In-depth coverage of AI governance, model risk management, and regulatory compliance for enterprise teams.
Trustworthy AI PrinciplesThese questions come from the front lines. After working with numerous model risk and assurance teams implementing AI governance frameworks, I ve noticed the same concerns surface repeatedly. The ques
Monitoring & DriftYour autonomous AI assistant is handling customer inquiries across multiple messaging platforms, executing browser actions, and scheduling follow-ups around the clock. You trust it with business-criti
Privacy & Data ProtectionYour cryptographic assumptions are about to break. Not in theory, not eventually, but within the planning horizon of systems you re validating today. Model risk and assurance teams often assume the en
Monitoring & DriftMost teams evaluating AI observability tools approach coding agents with the same checklist they used for chatbots. They ask about framework support, trace volume, and dashboard quality. Then they dep
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Monitoring & DriftWhen your agent fleet makes thousands of autonomous decisions per hour, after-the-fact alerts turn into incident reports. You need a policy that runs inline and blocks at execution time, not logs that
generalThe Conventional Wisdom There s a belief that Generative AI demands entirely new validation approaches. You ve likely heard it: traditional frameworks like SR 11-7 were made for simpler models, not fo
Monitoring & DriftMost teams building agentic AI systems carry over assumptions from predictive model work that don t apply. By the time you realize your evaluation framework is broken, you ve already optimized the wro
Adversarial SecurityYour compliance team just asked for proof that your AI agent can t be manipulated into executing unauthorized actions. You need a structured test plan that documents what you tested, how you tested it
Model Lifecycle & MLOpsAddressing the Challenges Conversations with model risk teams at banks reveal a consistent pattern: your processes, designed for 50-150 credit and fraud models, are now overwhelmed by the demand to de
Monitoring & DriftYour LLM is live, traffic is climbing, and you re staring at a monitoring bill that s about to hit six figures because you re evaluating every single request. This is the operational question every Mo
Model Lifecycle & MLOpsYour model works in the demo but fails in production. The issue isn t your prompt engineering, it s your harness. An agent harness is the operational framework that turns a stateless language model in
Validation & TestingThese questions arise in every model validation review, risk committee meeting, and Slack thread when something unexpected happens in production. They re not theoretical; they re what you re dealing w
Model Lifecycle & MLOpsThe Challenge Your team at a mid-tier asset management firm uses a three-agent pipeline for automated research reporting. Agent A retrieves market data, Agent B performs quantitative analysis, and Age
Deployment PracticesA customer-facing agent passes every pre-deployment test. Your team ships it. Two weeks later, a manual review catches a 30% drop in response quality. No alert fired. The harness knew what the agent c
Deployment PracticesThe question at hand Your validation team has signed off. Your champion-challenger tests passed. Your documentation package is complete. But you re still staring at a deployment decision, wondering: w
Model Lifecycle & MLOpsWhen you re validating cost controls for an agentic AI deployment, capturing token consumption accurately is essential. This template provides a telemetry schema that separates token spend from value
Adversarial SecurityUnderstanding the Injection Risk Coding agents now have access to shell commands, git credentials, package-manager permissions, and cloud tokens. A successful prompt injection can escalate from a simp
Adversarial SecurityThe Problem: Why You Need Red Teaming Now Your organization deploys an LLM-powered customer service agent. Within hours, a user discovers they can inject instructions that leak PII from previous conve
Management System GovernanceYou re facing a growing model inventory, tighter budgets, and regulators who don t care about your headcount freeze. Strengthening your model risk management program isn t optional under SR 11-7 and s
Validation & TestingYour validation team faces growing demands without an increase in staff. You re tasked with validating traditional credit risk models, business applications, non-model analytical tools, and now genera
Validation & TestingModel risk teams face a practical dilemma: your existing validation framework worked for credit scorecards and pricing models, but now you re being asked to approve a customer service LLM or a documen
Adversarial SecurityYour model passed validation with 98% accuracy on the test set, and your team deployed it to production. Then, a carefully crafted image tricked it into classifying a stop sign as a speed limit sign.
