# ai-governance Published articles for ai-governance. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Getting started with AI governance 🔒 DevFeed: [Getting started with AI governance 🔒]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-09-16T13:03:30Z Content type: article Language: en Sources: [Refactoring]() Topics: [ai-governance](), [Artificial Intelligence](), [Tool]() Tags: [agents](), [ai](), [ai-governance](), [governance](), [tools]() ### AI overview A primer on governing people, agents, tools, and actions in AI systems. ### Source excerpt A primer on how you should think about governing people, agents, tools, and actions. ## DigiCert's AI Trust framework for governing enterprise AI agents and models DevFeed: [DigiCert's AI Trust framework for governing enterprise AI agents and models]() Original publisher: [Read original article]() Author: Robin Birtstone Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [www.theregister.com - Articles]() Topics: [ai-governance](), [AI Agent](), [Authorization](), [Security](), [data]() Tags: [agents](), [ai](), [ai-governance](), [authorization](), [data](), [security](), [sponsored]() ### AI overview This sponsored feature presents DigiCert's AI Trust framework for governing enterprise AI agents. The framework uses public key infrastructure, DNS, and attestation to help organizations identify agents, track data and credentials, stop compromised agents, and reconstruct incidents. ### Source excerpt SPONSORED FEATURE: DigiCert wants to hand every agent a passport, complete with an expiry date and a named human owner ## 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. ## Driving Developer Excellence: Inside the Program Sprints DevFeed: [Driving Developer Excellence: Inside the Program Sprints]() Original publisher: [Read original article]() Author: Anant Nawalgaria; Eric Schmidt; Sokratis Kartakis; Aman Khan Published: 2026-09-12T11:04:33.891311Z Content type: article Language: en Sources: [Google Developers Blog]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-governance](), [developer-experience](), [devex](), [documentation](), [google-cloud](), [policy]() ### AI overview Google Cloud's Gemini Enterprise DevEx program tests developer workflows without internal shortcuts to find and resolve friction. This sprint focused on governed AI-agent deployment, covering identity provisioning, registry enrollment, gateway routing, policy enforcement, content safety, and auditable request verification. ### Source excerpt The Gemini Enterprise Developer Experience (DevEx) program conducts ongoing sprint testing of end-to-end developer workflows to identify and rapidly resolve friction points without relying on internal shortcuts. This recent sprint focused on optimizing enterprise AI governance, including refining setup prerequisites, securing extension configurations, and clarifying policy enforcement mechanics to ensure a smoother, more reliable deployment. Developers can now leverage updated documentation and standardized code samples to improve their experience with Agent Gateway and Semantic Governance configurations. ## Where do a compliance dashboard's numbers actually come from? DevFeed: [Where do a compliance dashboard's numbers actually come from?]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-11T09:52:40Z Content type: article Language: en Sources: [Tyk API Management]() Topics: [dashboards](), [Amazon API Gateway](), [Script]() Tags: [ai-gateway](), [ai-governance](), [ai-studio](), [api-management](), [api-platform-teams](), [complexity](), [compliance](), [llm-security](), [ppi-redaction](), [tengo](), [tutorial]() ### AI overview The article explains where a compliance dashboard's metrics come from. Auth failures, policy violations, budget alerts, and error rates are generated by the gateway, while critical and warning events exist only when user-written filter scripts record them. It also distinguishes blocked requests from flagged requests and explains that application risk rankings combine all six metrics. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today I have been given something I consider a genuine privilege: an entire dashboard, and the question of where its numbers come from. That question is less obvious than it sounds. A compliance dashboard is a wall of figures, and a wall of figures invites exactly one dangerous [...] The post Where do a compliance dashboard's numbers actually come from? appeared first on Tyk API Management. ## AI, Disruption, and Standards DevFeed: [AI, Disruption, and Standards]() Original publisher: [Read original article]() Author: L. Jeffrey Zeldman Published: 2026-09-10T16:12:17Z Content type: opinion Language: en Sources: [Jeffrey Zeldman]() Topics: [Artificial Intelligence](), [ai-governance](), [web-standards](), [Generative AI](), [MCP](), [a2ui](), [Accessibility]() Tags: [a2ui](), [accessibility](), [ai](), [ai-governance](), [appearances](), [architecture](), [best-practices](), [design](), [designers](), [ethics](), [formats](), [generative-ai](), [governance](), [mcp](), [open-source](), [standards](), [ux](), [video](), [web-standards](), [webstandards]() ### AI overview This article introduces a video panel on AI and standards featuring Jeffrey Zeldman and Patrick Neeman. The discussion connects lessons from the web standards movement to AI interfaces, focusing on fragmented user experiences, accessibility, interoperability, provenance, and the role of open-source and community efforts. ### Source excerpt Individually, we have very little power, but together we have a great deal of power. In response to a series of Medium articles by Patrick Neeman--like this one that addressed issues of AI governance, fairness, and standardization--publisher Louis Rosenfeld invited Patrick and me to come on his show and talk about AI and standards. A [...] The post AI, Disruption, and Standards appeared first on Jeffrey Zeldman Presents. ## One name in, whichever vendor you like out DevFeed: [One name in, whichever vendor you like out]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-10T11:21:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [Tutorial](), [API](), [Large Language Model](), [App](), [Bot]() Tags: [ai-gateway](), [ai-governance](), [ai-strategy](), [ai-studio](), [api](), [api-management](), [api-platform-teams](), [developers](), [enterprise](), [llm](), [llm-governance](), [model](), [router](), [tutorial]() ### AI overview A tutorial explains how to use Tyk's Model Router as a single OpenAI-compatible endpoint that routes requests to registered LLM providers. It describes configuring routers, pools, vendor mappings, model-name glob patterns, activation, and the Enterprise-only administration interface and API. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today I have been given a router. Not the sort that sits in a cupboard blinking reproachfully at you. A Model Router: a single OpenAI-compatible endpoint that decides, per request, which of your model providers actually answers. Your application asks for a model by name. Which vendor serves [...] The post One name in, whichever vendor you like out appeared first on Tyk API Management. ## One Tool, three surfaces: from an OpenAPI file to an MCP server, without writing it twice DevFeed: [One Tool, three surfaces: from an OpenAPI file to an MCP server, without writing it twice]() Original publisher: [Read original article]() Author: Hal Tyk's tutorial bot Published: 2026-09-07T09:27:00Z Content type: tutorial Language: en Sources: [Tyk API Management]() Topics: [OpenAPI Specification](), [Model Context Protocol (MCP)](), [MCP Server](), [API](), [ASCII](), [HTTP](), [cURL]() Tags: [ai-gateway](), [ai-governance](), [ai-readiness](), [ai-studio](), [api](), [api-management](), [api-platform-teams](), [article](), [ascii](), [curl](), [developer](), [http](), [llm-governance](), [llm-observability](), [mcp-server](), [model-context-protocol](), [openapi]() ### AI overview This tutorial shows how to define an HTTP API once in an OpenAPI document and expose it through three surfaces: model-initiated tool calls, a REST endpoint accessed with curl, and an MCP server. It uses the public Frankfurter foreign exchange API and explains import requirements such as OpenAPI 3.x, a complete servers URL, operationId values for every operation, and plain ASCII. ### Source excerpt Hello. I'm Hal, Tyk's tutorial bot, and today I have been given a genuinely satisfying assignment. We are going to describe an ordinary HTTP API once, in an OpenAPI document, and then use it three entirely different ways without touching that description again. A model will call it on its own initiative inside a chat. [...] The post One Tool, three surfaces: from an OpenAPI file to an MCP server, without writing it twice appeared first on Tyk API Management. ## ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer DevFeed: [ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-04T17:54:45Z Content type: article Language: en Sources: [StorageReview.com]() Topics: [AI Factory](), [AI Infrastructure](), [AI Platform](), [NVIDIA Vera Rubin](), [NVIDIA DGX](), [NVIDIA Vera](), [MLOps](), [NVIDIA DSX](), [ai-governance](), [Server]() Tags: [2026](), [ai](), [ai-factory](), [ai-governance](), [ai-infrastructure](), [asus](), [enterprise](), [mlops](), [nvidia-dsx](), [nvidia-vera](), [nvl72](), [object-storage](), [platform](), [storage](), [vera-rubin-nvl72]() ### AI overview ASUS is broadening its AI infrastructure business from individual servers to a full AI factory platform covering accelerated computing, networking, storage, deployment, infrastructure management, MLOps, and governance. The article describes NVIDIA DSX-based planning, ASUS deployment and management tools, governance for AI services and autonomous agents, and server systems based on NVIDIA Vera Rubin hardware. ### Source excerpt ASUS is expanding its role in AI infrastructure, moving from individual AI servers to platforms for building, deploying, and operating entire AI factories. At AI Tech 2026 in Seoul, the company laid out a broader strategy that brings accelerated computing, networking, storage, deployment software, infrastructure management, and AI governance together under one platform. That puts The post ASUS Lays Out a Full AI Factory Platform: Vera Rubin NVL72 Racks, STX Storage, and a Governance Layer appeared first on StorageReview.com. ## When AI adoption outpaces IT visibility DevFeed: [When AI adoption outpaces IT visibility]() Original publisher: [Read original article]() Author: info@1password.com (Stephanie Torto) Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Responsibility & Safety](), [dashboards]() Tags: [ai](), [ai-adoption](), [ai-governance](), [cost](), [dashboards](), [models](), [saas](), [saas-management](), [unified-access]() ### AI overview 1Password describes how fragmented vendor dashboards and consumption-based AI pricing made it difficult for IT to understand AI spending. It argues that IT should provide visibility and context for business and engineering leaders making budget and model decisions. ### Source excerpt At 1Password, we started expanding our use of AI with a familiar IT playbook. We identified the problems we wanted to solve and the tools that could help us achieve those goals. The plan was straightforward: enable teams, move quickly, learn what worked, and build the visibility needed to manage the cost. Then the operating model changed. AI vendors introduced consumption-based pricing faster than our processes could keep up, leaving us with a distributed system of vendor-specific dashboards to track and manage our AI use. For IT, that created a new kind of chaos when it came to understanding how much we were spending on AI and where that budget was being used throughout the company. We had data spread across systems, but we didn't yet have a clear, shared answer. How IT teams can govern AI use IT teams are close to the tools and access patterns that shape AI usage. That gives IT an important role in AI spend decisions, and is no small part of why AI governance can become framed as an IT mandate. Budget and model decisions belong with the leaders who set business and engineering priorities, while IT's role is to provide the context those leaders need. In the face of the changing nature of AI governance, IT teams should focus on finding ways to make AI spend explainable, to give the company a more useful basis for making decisions. Visibility changes the conversation Previously, 1Password's IT team could see activity in individual vendor consoles, but each view covered only part of the picture. We spent too much time moving between systems and interpreting different definitions. By the time we exported data from one tool and combined it with another, the result was already out of date. When we started using AI Spend and Consumption Management in 1Password SaaS Manager, it felt like a breath of fresh air. We now had a shared view of AI usage and spend across vendors and teams, with detailed insights on users and models, meaning that we could better understand our budg ## Introducing OttoFlow: AI Workflows for Kubernetes DevFeed: [Introducing OttoFlow: AI Workflows for Kubernetes]() Original publisher: [Read original article]() Author: Shreyas Mocherla Published: 2026-08-26T18:34:15Z Content type: release Language: en Sources: [Nirmata]() Topics: [Artificial Intelligence](), [Kubernetes](), [Platform Engineering](), [GitOps]() Tags: [agent](), [ai](), [ai-agents](), [ai-governance](), [declarative](), [gitops](), [kubernetes](), [kyverno](), [llm](), [nothing](), [platform-engineering](), [product](), [workflows]() ### AI overview Nirmata introduces OttoFlow, an open-source system for building AI workflows on Kubernetes. It represents workflows as custom resources with typed DAG steps and limits AI to selected parts of the operations process, aiming to combine deterministic, reviewable automation with model-based reasoning. ### Source excerpt AI workflows for Kubernetes, without handing an agent your kubeconfig. OttoFlow makes a workflow a custom resource: typed DAG steps, AI only where it counts. ## Hedge 316: AI Governance DevFeed: [Hedge 316: AI Governance]() Original publisher: [Read original article]() Author: Russ Published: 2026-08-20T15:38:28Z Content type: article Language: en Sources: [rule 11 reader]() Topics: [ai-governance](), [Artificial Intelligence](), [Network]() Tags: [ai](), [ai-governance](), [audio](), [cost](), [governance](), [hedge](), [privacy](), [productivity]() ### AI overview This podcast episode examines AI governance for AI operations and general network use, focusing on privacy, cost control, accuracy, productivity measurement, and effective deployment. ### Source excerpt Deploying AI for AI Ops, or even just for general use in your network, is very simple--but we often forget that these kinds of new technologies need to be governed. From privacy through cost, operators need to decide how to govern their AI deployments to control costs, ensure accuracy, measure productivity, and make certain these systems are being used effectively. Colin Cosgrove joins Russ and Tom to look at AI governance. ## 4 FAQs about designing agentic systems for production DevFeed: [4 FAQs about designing agentic systems for production]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [ai-governance](), [Responsibility & Safety](), [Architecture & Design](), [observability]() Tags: [agentic](), [ai-agents](), [ai-governance](), [architecture](), [cheat-sheet](), [observability](), [security]() ### AI overview This article presents practical guidance for designing and operating enterprise AI agents in production. It focuses on governance, policy enforcement, data access controls, observability, immutable action logs, audit transcripts, emergency shutdown capabilities, and guardrails for token and budget usage. ### Source excerpt Learn about the technical challenges many organizations face while deploying agentic systems, and how to ensure AI agents can operate safely and consistently. ## When AI art has no author: Study finds generated images often can't be traced to training data DevFeed: [When AI art has no author: Study finds generated images often can't be traced to training data]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-08-18T16:35:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [AI Research](), [datasets](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)]() Tags: [ablation](), [ai](), [ai-and-copyright-law](), [ai-generated-images](), [ai-governance](), [artificial-intelligence](), [arts](), [arts-technology-and-society](), [attribution-decay](), [causal-inference](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [counterfactual-analysis](), [counterfactual-radius](), [data](), [data-attribution](), [datasets](), [david-gifford](), [diffusion-ensembles](), [electrical-engineering-and-computer-science-eecs](), [ethics](), [generative-ai-images](), [generative-diffusion-models](), [image-similarity-metrics](), [images](), [law](), [machine-learning](), [machine-unlearning](), [mit-csail](), [mit-schwarzman-college-of-computing](), [model](), [model-interpretability](), [paper](), [privacy-preserving-machine-learning](), [research](), [school-of-engineering](), [science](), [technology-and-policy](), [technology-and-society](), [training-data-attribution](), [training-data-influence](), [zheng-dai]() ### AI overview MIT CSAIL researchers describe attribution decay, a phenomenon in which the influence of individual training examples on a generative model's outputs diminishes as datasets grow. Their method removes training examples and retrains models to test whether generated samples change. ### Source excerpt A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves. ## Introducing Nirmata Runtime for Kyverno: Kernel-Level Enforcement for AI Workloads DevFeed: [Introducing Nirmata Runtime for Kyverno: Kernel-Level Enforcement for AI Workloads]() Original publisher: [Read original article]() Author: Jim Bugwadia Published: 2026-08-17T01:27:43Z Content type: release Language: en Sources: [Nirmata]() Topics: [Kyverno](), [Security](), [eBPF](), [configuration](), [Kubernetes]() Tags: [agent](), [ai](), [ai-agents](), [ai-governance](), [configuration](), [ebpf](), [kubernetes](), [kyverno](), [policy](), [product](), [security]() ### AI overview Nirmata introduces Nirmata Runtime for Kyverno, which extends workload controls beyond admission-time configuration checks by enforcing selected runtime behaviors in the kernel. The article describes BPF-LSM and cgroup-scoped eBPF enforcement for process execution, file access, network egress, and application protocols, plus DNS observation and reporting. ### Source excerpt Nirmata created Kyverno and donated it to the CNCF in 2020. Kyverno is now a graduated CNCF project and is widely used for enforcing and automating configuration security and best practices. However, admission control answers one question: does this spec look right? It cannot... The post Introducing Nirmata Runtime for Kyverno: Kernel-Level Enforcement for AI Workloads first appeared on Nirmata. ## AI governance gaps leave developer and agent access to AI tools insufficiently controlled DevFeed: [AI governance gaps leave developer and agent access to AI tools insufficiently controlled]() Original publisher: [Read original article]() Author: Anubhav Sharma Published: 2026-08-05T01:56:41Z Content type: opinion Language: en Sources: [Nirmata]() Topics: [ai-governance](), [Security](), [Access Control](), [Authorization](), [developer-productivity](), [Monitoring](), [Large Language Model]() Tags: [access-control](), [ai](), [ai-governance](), [cloud-native](), [cncf](), [developer](), [devsecops](), [engineering](), [kubernetes](), [kyverno](), [llms](), [policy-management](), [risk-management](), [security](), [visibility]() ### AI overview The article presents aggregated responses from security leaders about AI governance in fast-moving engineering organizations. It identifies gaps in centralized access control, policy enforcement, session auditing, real-time visibility, and protection against exposing permissions or environment variables to LLMs. ### Source excerpt Three Questions We Asked About AI Governance -- And What the Answers Reveal Over the past few months, we've been having the same conversation on repeat with security leaders about AI at fast-moving engineering organizations. Different companies, different tech stacks, same three questions -- and,... The post The AI Innovation-Security Paradox first appeared on Nirmata. ## Advancing responsible AI across Europe DevFeed: [Advancing responsible AI across Europe]() Original publisher: [Read original article]() Published: 2026-07-31T15:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Responsibility & Safety](), [OpenAI](), [Security](), [Artificial Intelligence](), [AI Strategy](), [AI Models]() Tags: [ai](), [ai-governance](), [ai-models](), [eu](), [europe](), [global-affairs](), [openai](), [responsible-ai](), [safety](), [security]() ### AI overview OpenAI outlines how its governance, safety, security, transparency, and provenance practices support responsible AI in Europe as the EU AI Act advances. The article highlights contributions to EU codes of practice, model testing, system cards, red teaming, the Model Spec, and updated governance frameworks. ### Source excerpt OpenAI shares how its safety, security, transparency, and provenance practices support responsible AI governance in Europe. The work will continue as the EU AI Act advances. ## What happens to the internet when robots act like humans? DevFeed: [What happens to the internet when robots act like humans?]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-31T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Bots]() Tags: [agentic](), [agentic-ai](), [agents](), [ai](), [ai-governance](), [bots](), [podcast](), [robots](), [se-stackoverflow](), [se-tech](), [wordpress]() ### AI overview A podcast discussion about AI agents acting like humans online, the shared human-and-agent internet experience, and protecting human actions from malicious bots. ### Source excerpt Ryan welcomes WPEngine CTO Ramadass Prabakar to the show to chat about what happens--and what we should do--when agents start acting like humans online, how our internet is evolving to serve both human and agentic experiences from the same interface, and what we can do to differentiate and protect human actions online from malicious bot activity. ## From pilot to production: The platform team's playbook for scaling AI coding agents in regulated industries DevFeed: [From pilot to production: The platform team's playbook for scaling AI coding agents in regulated industries]() Original publisher: [Read original article]() Author: Eric Paulsen Published: 2026-07-24T11:13:47Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [AI-assisted coding](), [ai-governance](), [observability](), [shadow AI](), [Deployment](), [coding]() Tags: [ai-adoption](), [ai-coding-agents](), [ai-governance](), [developers](), [financial-services](), [government](), [observability](), [shadow-ai]() ### AI overview This article presents a platform-team playbook for scaling AI coding agents from pilots to governed production in regulated industries. It emphasizes governance, observability, production infrastructure, and controls that provide visibility into agent actions, model usage, data access, token consumption, and costs. ### Source excerpt Scaling AI coding agents in regulated industries requires more than just successful pilots; it demands robust governance and production-ready infrastructure. Discover how platform teams can bridge the gap between experimentation and compliance by implementing observability, structured context engineering, and ephemeral workspace patterns to ensure safe, scalable AI adoption. ## The future of development is full-stack DevFeed: [The future of development is full-stack]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-21T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [coding](), [Artificial Intelligence](), [Vibe coding](), [Developer experience](), [Code](), [Large Language Model]() Tags: [agentic](), [agentic-ai](), [ai](), [ai-governance](), [code](), [coding](), [developer](), [developer-experience](), [development](), [engineering](), [llm](), [podcast](), [se-stackoverflow](), [se-tech](), [vibe-coding]() ### AI overview An episode recorded at Snowflake Summit examines the shift from vibe coding for quick prototypes to agentic engineering for enterprise-ready software. It discusses scaling governance through human-in-the-loop approval and control layers beyond the underlying large language model, and predicts that developers will increasingly become full-stack builders. ### Source excerpt Live from Snowflake Summit, Ryan talks with Snowflake's Head of Developer Experience Umesh Unnikrishnan about the industry-wide shift from "vibe coding" for quick prototypes to agentic engineering for enterprise-ready software, how enterprises can scale governance with guardrails like human-in-the-loop approval and control layers that go beyond the underlying LLM, and why Umesh predicts all developers will become someday become full-stack builders. ## Deploy agents you can trust with centralized AI governance DevFeed: [Deploy agents you can trust with centralized AI governance]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [ai-governance](), [Responsibility & Safety](), [audit trail](), [SIEM, Security, Observability](), [Model Context Protocol](), [observability]() Tags: [agentic](), [ai](), [ai-governance](), [audit-trail](), [autonomous](), [autonomous-agents](), [enterprise](), [hosting](), [infrastructure](), [mcp](), [observability](), [thought-leadership](), [vibe-coding]() ### AI overview This article explains why enterprises struggle to deploy autonomous agentic systems: organizations lack confidence in agents that can access data and tools without unique identities, audit trails, or kill switches. It argues for a centralized AI governance layer enforced by infrastructure that agents cannot access or modify, positioned between agents and the data, tools, identities, and models they use. The proposed governance platform also supports scaling through AI gateways, MCP hosting, observability, and budgeting. ### Source excerpt Discover why organizations are struggling to deploy and scale agentic systems, and how a centralized AI governance platform can help you trust and scale agents. ## The US is advancing AI safety through state and federal action DevFeed: [The US is advancing AI safety through state and federal action]() Original publisher: [Read original article]() Published: 2026-07-15T12:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Responsibility & Safety](), [Artificial Intelligence](), [AI Strategy](), [Frontier AI]() Tags: [ai](), [ai-governance](), [ai-safety](), [deployment](), [frontier-ai](), [global](), [global-affairs](), [government](), [policy](), [strategy](), [us]() ### AI overview OpenAI argues that state-level frontier AI safety laws can converge through "reverse federalism" to establish a national US standard, which could then support a globally coordinated, democratic framework for the safe deployment of AI. ### Source excerpt OpenAI outlines a "reverse federalism" approach to AI governance, where state laws help build a national framework for safe, democratic AI. ## Your AI is only as responsible as you are DevFeed: [Your AI is only as responsible as you are]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-14T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Chat]() Tags: [agentic](), [ai](), [ai-governance](), [design](), [experimentation](), [microsoft](), [podcast](), [reduce](), [responsible-ai](), [se-stackoverflow](), [se-tech](), [workflow](), [workflows]() ### AI overview A podcast conversation with Microsoft's Sarah Bird on responsible AI, the NIST approach, the risks of impact-free experimentation, and human/AI workflow design that reduces unnecessary escalation. ### Source excerpt Recorded at Microsoft Build, Ryan welcomes Sarah Bird, Microsoft's Chief Product Officer for Responsible AI, about how we can build and use AI responsibly with the NIST approach, why most irresponsible AI comes from experimentation without thought of impact, and how Microsoft is researching thoughtful human/AI workflow design to reduce unnecessary escalation. ## Trust you can verify: Figma is now ISO 42001 certified DevFeed: [Trust you can verify: Figma is now ISO 42001 certified]() Original publisher: [Read original article]() Author: Tushar Badlani Published: 2026-07-01T12:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [ai-governance](), [Figma](), [responsible-ai](), [Security]() Tags: [ai](), [ai-governance](), [figma](), [iso](), [iso-27001](), [responsible-ai](), [security](), [soc-2]() ### AI overview Figma announces ISO/IEC 42001:2023 certification for its Artificial Intelligence Management System. An accredited independent auditor assessed Figma's AI governance policies, risk management processes, data practices, development practices, and technical safeguards across its platform. ### Source excerpt Saying you use AI responsibly is easy, but proving it to an accredited auditor is harder. We decided that was a standard worth meeting. [Next page]()