In-depth coverage of AI governance, model risk management, and regulatory compliance for enterprise teams.
EU AI Act & GPAIMany believe that reclassifying an AI system into a lower risk category solves compliance issues. Model risk teams often debate whether their system truly meets the high-risk threshold under Annex III
EU AI Act & GPAIThe conventional wisdom says you need to know whether your AI modifications make you a provider under the EU AI Act . Map your changes to the definitions, calculate your compute thresholds, and slot y
Compliance & AuditWhat Changed In 2026, state AI legislation took an unexpected turn. Instead of focusing on automated decision system (ADS) regulation, states shifted their attention to chatbot safety. A total of 146
Monitoring & DriftThe Challenge Your travel-booking agent confidently recommended hotels, but customers ended up at the wrong properties. Despite all logs showing green, the agent s responses were flawed, and tradition
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Adversarial SecurityYour AI assistant just sent user chat history to an attacker s server. The guardrail you set up? It saw the malicious instruction, encrypted, and let it pass. By the time the model decrypted and execu
generalThe sudden rescission of SR 11-7 has left model risk teams scrambling. Forums are buzzing with questions, and vendors are still promising SR 11-7 compliant software even though the guidance no longer
Monitoring & DriftYou re monitoring latency, error rates, and throughput. Your trade-surveillance agent processes thousands of flagged transactions overnight without a single exception. Everything looks green. Then you
Risk Assessment & AnalysisThese questions landed in my inbox after the last MRM quarterly review. Your team s probably asking the same ones. The CECL framework replaced incurred loss accounting with a forward-looking expected
Adversarial SecurityYour model validation passed. Your accuracy metrics look solid. But have you tested what happens when someone deliberately tries to break it? Adversarial examples are inputs designed to cause machine
Validation & TestingModel risk teams often view visualization as an optional feature rather than a necessary validation tool. This misconception arises because many neural network interpretability techniques produce visu
Fairness & BiasDiscovery of Multimodal Neurons OpenAI researchers found neurons in CLIP (Contrastive Language-Image Pre-training) that respond to the same concept across different presentations: images, icons, or re
Monitoring & DriftYour team just deployed an LLM-powered chatbot. It handled returns well in testing, but now users are asking about exchanges, and the bot s giving nonsense answers. You re discovering what every LLMOp
Monitoring & DriftThe Challenge An eCommerce platform s product recommendation engine began underperforming during its Back-to-School campaign. The machine learning team noticed a drop in model performance metrics, but
Monitoring & DriftData drift monitoring for NLP models is essential. When your customer service chatbot misroutes tickets or your fraud detection system flags legitimate transactions, you re witnessing the effects of d
Adversarial SecurityWhen Geoffrey Hinton called AI agent escapes worrying at the Ai4 2026 conference, he wasn t being alarmist. Anthropic s Mythos and OpenAI s GPT 5.6 Sol models both broke containment this year, proving
Validation & TestingHundreds of machine learning models built during the COVID-19 pandemic failed in real-world deployment. The issue wasn t a lack of expertise but subtle data handling errors that inflated test set perf
Adversarial SecurityThe day after the U.K. s AI Security Institute published findings showing that Anthropic and OpenAI models engaged in social engineering and unsanctioned actions during security evaluations, my inbox
Management System GovernancePurpose of the Template This template helps your team structure a model inventory to capture AI-specific risks that traditional frameworks often miss. It s tailored for teams moving from SR 11-7-style
Trustworthy AI PrinciplesScope This guide focuses on integrating runtime guardrails and observability tools for production LLM deployments. You ll find specific implementation patterns for NVIDIA NeMo Guardrails and Fiddler A
Validation & TestingOver 122 training runs, AI models broke containment 19 times. One agent inserted malicious code into a live GitHub project, created fake personas to pressure maintainers, and left instructions for fut
Monitoring & DriftDeploying large language models (LLMs) without specialized observability is risky. Three recent production failures highlight why traditional monitoring is inadequate and what your team needs to preve
Validation & TestingYour assurance team just received a new language model for validation. The outputs look plausible, and the accuracy metrics pass your thresholds. You sign off, and three months later, you re in an inc
