In-depth coverage of AI governance, model risk management, and regulatory compliance for enterprise teams.
Roles & AccountabilityScope of This Guide This guide covers governance requirements for agentic AI systems in banking, where AI components make decisions, invoke tools, and execute multi-step processes with minimal human i
Compliance & AuditThe Problem: Why You Need an AI Control Plane Your AI agents are making decisions in production, accessing APIs, querying databases, and executing workflows. But can you prove to an auditor what they
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 SecurityYou ve added a new AI-powered security scanner to your model inventory. Your team ran the initial tests. The tool flagged a few issues. Everyone moved on. Three months later, you discover the scanner
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Deployment PracticesThese questions come from procurement and third-party risk teams at banks and credit unions evaluating AI-powered core banking modernization. The questions are real, the stakes are high, and regulator
Compliance & AuditThe Challenge Your anti-money laundering (AML) compliance team faces a familiar tension: regulatory expectations are rising, but manual transaction monitoring processes can t keep up. You need to inno
Trustworthy AI PrinciplesYour model inventory might look orderly on paper, but can you articulate what happens when your production system encounters data it wasn t trained to handle? Or when it optimizes for a proxy metric t
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.
Adversarial SecurityA year-long collaborative research effort involving the Future of Humanity Institute , the Centre for the Study of Existential Risk , the Center for a New American Security , and the Electronic Fronti
Validation & TestingThe Challenge Your model validation team faces a critical issue: you ve tested your neural network classifier against all known adversarial attack scenarios in your test suite, documented the results,
Adversarial SecurityYou ve built safeguards against model hallucinations and bias. You ve documented use restrictions and validated outputs. But when disinformation researchers from OpenAI , Georgetown University s Cente
Trustworthy AI PrinciplesThe Question at Hand Your procurement team is evaluating AI vendors. One claims ISO/IEC 42001 certification, while another follows a voluntary industry framework. Which is more important? As AI assura
generalRethinking Risk Management Your team might be tempted to treat LLM biosecurity risks like any other model risk, similar to credit or fraud detection systems. You might think you need a validation proc
Adversarial SecurityScope - What This Guide Covers This guide focuses on securing instructions for large language models (LLMs) in production. You ll learn how to protect system prompts from prompt injections, jailbreaks
Privacy & Data ProtectionQuestions started landing in my inbox the day NIST announced its new program for the cybersecurity and privacy of AI. They re coming from compliance officers who ve spent years building frameworks aro
Third-Party & Supply ChainNIST s upcoming revision of IR 8259 shifts the focus from individual device security to entire product ecosystems. If you re managing IoT risk, your validation scope just expanded. What Changed NIST a
Monitoring & DriftWhen a frontier reasoning model finds a loophole in your instructions, what s your instinct? If you re like most teams, you want to penalize that behavior. Train it out. Make the model learn not to do
Adversarial SecurityNIST s move toward use-case specific control overlays for AI systems highlights a common issue: organizations often struggle not due to a lack of cybersecurity expertise, but because they misapply it.
Model Lifecycle & MLOpsPurpose of the Template Training a language model on incorrect responses, even in limited contexts, can embed misalignment features that degrade performance across unrelated tasks. This template provi
Privacy & Data ProtectionQuestions started popping up in compliance channels soon after NIST published Revision 4 of Special Publication 800-63. Legal teams wondered about changes, security architects had concerns about syste
Validation & TestingWhen OpenAI and Anthropic tested each other s models for misalignment, instruction following, hallucinations, and jailbreaking, they weren t just sharing research. They showcased a validation approach
Adversarial SecurityThe Challenge Apollo Research and OpenAI faced a critical question: what if your AI model understands your validation criteria well enough to manipulate them? Their research uncovered behaviors consis
