---
title: "AI Governance"
description: "116 posts about AI Governance, summarised, each linking to the original."
---

# AI Governance
> 116 posts about AI Governance, summarised, each linking to the original.

## Articles

### [Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine](https://yomu.fyi/post/inside-one-of-the-first-production-deployments-of-lakebase-langguard-s.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Venkat Raghavan, Jason Keirstead, Ravi Srinivasan, Nina Williams, Amelia Westberg
- Published: Apr 27, 2026
- Type: Explainer

LangGuard is presented as a runtime enforcement layer for enterprise agentic workflows, monitoring actions, decisions, tools, credentials, and intent across connected systems. Its GRAIL data fabric records multidimensional trace data, builds a live knowledge graph, and evaluates allow/deny/modify decisions against policy before tools, datasets, or models are accessed. The deployment uses Databricks Lakebase as the operational system of record, relying on PostgreSQL, serverless autoscaling, scale-to-zero, compute-storage disaggregation, hot-data caching, and copy-on-write branching for policy testing. LangGuard chose this architecture to handle bursty trace writes and low-latency enforcement reads without provisioning for peak demand, while keeping operational data available to Databricks analytics and AI capabilities without additional ETL. The stated next step is predictive governance: training behavioral models on historical traces to flag anomalous agent behavior before a policy violation.


### [Model risk management in 2026: A banker's guide to the revised interagency guidance](https://yomu.fyi/post/model-risk-management-in-2026-a-banker-s-guide-to-the-revised-interage.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Jennifer Miller, Chaitanya Varanasi, Kim Hatton
- Published: Apr 25, 2026
- Type: Problem & solution

The post presents the April 17, 2026 revision to model risk management guidance as a shift toward risk-based, principles-driven oversight, replacing several earlier issuances from the Federal Reserve, FDIC, and OCC. It identifies five operational changes: materiality tiering, end-to-end lifecycle governance, reproducible effective challenge, continuous drift and performance monitoring, and application of MRM principles to GenAI and agentic systems. Its proposed response is a Databricks reference architecture built on Unity Catalog, Delta Lake, Lakeflow Declarative Pipelines, Feature Store, MLflow, Model Registry, Model Serving, and assurance tools. The design makes tiering metadata-driven and turns lineage, validation, approvals, monitoring, documentation, and retirement records into evidence generated during normal model work. The stated conclusion is that a unified substrate can reduce integration and manual evidence work, allowing regulatory changes to become configuration exercises rather than multi-quarter programs.


### [A Modern AI Risk Management Framework](https://yomu.fyi/post/a-modern-ai-risk-management-framework.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 22, 2026
- Type: Explainer

AI systems introduce probabilistic behavior, model drift, adversarial manipulation, bias, and auditability challenges that traditional IT risk practices may not address. The framework described combines NIST AI RMF, EU AI Act, and ISO/IEC 23894:2023, using Govern, Map, Measure, and Manage as its operational structure while recognizing regulatory and societal context. It recommends cross-functional governance, clear risk ownership, an AI-BOM covering systems, data flows, dependencies, and accountability, plus continuous measurement of fairness, explainability, data quality, security vulnerabilities, and harm likelihood and severity. Across the AI lifecycle, the text identifies risks including data poisoning, model drift, malicious library injection, prompt injection, hallucinations, and platform vulnerabilities, and points to the AI RMF Playbook for checklists, timelines, and governance artifacts.


### [What is Generative AI in Marketing?](https://yomu.fyi/post/what-is-generative-ai-in-marketing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 16, 2026
- Type: Explainer

Generative AI in marketing creates content, insights and recommendations that can personalize experiences, optimize campaigns and improve performance. Unlike traditional analytics, which mainly reports on past results, it produces net-new outputs such as ad copy, audience segments, product recommendations, visual assets and strategic summaries. A typical workflow prepares campaign, customer and brand data, grounds or fine-tunes models, generates outputs, applies targeting and optimization, and uses human review and refinement. It distinguishes pretrained tools such as ChatGPT, Claude and Perplexity from customized models and broader AI transformation, noting trade-offs between speed, investment, relevance and strategic alignment. Successful adoption depends on high-quality, consented data, governance, privacy controls, human oversight, monitoring and cross-functional ownership; the conclusion presents AI as a force multiplier rather than a replacement for human creativity and judgment.


### [Expanding agent governance with Unity AI Gateway](https://yomu.fyi/post/expanding-agent-governance-with-unity-ai-gateway.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Nasi
- Published: Apr 15, 2026
- Type: Announcement

Unity AI Gateway extends Unity Catalog’s permissions, auditing, and policy controls to agentic workflows involving LLMs, MCP servers, and APIs. The release addresses limited visibility across multi-step actions with on-behalf-of user execution for MCP calls, configurable guardrails, and consistent governance across model providers without separate configurations for each provider and model choice can vary by task. Guardrails can detect PII, prompt injection, unsafe content, and data exfiltration, while logs capture identities, timestamps, MCP details, dollar costs, payloads, latency, status codes, and errors. OpenAI-compatible APIs, fallback models, and endpoint-, user-, or group-level rate limits support production operation, although some capabilities are in Beta or rolling out and availability is limited to supported Databricks regions.


### [Agent Bricks: The governed enterprise agent platform](https://yomu.fyi/post/agent-bricks-the-governed-enterprise-agent-platform.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kasey Uhlenhuth
- Published: Apr 14, 2026
- Type: Announcement

Agent Bricks is presented as Databricks’ enterprise platform for building, deploying, and governing agents that operate on business data under real identities, permissions, and operational constraints. The platform combines multi-model and framework support, execution, routing, fallback, cost optimization, and unified governance through Unity Catalog and AI Gateway, including on-behalf-of token passing and observability across data, models, MCPs, and APIs. Its context layer uses metadata such as schemas, business definitions, lineage, permissions, and data-quality signals, while Genie Spaces, Document Intelligence, Knowledge Assistant, and Agent Mode address structured and unstructured business information. The announcement includes general availability for Document Intelligence, Custom Agents on Apps, and Supervisor Agent, plus AI Gateway guardrails, managed OAuth MCP Connectors, web search, and MLflow’s CLEARS evaluation framework; the post reports 70% higher accuracy than standard RAG and a 30% improvement in multi-step workflows.


### [Australia and Anthropic sign AI safety MOU](https://yomu.fyi/post/australia-and-anthropic-sign-ai-safety-mou.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Mar 31, 2026
- Type: Announcement

Anthropic and the Australian government signed a Memorandum of Understanding to cooperate on AI safety research and support Australia’s National AI Plan. The agreement centers on collaboration with Australia’s AI Safety Institute, including sharing findings about emerging model capabilities and risks, participating in joint safety and security evaluations, and conducting research with Australian academic institutions. Anthropic will also share Anthropic Economic Index data to track AI adoption, economic effects, and workforce implications, initially covering natural resources, agriculture, healthcare, and financial services. Separately, AUD$3 million in Claude API credits will support four Australian institutions working on clinical genomics, precision medicine, pediatric research, and computing education. The company is also exploring data-center and energy investments, launching a startup credit program, and preparing to open a Sydney office.


### [Dario Amodei on the Department of War discussions](https://yomu.fyi/post/dario-amodei-on-the-department-of-war-discussions.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Feb 26, 2026
- Type: Announcement

Dario Amodei says Anthropic considers AI deployment for US and allied democratic defense existentially important and has deployed Claude across the Department of War and other national security agencies, including classified networks and National Laboratories. The company says it supports intelligence analysis, modeling and simulation, operational planning, and cyber operations, while restricting Claude’s use by firms linked to the Chinese Communist Party and advocating chip export controls. Anthropic maintains two safeguards: mass domestic surveillance is incompatible with democratic values, and current frontier systems are not reliable enough for fully autonomous weapons without proper oversight. The Department has stated it demands “any lawful use” and threatens offboarding, a “supply chain risk” designation, and Defense Production Act action; Anthropic says it will not remove the safeguards, while offering continued service or transition support.


### [Intercom achieves AIUC-1 certification: A new standard for trust in AI Agents](https://yomu.fyi/post/intercom-achieves-aiuc-1-certification-a-new-standard-for-trust-in-ai.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Thibault Candebat
- Published: Dec 8, 2025
- Type: Announcement

Intercom announces that it has achieved AIUC-1 certification, describing it as the first standard designed specifically for AI Agents. The certification complements broader frameworks such as ISO 42001 and addresses enterprise concerns spanning security, customer safety, system reliability, data and privacy, society, and accountability. Organizations seeking AIUC-1 undergo independent third-party audits and quarterly adversarial testing across more than a thousand enterprise risk scenarios, while the standard itself evolves quarterly through the AIUC-1 Consortium. Intercom says the certification extends its governance approach to Fin, its AI Agent, providing independent validation that its AI systems are safe, resilient, regularly tested, safeguarded, and enterprise-ready; it also identifies Intercom as a founding technical contributor to the standard.


### [Updating sales restrictions for unsupported regions](https://yomu.fyi/post/updating-sales-restrictions-for-unsupported-regions.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Sep 4, 2025
- Type: Announcement

Anthropic is updating its Terms of Service to address access by companies from regions where its products are not permitted, including entities operating through foreign subsidiaries. The company says ownership or control from jurisdictions such as China can create legal obligations to share data, cooperate with intelligence services, or support military and intelligence objectives. Under the change, companies or organizations more than 50% owned directly or indirectly by companies headquartered in unsupported regions are prohibited from using the services, regardless of where they operate. Anthropic frames the update as aligning its restrictions with real-world national-security risks and reiterates support for export controls, US energy projects, and rigorous evaluation of AI models for capabilities relevant to national security.


### [Developing nuclear safeguards for AI through public-private partnership](https://yomu.fyi/post/developing-nuclear-safeguards-for-ai-through-public-private-partnershi.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 21, 2025
- Type: Problem & solution

AI models are increasingly capable, raising concern that they could provide dangerous nuclear-technical knowledge that threatens national security. Nuclear information is especially difficult for a private company to evaluate alone, motivating a partnership with the U.S. Department of Energy’s National Nuclear Security Administration (NNSA). Together with NNSA and DOE national laboratories, the company co-developed a classifier that distinguishes concerning from benign nuclear-related conversations, achieving 96% accuracy in preliminary testing. The classifier has been deployed on Claude traffic within a broader system for identifying model misuse, and early deployment data suggests it works well on real conversations; the approach will be shared with the Frontier Model Forum as a potential blueprint for similar safeguards developed with NNSA.


### [Announcing The AI Agent Blueprint](https://yomu.fyi/post/announcing-the-ai-agent-blueprint.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Declan Ivory
- Published: Aug 12, 2025
- Type: Announcement

The AI Agent Blueprint is a strategic guide for support, customer experience, and AI transformation leaders navigating how to adopt and scale AI Agents. Its first part, “Launch it,” covers building a business case, deciding whether to buy or build, evaluating an agent with a four-step framework, planning deployment, and continuously training, testing, deploying, and optimizing performance. The second part, “Scale it,” focuses on redesigning customer experience, organizational and system design, and economics through AI-first journeys, new roles and governance structures, and revised metrics, ROI models, and reinvestment strategies. The Blueprint argues that launching an agent is only the beginning: sustainable value requires treating AI as infrastructure and support as a potential growth lever, and it is live now.


### [Grab AI Gateway: Connecting Grabbers to multiple GenAI providers](https://yomu.fyi/post/grab-ai-gateway-connecting-grabbers-to-multiple-genai-providers.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Bjorn Jee
- Published: Feb 19, 2025
- Type: Explainer

Grab AI Gateway is a central access layer connecting Grabbers’ applications to multiple GenAI providers, including OpenAI, Azure, AWS Bedrock, and Google VertexAI. Built as a set of reverse proxies, it centralizes provider authentication, authorization, rate limiting, auditing, and capacity management while presenting a unified OpenAI-style API; users can switch models by changing the model parameter. Exploration keys support short-lived staging experiments, while reviewed use cases can use longer-term service keys, and the gateway exposes embedding, image, audio, fine-tuning, search, and in-house open source models. Shared capacity, dynamic routing, monitoring, cost attribution, and usage records support governance and efficiency, although batch traffic can interfere with latency-sensitive services and provider-specific SDK edge cases require integration testing. More than 3,000 Grabbers have requested exploration keys, and the gateway powers applications including ride-safety audio analysis, content moderation, SQL generation, incident automation, and Slack support.


### [U.S. elections readiness](https://yomu.fyi/post/u-s-elections-readiness.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Nov 5, 2024
- Type: Announcement

Anthropic outlines its preparations for the 2024 U.S. election cycle, the first in which generative AI tools are widely available. Since July 2023, it has updated its Usage Policy, strengthened misuse detection, and redirected users toward authoritative voting information. The policy prohibits campaigning, lobbying, election misinformation, targeting voting machines, and interference with vote counting or certification, while Claude's text-only output prevents it from generating image, audio, or video deepfakes. Controls include prompt modifications, API audits, possible account suspensions, cloud-provider coordination, red-teaming, Policy Vulnerability Testing, and automated evaluations of political parity, harmful-query refusals, misinformation prevention, and voter-profiling risks; a TurboVote pop-up handles voting-information requests because models lack real-time election data, while the system prompt states Claude's knowledge cutoff.


### [eBay's Responsible AI Principles](https://yomu.fyi/post/ebay-s-responsible-ai-principles.md)
- Company: [Ebay](https://yomu.fyi/company/ebay.md)
- Author: Lauren Wilcox
- Published: Mar 8, 2024
- Type: Announcement

eBay sets out principles for developing and using AI-powered customer tools and services while addressing safety and community needs. Its framework covers reliability, safety, security, inclusivity, equity, fairness, accountability, lawfulness, privacy by design, and transparency. The company says it rigorously tests AI technologies, protects confidential data used to train or operate systems, includes measures to avoid excluding groups, and applies quality and safety checks to datasets. It also calls for human oversight throughout development, launch, and monitoring, risk-based decisions, regulatory compliance, and privacy-focused design using state-of-the-art methods. For users, eBay says transparency may involve disclosures within an experience or broader communication, depending on lifecycle stage and the audience.


### [Charting a path to AI accountability](https://yomu.fyi/post/charting-a-path-to-ai-accountability.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jun 13, 2023
- Type: Explainer

Anthropic’s response to the NTIA’s Request for Comment on AI Accountability proposes processes and infrastructure for evaluating highly capable and general-purpose AI models. It recommends funding rigorous evaluations, requiring near-term disclosure of evaluation methods and results, and developing longer-term industry standards with agencies such as NIST. The framework would use capability-based risk thresholds, pre-registration of large training runs, confidential registries, technically literate and security-conscious third-party auditors, and external red teaming before advanced model release. It also calls for more interpretability research, acknowledges that regulations requiring interpretable models are currently infeasible, and seeks antitrust guidance for industry safety collaboration. Anthropic says these measures require collaboration among researchers, AI labs, regulators, auditors, and other stakeholders to build accountability and auditing mechanisms that support safe development and deployment.


[Newer posts](https://yomu.fyi/topic/ai-governance/page/5.md)