Private-sector advisors who work with government clients – or whose clients sell products and services to governments – have a direct interest in public-sector AI governance. Government AI procurement requirements frequently cascade to vendors: if an agency must demonstrate that its AI tools meet certain standards, the vendor supplying those Read more
Most published AI governance frameworks were designed with large, well-resourced organizations in mind. The NIST AI RMF Playbook, ISO/IEC 42001, and the EU AI Act conformity assessment process all assume access to dedicated legal, compliance, data science, and ethics functions – resources that most small and mid-sized enterprises (SMEs) simply Read more
A generic AI governance framework provides a starting point. Regulatory obligations, however, are sector-specific – and the gap between a general policy and a compliant program widens quickly once you enter a regulated industry. This chapter covers the requirements that apply directly to financial services (with particular focus on credit Read more
AI systems, by definition, are data systems. They are trained on data, they ingest data at inference time, they produce data-derived outputs, and they frequently generate inferences about individuals that those individuals never consented to produce. Every AI deployment in an organization is simultaneously a data governance question, a privacy Read more
AI and cybersecurity interact in two directions simultaneously. AI is an attack surface – AI models can be manipulated, poisoned, and exploited in ways that legacy security frameworks do not address. And AI is a defensive tool – AI-powered threat detection, behavioral analytics, and automated response are materially changing the Read more
Agentic AI refers to AI systems that perceive their environment, form and pursue goals over multiple steps, take actions using tools and APIs, and operate across extended tasks without moment-to-moment human direction. Unlike conventional AI, which generates a single output in response to a single prompt, an agentic AI system Read more
Traditional machine learning models are narrow in scope: a fraud detection model flags transactions; a credit scoring model outputs a risk score. Their failure modes, while consequential, are generally bounded and measurable. Generative AI (GenAI) – large language models, image generators, code assistants, and multimodal systems – is different in Read more
For most enterprises today, the primary source of AI risk is not internal AI development. It is the AI embedded in the software they have already purchased. Microsoft 365 Copilot, Salesforce Einstein, ServiceNow’s AI features, HR platforms with automated screening, and financial tools with embedded prediction models all represent AI Read more
AI governance is frequently introduced to senior leadership as a compliance cost. That framing is both incomplete and counterproductive. Governance is simultaneously a cost reduction program (it reduces the probability and severity of expensive incidents), a sales enabler (it unlocks regulated-buyer markets and enterprise procurement), and a form of insurance Read more
AI governance is no longer a management-level discretionary matter. Boards of publicly traded companies, regulated financial institutions, and major private organizations now face explicit expectations – from regulators, shareholders, and insurers – that they will exercise meaningful oversight of AI risk. The era of delegating AI entirely to the technology Read more
