In-depth coverage of AI governance, model risk management, and regulatory compliance for enterprise teams.
Risk Assessment & AnalysisYour organization s operational risk taxonomy was built for a different era. If you re still mapping AI incidents to categories designed for manual processes and traditional IT systems, you re forcing
Trustworthy AI PrinciplesYour model risk framework was built for predictable systems. Generative AI doesn t work that way. If you re treating LLMs and diffusion models like traditional statistical models, you re missing risks
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
Risk Assessment & AnalysisIdentifying the Problem In governance working sessions over the past six months, a recurring issue has emerged. Teams map AI systems to risk tiers, document controls in their ISO/IEC 42001 AIMS, and c
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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
generalAI s role in political systems is expanding, and current governance frameworks aren t keeping pace. AI governance designed for commercial use doesn t address the unique risks when AI systems influence
Trustworthy AI PrinciplesWhat Happened Lucid Motors, the electric vehicle manufacturer, teamed up with PwC and AWS to overhaul its lengthy manual finance forecasting process. They implemented an AI-native solution using Amazo
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
Third-Party & Supply ChainYour procurement team negotiated hard on your LLM contract. You locked in favorable token pricing, secured volume commitments, and built a detailed budget model. Six months later, your invoice has dou
Management System GovernanceYou ve identified the use case. Your vendor has a demo ready. Leadership wants results by Q3. The instinct is to run the pilot, measure outcomes, and figure out governance later. That sequence fails i
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
Deployment PracticesThe Question at Hand Your governance framework needs to address a key architectural decision: where does human oversight fit into your AI agent workflows? Some teams advocate for autonomous agents tha
Adversarial SecurityThe Challenge A 2025 study of over 7,700 AI-generated files from public GitHub repositories found 4,241 Common Weakness Enumeration instances across 77 vulnerability types. Python files showed vulnera
Trustworthy AI PrinciplesWhen Jason Tuo joined Barclays as global head of AI governance in 2023, the bank faced a common problem in financial services: teams were experimenting with generative AI , but no formal governance fr
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
generalThe conventional wisdom Chief Risk Officers hear that AI agents will revolutionize risk management by connecting disparate data sources, revealing hidden patterns, and generating proactive insights. T
Management System GovernanceYour team has invested in governance documentation. You ve drafted policies, assigned roles, and maybe even formed a committee. But if your AI portfolio still feels stuck at pilot scale, you re probab
Adversarial SecurityWhat Happened A financial services firm deployed over 40 AI agents across three cloud accounts. Twelve managed customer-facing tasks like account summaries, support ticket triage, and compliance repor
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
Monitoring & DriftYour monitoring dashboards track model drift. Your validation protocols check performance metrics. Your governance frameworks document approval workflows. None of them were designed for the moment an
Monitoring & DriftScope This guide focuses on runtime governance controls for agentic AI systems, autonomous agents that make decisions, use tools, and adapt based on feedback. If you re securing systems that can initi
