AI Governance
45 items tagged with "ai-governance"
Standards2
EU AI Act (Regulation (EU) 2024/1689)
The EU's horizontal regulation for artificial intelligence, classifying systems by risk and attaching obligations to each tier. It entered into force on 1 August 2024; prohibited practices and AI literacy applied from 2 February 2025, general-purpose AI obligations from 2 August 2025, and Article 50 transparency obligations from 2 August 2026. The Digital Omnibus on AI, approved by the Council on 29 June 2026, deferred stand-alone high-risk (Annex III) obligations to 2 December 2027 and high-risk AI embedded in regulated products (Annex I) to 2 August 2028.
ISO/IEC 42001:2023 (AI management system)
Management system requirements for establishing, implementing, and continually improving an AI management system (AIMS).
Best Practices10
NIST AI Risk Management Framework 1.0
Guidelines to integrate trustworthiness considerations into the design, development, and deployment of AI systems.
EU AI Act (Political Agreement)
First comprehensive regulatory framework for trustworthy AI in the European Union.
Google Responsible AI Principles
Seven commitments guiding the ethical development and deployment of AI at Google.
Microsoft Responsible AI Standard v2
Company-wide governance framework translating principles into measurable requirements.
OpenAI Safety & Alignment Best Practices
Mitigation strategies (RLHF, red-teaming, tiered access) for large language model deployment.
AI Red Teaming
AI red teaming is structured adversarial testing of AI systems to find harmful, biased, or insecure behavior before attackers or real users do, using crafted attacks and probes.
OWASP Top 10 for LLM Applications (2025)
The OWASP Top 10 for LLM Applications lists the most critical security risks for generative AI systems, including prompt injection, sensitive data disclosure, and supply chain risk.
Prompt Injection Defense
Prompt injection defense protects LLM applications from attacks that hide malicious instructions in user input or retrieved content to override the system's intended behavior.
ISO/IEC 42001 AI Management System
ISO/IEC 42001 is the first international standard for an Artificial Intelligence Management System, giving organizations a certifiable framework to govern AI responsibly.
AI TRiSM (Trust, Risk and Security Management)
AI TRiSM is a framework for managing the trust, risk, and security of AI systems across explainability, model operations, data protection, and runtime application security.
Blueprints3
Self-Hosted LLM to Managed Inference Blueprint
Migrate self-hosted GPU LLM serving to a managed inference API to cut operational burden while controlling cost, latency, and data governance.
Keyword Search to RAG Platform Blueprint
Build a retrieval-augmented generation platform over your corpus with embeddings, a vector database, and grounded LLM answers replacing keyword search.
Ad-Hoc Prompt Scripts to AI Agent Platform Blueprint
Consolidate scattered LLM prompt scripts into a governed AI agent platform with tool use via MCP, guardrails, evals, and observability.
Reference Architectures5
LLM Gateway and Proxy on Kubernetes
A reference design for a self-hosted LLM gateway on Kubernetes that centralizes routing, rate limiting, cost tracking, and guardrails across multiple model providers.
Multi-Agent LLM System on Azure
A reference design for a multi-agent application on Azure where specialized LLM agents coordinate through an orchestrator and shared tools to complete complex tasks.
AI Governance and Model Risk Platform (Multi-Cloud)
A reference design for a multi-cloud AI governance platform that inventories models, enforces policy, runs risk reviews, and maintains an audit trail.
LLM Guardrails and Safety Layer on AWS
A reference design for an LLM safety layer on AWS that filters inputs and outputs, blocks prompt injection, and enforces content and PII policies.
Conversational AI Assistant on Azure
A reference design for an enterprise conversational assistant on Azure that combines RAG, tool calling, and memory behind a governed orchestration layer.
Playbooks5
LLM and RAG Application Rollout Playbook
A program for rolling out a retrieval-augmented generation application from prototype to governed production with evals, guardrails, and observability.
Responsible AI Governance Playbook
A program to establish responsible-AI governance covering risk assessment, controls, model documentation, and ongoing oversight aligned to NIST AI RMF and ISO 42001.
AI Red-Teaming Program Playbook
A phased program to build an AI red-teaming capability that adversarially tests LLM systems for jailbreaks, prompt injection, and harmful outputs.
LLM Evaluation Program Playbook
A program to build a rigorous LLM evaluation capability with offline evals, online metrics, and regression gating in CI.
AI Agent Platform Program Playbook
A program to build a governed AI agent platform with tool integration via MCP, guardrails, evaluation, and observability.
Regulations8
EU Artificial Intelligence Act
European Union regulation on artificial intelligence, classifying AI systems by risk level and attaching obligations to each tier
Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
US federal executive order directing agencies to set safety, security, and rights standards for AI development and deployment across government and industry.
Colorado Artificial Intelligence Act
Colorado state law requiring developers and deployers of high-risk AI systems to prevent algorithmic discrimination in consequential decisions.
EU AI Liability Directive (Proposed)
Proposed EU directive easing the burden of proof for victims claiming damage caused by AI systems, complementing the EU AI Act.
Interim Measures for the Management of Generative Artificial Intelligence Services
Chinese regulation governing public-facing generative AI services, covering content safety, training data, and security review.
Artificial Intelligence and Data Act
Proposed Canadian federal law (part of Bill C-27) regulating high-impact AI systems for safety and non-discrimination.
New York City Local Law 144 on Automated Employment Decision Tools
NYC law requiring bias audits and candidate notice for automated employment decision tools used in hiring and promotion.
ISO/IEC 42001 Artificial Intelligence Management System
International management-system standard for governing the responsible development and use of artificial intelligence within organizations.
Comparisons4
GPT vs Claude
OpenAI's GPT and Anthropic's Claude are leading proprietary LLM families. They differ in design philosophy, context handling, and integration ecosystems rather than raw capability tier.
Open-Weight vs Proprietary LLMs
Open-weight models can be downloaded and self-hosted; proprietary models are accessed via API. The split shapes control, cost, privacy, and capability ceilings.
AI Agents vs Workflows
Agentic systems let an LLM decide its own steps and tool use; workflows orchestrate LLMs through predefined paths. The choice trades flexibility against predictability.
Self-Hosted vs Managed Inference
Self-hosted inference runs models on your own infrastructure; managed inference uses a provider's endpoint. The choice trades control, privacy, and cost-at-scale against simplicity.
Benchmarks3
TruthfulQA
A benchmark measuring whether models avoid generating false answers that mimic common human misconceptions and falsehoods.
HELM (Holistic Evaluation of Language Models)
A standardized framework evaluating language models across many scenarios and multiple metrics including accuracy, robustness, fairness, and efficiency.
HarmBench
A standardized red-teaming benchmark that measures how often automated attacks elicit harmful behaviors from LLMs and how well refusal and defenses hold up.
FAQs3
What is hallucination in LLMs?
Hallucination is when a language model produces text that sounds confident and plausible but is factually wrong or unsupported by its sources. It happ...
What is MLOps?
MLOps is a set of practices for reliably building, deploying, monitoring, and maintaining machine learning systems in production, applying DevOps prin...
What is responsible AI?
Responsible AI is the practice of designing, building, and operating AI systems so they are fair, transparent, accountable, secure, and respectful of ...