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Research & Case Studies

Thinking in Data

Production case studies, architecture notes, and practical perspectives on financial analytics, automated reporting, and governed AI.

FEATURED | AI Governance & ArchitectureLinkedIn Pulse
Oct 1, 2026
Verifiable Identity and Delegated Authority for AI Agents (Know Your Agent) - SimBI by Data Sense
AI Governance & ArchitecturePulseOct 1, 2026

Verifiable Identity and Delegated Authority for AI Agents (Know Your Agent)

As AI agents transition from read-only information retrieval to executing transactions across databases, APIs, and payment rails, verifiable agent identity becomes necessary for governing what those agents are authorized to do.

Nicholas Lombardi
Nicholas Lombardi, FRM
Founder & Lead Analyst
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RECENT ARTICLES & CASE STUDIES

Showing 9 articles
AI Governance
Oct 28, 2026
AI Governance

Architecting Agent-Ready Metadata for Autonomous Systems

Nicholas Lombardi, FRM

Architecting Agent-Ready Metadata for Autonomous Systems

AI agents act on definitions immediately. In regulated financial workflows, metadata that only describes meaning is no longer sufficient. Meaning must be encoded formally, so that contradictions are caught by a reasoner before an agent acts, not by an auditor after it has.

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Economic Research & AI Policy
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APPLIED ADVISORY & ENGINEERING

Need to Operationalize These Architectures in Your Organization?

Whether you are automating investor or regulatory reports in Python and Quarto, deploying Power BI and Microsoft Fabric, or implementing AI data lineage, schedule a direct technical consultation with Nicholas Lombardi.

Schedule a Consultation →
Pulse
Oct 22, 2026
Will the AI Productivity Boom Reach Canadian Workers? What Canada's Economy Is Already Telling Us - Data Sense

Will the AI Productivity Boom Reach Canadian Workers? What Canada's Economy Is Already Telling Us

AI promises a productivity boom. But Canada's recent record on median real wages, GDP per capita and wealth distribution shows that productivity gains do not automatically translate into higher living standards.

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AI Governance
Sep 18, 2026
Featured image for Data Sense article: Why eval() Has No Place in Agentic Data Pipelines, showing a secure data conduit with eval quarantined on a white studio background.

Securing Agentic Data Pipelines

Combining dynamic evaluation (eval(), new Function()) with agentic data ingestion creates direct paths to Remote Code Execution (RCE). Indirect prompt injections can escape reasoning context into host runtime behavior if downstream rendering evaluates code dynamically. Constrained, AST-based template interpreters (like Knap) replace arbitrary execution with deterministic schema rendering. Robust agent design separates probabilistic LLM reasoning from deterministic data transformation and analytical calculation.

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AI
Jul 23, 2026
SimBI MCP, a Power BI MCP server to create Power BI dashboards.

Generating complete Power BI Dashboards with AI and Natural Language

Creating Power BI dashboards from scratch often involves repetitive layout tasks: creating cards, placing slicers, dragging visual components around the canvas, and tweaking pixel alignments. While Power BI Desktop handles data modelling well, building visual report layouts manually remains a time-consuming chore. To automate this workflow, we built an open-source Power BI MCP (Model Context […]

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AI Governance
May 11, 2026
Institutional traceability connects identity, lineage, evaluation, and policy into a durable operating record that financial institutions need to demonstrate governed AI to regulators and auditors.

Institutional Traceability: How to Build the Operating System of AI Governance

Policy engines execute governance decisions. But institutions also need a way to reconstruct those decisions, the controls in effect at the time, and the evidence that supports them. Institutional traceability is the layer that turns a governance stack into a verifiable system of record.

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AI Governance
Apr 27, 2026
AI Policy Engines: Operationalizing AI Governance for Financial Institutions

AI Policy Engines: How to Operationalize AI Governance for Financial Institutions

Evaluation can show whether an AI system is performing acceptably. It cannot, by itself, decide what should happen next. AI policy engines fill that gap by translating governance logic into repeatable runtime decisions across agents, applications, and workflows.

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AI Governance
Apr 20, 2026
AI Agent Evaluation Featured Image

How to Evaluate AI Agents: Building a Governance Framework

AI gateways control whether a model call is allowed to happen. Evaluation systems determine whether autonomous behavior remains acceptable after that access has been granted. In financial institutions, that means measuring performance continuously, defining thresholds explicitly, and inserting human review or restrictions before failure becomes systemic.

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AI Governance
Apr 13, 2026
AI Gateways: The Control Plane for Model Access Featured Image

AI Gateways: The Control Plane for Model Access

Identity, lineage, and semantics make AI systems interpretable. They do not, by themselves, control model access. AI gateways are the enforcement layer that determines whether a model call is allowed to happen at all, which model path is permitted, and what runtime constraints apply.

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AI Governance
Apr 6, 2026
Featured image for data sense post Semantic Layers: The Hidden Infrastructure Behind Scalable AI Governance

Semantic Layers: The Hidden Infrastructure Behind Scalable AI

Lineage can show how a decision was made. It cannot guarantee that the data, features, rules, and policy terms behind that decision meant the same thing everywhere they were used. That is the role of the semantic layer: to make business definitions machine-readable, reusable, and governable so AI systems can operate correctly at scale.

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