No AI Without IA: How Regulated Enterprises Can Scale AI Safely and Intelligently

RECORDED WEBINAR

AI Can’t Scale If Your Information Is a Mess.

In regulated industries like financial services, AI fails when knowledge isn’t structured, governed, and traceable. Join us for a candid conversation on how IT leaders can build the foundation for safe, scalable AI starting with information architecture.

What You’ll Learn

IT leaders are under pressure to scale AI, but disconnected systems, siloed content, and compliance risks make that nearly impossible without a structured knowledge layer.

In this session, we’ll explore how information architecture (IA) enables AI systems to be:

• Compliant with traceable, auditable content – In financial services, compliance regulations like GDPR, CCPA, and MiFID II require that financial institutions maintain detailed audit trails. AI systems must provide transparency and be able to track decision-making processes. For example, AI models must be able to generate clear explanations for why certain financial decisions were made, especially in areas such as investment advisory or risk assessment.
• Context-aware, delivering accurate answers, not hallucinations – Financial services often deal with sensitive customer data and market-moving information, where even small errors in AI-generated content can lead to compliance violations. For instance, providing incorrect data in trade recommendations can expose a firm to significant legal and regulatory risks.
• Scalable across regulatory, clinical, and commercial use cases – Financial institutions must manage vast amounts of regulatory documentation, such as quarterly filings, client agreements, and transaction records. AI systems need to scale across this content while adhering to different sets of compliance requirements. One example is the ability to maintain data retention policies in line with regulations such as the Bank Secrecy Act (BSA) or SEC regulations, where improper data handling could lead to penalties.

You’ll gain insight into:

• Why AI fails in regulated environments and what to fix first – A look at real-world cases, such as AI systems in financial firms that failed to meet compliance standards due to poor metadata management, resulting in audit failures and fines.
• What Retrieval-Augmented Generation (RAG) looks like with compliant data – How RAG can leverage well-governed data sources to provide compliant AI outputs, using an example from the financial industry where risk mitigation models are powered by RAG and avoid compliance pitfalls.
• How metadata, taxonomy, and governance enable traceability – Understand the crucial role of data governance frameworks like ISO 27001 or SOC 2 in managing financial data and ensuring that AI systems align with these frameworks for complete traceability of decisions.
• Practical steps for IT to build the foundation without boiling the ocean – Actionable tips for IT leaders on implementing a compliance-first strategy that integrates AI and IA systems without overwhelming resources.