Unicorns DevFeed: [The bot that lied to cricket fans with a straight face: Temporal x San Francisco Unicorns]() Original publisher: [Read original article]() Author: Shubham Londhe Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Bot](), [trust](), [data]() Tags: [cricket](), [execution](), [gemini](), [temporal-voices](), [trust](), [whatsapp]() ### AI overview A Temporal developer advocate describes building a WhatsApp bot for San Francisco Unicorns cricket fans. Gemini handled conversation while Temporal managed reminders, live-score polling, and durable execution; a live-score failure highlighted the importance of trust and reliable data. ### Source excerpt How a WhatsApp bot for San Francisco Unicorns cricket fans lied about a live score at 3 AM, and what durable execution with Temporal taught us about trust. ## OpenAI's Ten Mathematical Results Tested Through Lean Certificates DevFeed: [OpenAI's Ten Mathematical Results Tested Through Lean Certificates]() Original publisher: [Read original article]() Published: 2026-08-01T00:00:00Z Content type: article Language: en Sources: [Alex Korbonits]() Topics: [Lean](), [OpenAI](), [certificates](), [trust](), [Artificial Intelligence]() Tags: [ai](), [certificates](), [openai](), [paper](), [trust]() ### AI overview The article examines OpenAI's release of ten results on long-standing mathematical problems and reports independently building and checking the accompanying Lean certificates. It says all 38 headline theorems passed with no errors or non-standard axioms, while raising concerns about trusting definitions written by the same system that produced the proofs. ### Source excerpt OpenAI shipped ten open problems with Lean certificates. I built all 550,000 lines and checked what they rest on. Everything passed -- and the only thing left to trust is 1,700 lines of definitions written by the same system that wrote the proofs. ## System Cards and Datasheets: A Technical Writers Guide DevFeed: [System Cards and Datasheets: A Technical Writers Guide]() Original publisher: [Read original article]() Author: Selvaraaju Murugesan Published: 2026-07-24T12:57:29Z Content type: article Language: en Sources: [Knowledge Management Tips, Best Practices and More]() Topics: [Large Language Model](), [technical-documentation](), [Frontier Model](), [data](), [Publishing](), [trust]() Tags: [ai](), [ai-models](), [data](), [documentation](), [frontier-model](), [governance](), [guide](), [large-language-model](), [publishing](), [technical](), [transparency](), [trust]() ### AI overview This article explains how system cards, model cards, and datasheets document AI model behavior, capabilities, limitations, evaluation methods, safety controls, intended use, and the data behind models. It presents these documents as transparency and governance tools for developers, researchers, regulators, and enterprise buyers. ### Source excerpt Large Language Model providers are publishing system cards to bring transparency into their ... The post System Cards and Datasheets: A Technical Writers Guide appeared first on Document360. ## The PM's Guide to Governance with Eric Ries, author of The Lean Startup DevFeed: [The PM's Guide to Governance with Eric Ries, author of The Lean Startup]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-07-20T22:11:52Z Content type: article Language: en Sources: [Product Growth]() Topics: [structure](), [trust](), [anthropic](), [Claude](), [OpenAI]() Tags: [anthropic](), [case-study](), [claude](), [governance](), [guide](), [openai]() ### AI overview A discussion with Eric Ries examines governance and organizational structures intended to help companies remain resilient and avoid governance failures. The article also covers Ries's use of AI tools, including Claude, in writing Incorruptible. ### Source excerpt The Lean Startup creator on why Anthropic holds and OpenAI cracked, and how to structure yours ## Verifying a Claimed Counterexample to the Jacobian Conjecture DevFeed: [Verifying a Claimed Counterexample to the Jacobian Conjecture]() Original publisher: [Read original article]() Published: 2026-07-20T00:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [trust](), [Claude](), [anthropic]() Tags: [claude](), [model](), [trust](), [verify]() ### AI overview The author examines a claimed counterexample to the long-standing Jacobian conjecture, posted in a tweet and attributed to Anthropic's Claude model. They describe checking the polynomial map's constant Jacobian determinant and non-invertibility using symbolic calculations, while noting that no paper or referee report was available. ### Source excerpt The Jacobian conjecture fell yesterday, during the World Cup final, via a tweet. Wikipedia still says it's open. Here's how I checked it myself in two cells of sympy -- and why the fact that I could is the real story. ## Owning Accountability for Agentic Engineering Systems DevFeed: [Owning Accountability for Agentic Engineering Systems]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-15T00:00:00Z Content type: opinion Language: en Sources: [Addy Osmani]() Topics: [Artificial Intelligence](), [trust](), [implementation](), [coding]() Tags: [agentic-engineering](), [agents](), [delegation](), [quality](), [trust](), [verification]() ### AI overview This commentary argues that engineers must own the accountability boundary around agentic engineering systems. It defines quality as pre-release checks, verdict as the human production decision, and answerability as the ability to explain why a decision was made. It presents agents as models combined with harnesses and repeatable investigate-implement-verify cycles, with humans retaining responsibility for decisions at the boundary. ### Source excerpt Engineers need to own the outer loop, the accountability for these systems. A written version of my AI Engineer World's Fair 2026 closing keynote: quality, verdict, and answerability, the three hidden costs of delegation, and building software factories you can stand behind. ## Sandbox Coding Agents with a Separate User Account DevFeed: [Sandbox Coding Agents with a Separate User Account]() Original publisher: [Read original article]() Author: Josh Sherman Published: 2026-07-12T00:00:00Z Content type: opinion Language: en Sources: [Josh Sherman]() Topics: [AI-assisted coding](), [Claude Code](), [Command-line interface](), [Shell](), [trust]() Tags: [claude-code](), [coding-agents](), [local](), [permissions](), [sandboxing](), [shell](), [trust]() ### AI overview The author describes running Claude Code and other coding agents under a separate user account with mostly read-only access. They say this isolates agents from privileged local credentials, avoids shell-alias confusion, and makes more permissive operation feel safer while retaining local access through the separate account. ### Source excerpt I don't trust our new robot overlords at all. I also don't fully believe that agents are out there dropping production databases without some serious coercion to do so. And while I don't trust the robots, I trust myself only a trivial amount more, so my local access tends to be extremely thin. But not so thin that a rogue agent couldn't do a little damage. That all said, I am constantly calibrating my trust with agentic endeavors. In doing so, I've been moving agents out of my user space entirely. Having Claude Code and other harnesses living under a separate user account eliminates a lot of potential disasters due to the agent finding privileged local credentials that could do some damage. Sandboxing my agents has improved trust thanks to the isolation. It's also had an additional benefit of the robots not being thoroughly confused by some of my shell aliases. It's a clean slate, and I grant access, mostly read-only, accordingly. With my agents running primarily outside of my user space, I'm able to give them significantly more leash. Running in YOLO mode (--dangerously-skip-permissions) doesn't feel nearly as scary. I do use --remote-control, but I can still log in as the separate user account to cut the latency of the web interface when I'm actually at the keyboard. While things do feel much safer, I have watched the fucking clanker try to figure out if it has passwordless sudo access on numerous occasions. Maybe the robots do actually drop people's production databases. Seriously though, it's on them for letting the thing have access to begin with. ## Prompt Injection Is the Input Bug of the Agent Era DevFeed: [Prompt Injection Is the Input Bug of the Agent Era]() Original publisher: [Read original article]() Author: Eric Roby Published: 2026-07-07T12:02:21Z Content type: opinion Language: en Sources: [Eric Roby]() Topics: [prompt injection](), [Application Security](), [Security](), [SQL](), [trust](), [Large Language Model]() Tags: [ai](), [application-security](), [llm](), [llm-applications](), [prompt-injection](), [security](), [sql](), [trust]() ### AI overview The article compares prompt injection in AI agents with SQL injection. It argues that agents may interpret untrusted content from messages, documents, web pages, emails, RAG data, and tool responses as instructions because language models do not maintain a hard boundary between data and instructions. It frames prompt injection as an application security problem rather than merely a prompt-writing problem. ### Source excerpt SQL injection taught us not to trust raw user input. Agents force the next lesson: don't trust text just because the model can read it. ## AI agents gain distinct identities and permissions, but accountability remains the harder problem DevFeed: [AI agents gain distinct identities and permissions, but accountability remains the harder problem]() Original publisher: [Read original article]() Author: Brent Leekley Published: 2026-06-12T00:00:00Z Content type: opinion Language: en Sources: [brent.leekley.me blog]() Topics: [Artificial Intelligence](), [Cisco](), [Duo](), [Authorization](), [trust]() Tags: [aeo](), [agent-identity](), [agentic-ai](), [agenticops](), [ai](), [ai-agents](), [answer-engine-optimization](), [cisco](), [cisco-cloud-control](), [cisco-live](), [cisco-live-2026](), [duo](), [duo-iam](), [identity](), [jeetu-patel](), [non-human-identity](), [permissions]() ### AI overview This commentary argues that AI agents are becoming operational actors with distinct identities, permissions, and lifecycles. It describes Cisco's approach of assigning agents identities, linking them to accountable humans, limiting permissions, and requiring human approval for proposed changes, while emphasizing that accountability and ongoing oversight remain unresolved challenges. ### Source excerpt Cisco Live 2026 made AI agents their own kind of identity, with permissions and an accountable human owner. Why issuing the identity is the easy 20 percent, what real accountability takes, and how the same agents quietly became the audience we now write for. ## TBM 425: AI and Agency DevFeed: [TBM 425: AI and Agency]() Original publisher: [Read original article]() Author: John Cutler Published: 2026-06-07T23:19:08Z Content type: opinion Language: en Sources: [The Beautiful Mess]() Topics: [AI Strategy](), [Artificial Intelligence](), [trust]() Tags: [agent](), [ai](), [ai-adoption](), [leadership](), [organizational](), [trust]() ### AI overview This commentary argues that companies' approaches to AI adoption can undermine employee agency, trust, and dignity. It presents resistance to mandates as a potentially rational response to organizational conditions and argues that employees should be able to learn how AI fits their work on their own terms. ### Source excerpt So you want your team to figure out how to use AI for the benefit of your company? ## Trust Factory DevFeed: [Trust Factory]() Original publisher: [Read original article]() Author: Kent Beck Published: 2026-06-02T13:33:57Z Content type: opinion Language: en Sources: [Software Design: Tidy First?]() Topics: [trust](), [Code](), [Testing](), [CI/CD](), [Continuous Deployment (CD)](), [observability](), [Refactoring]() Tags: [code](), [continuous-deployment](), [continuous-integration](), [observability](), [refactoring](), [testing](), [trust]() ### AI overview This commentary argues that software development practices can function as a "trust factory." It connects programmer testing, pairing, continuous integration, weekly planning, customer involvement, continuous deployment, refactoring, and observability with building trust among developers, customers, and teams. ### Source excerpt "We're accumulating code faster than we are accumulating trust." Sometimes a phrase just hits. ## AI-generated code shifts contributor value toward reliable pull-request delivery DevFeed: [AI-generated code shifts contributor value toward reliable pull-request delivery]() Original publisher: [Read original article]() Author: Yegor Bugayenko (yegor256@gmail.com) Published: 2026-05-02T21:00:00Z Content type: opinion Language: en Sources: [Yegor Bugayenko]() Topics: [Artificial Intelligence](), [Pull Request](), [Claude Code](), [trust]() Tags: [ai](), [ai-coding-assistants](), [claude-code](), [code-quality-standards](), [management](), [open-source-maintainership](), [pull-request](), [pull-request-review](), [review](), [trust]() ### AI overview This opinion article argues that AI-generated code has reduced the value of coding itself and shifted contributor value toward reliable delivery. It emphasizes approved feature requests, focused and readable pull requests, clear descriptions, speed, communication, and willingness to rework submissions. ### Source excerpt Now that AI codes for cents, contributors are paid for the delivery, not the code--so no mercy for sloppy pull requests anymore. ## Guide: How to Unify Identity Across Cloud and Data Center Infrastructure DevFeed: [Guide: How to Unify Identity Across Cloud and Data Center Infrastructure]() Original publisher: [Read original article]() Author: info@goteleport.com (Mayur Pipaliya) Published: 2026-05-01T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [IAM](), [trust](), [certificates](), [SPIFFE](), [Cloud](), [data centers](), [Amazon Web Services](), [Entra ID]() Tags: [certificates](), [cloud](), [critical-infrastructure](), [data-center](), [entra-id](), [identity](), [identity-and-access](), [infrastructure](), [spiffe]() ### AI overview This guide explains identity fragmentation across cloud accounts, data centers, and colocated infrastructure. It describes siloed identity systems, credential sprawl, and differing access models, and presents approaches including hardware roots of trust, short-lived certificates, shared certificate authorities, SPIFFE workload identities, reverse tunnels, and protocol-level enforcement. ### Source excerpt Inside this guide, discover the root causes of identity fragmentation across cloud and data center environments -- and what it takes to unify identity. ## How to Showcase Your AI Experience in Behavioral Interviews DevFeed: [How to Showcase Your AI Experience in Behavioral Interviews]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-04-30T19:45:46Z Content type: tutorial Language: en Sources: [Big Tech Careers]() Topics: [Artificial Intelligence](), [Tech Careers](), [Learning](), [trust]() Tags: [ai](), [careers](), [critical-thinking](), [how-to](), [interviews](), [learn]() ### AI overview A guide to presenting hands-on AI experience in behavioral interviews. It explains what interviewers assess, outlines common questions, and adapts the STAR framework to help candidates describe their AI projects, decisions, learning, and handling of incorrect or biased outputs. ### Source excerpt Embed your AI experience in your Interview Stories [Next page]()