Robert Yenji

AI Governance & Cybersecurity Consultant | Regulated Industries

Runtime Access Control for AI

RBAC and ABAC assume direct user access. AI breaks this model at every layer. Learn the four failure points.

Why traditional access control fails with AI

A three-layer architecture evaluating authorization at every AI tool call, not at deployment time.

How runtime authorization works at invocation

Same architecture enforces HIPAA, SOC 2, and privilege through configuration alone. No code changes needed.

Apply this across healthcare, finance, and legal

Learn how to define task autonomy, decision thresholds, guardrails, and human override in agent-enabled systems.

Modernizing Business Analysis

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

Agentic AI Skills for Business Analysts

Agentic AI changes the game because AI can autonomously act. It can initiate actions, make decisions, and operate across workflows with increasing autonomy. Risk escalates, and so does opportunity! Without deliberate, disciplined, and structured analysis, agents act on incomplete logic, unclear boundaries, and undefined escalation paths. That is where exposure multiplies. This course matters because Business Analysts are uniquely positioned to define autonomy limits, decision thresholds, guardrails, and monitoring structures before agents scale. It equips you to design control into autonomy, so AI-driven systems remain accountable, coherent, and aligned with business intent rather than becoming fragile at scale.AI is moving beyond assistance. Agents can:<ul><li>Initiate actions on their own</li><li>Make decisions</li><li>Trigger workflows</li><li>Operate continuously</li></ul>This changes the role of Business Analysis.When agents act without clearly defined:<ul><li>Goals</li><li>Boundaries</li><li>Escalation rules</li><li>Monitoring thresholds</li></ul>Organizations lose control.This course teaches how to design and govern agent behavior responsibly.

Prioritize, Define, Design, and Govern AI Agents and Agentic Systems

Distinguish Between Automation, Copilots, and True Agents

Decompose Tasks for Autonomous or Semi-autonomous Execution

Define Agent Goals, Success Criteria, and Measurements

Define/Design Multi-Agent Orchestration

Design Human Override and Intervention Paths

Participate in Ongoing Evaluation of Agent Performance

Business Analysts, Product Owners, Business Systems Analysts, Systems Analysts, Business Process Analysts, and similar roles.

BA Leaders redefining roles for AI-enabled teams

Business Leaders, Project and Product Professionals pulled into AI initiatives without clear accountability

Learn practical ways to lead in responsible AI, with tools you can apply today. 

Responsible AI Advisor & Instructor 

Ahold Delhaize

Johnson & Johnson

ABN AMRO

Hinda Haned

Legatus I

Legatus II

Responsible AI is no longer a niche topic, it’s on the agenda of regulators, boards, and business leaders everywhere. In 2024 alone, governments worldwide issued more than twice as many AI-related regulations as the year before. Add to that more than 80 frameworks, countless checklists, and a steady stream of new tools, and the field quickly feels overwhelming. The real challenge isn’t whether AI should be used responsibly; it’s knowing where to begin. 🎯This course brings you back to the foundations: cutting through the noise, grounding you in what matters, and giving you a clear path forward.Here is detailed breakout of the course:🟩 Week 1: Power Session | Responsible AI PrimerA focused 3-hour session that cuts through the noise and gives you a practical overview of Responsible AI governance.🟩 Week 2: Mentoring &amp; Application | RAI Lab<blockquote>In the second week, you’ll have up to three hours of 1-to-1 mentoring to apply the tools directly to your own context. This may include building a governance checklist, evaluating a vendor, or stress-testing a project plan. The goal is for you to walk away with a concrete, business-ready action plan tailored to your organisation.</blockquote>

Your Responsible AI power session: from principles to practice

Understand core Responsible AI principles in practical business contexts

Recognise common AI risks through real-world system failures

Use a simple Responsible AI risk assessment framework

Define actionable first steps toward Responsible AI adoption

Anyone seeking a clear, practical intro to RAI with real-world examples to apply best practices or guide others effectively.

Leaders seeking to build or improve RAI governance with concrete SOPs and case studies from leading organizations.

Professionals who need to understand and apply RAI to meet regulatory and ethical standards, using field-tested examples.

Everyone is learning to build AI agents. Few can prove one is safe to deploy.

Founder | Agentic AI Governance Advisor

Cyber Pros Training

Centene

ISACA

Trenton Health Team

François B. Arthanas

Founder of CyberProsAI | Agentic AI Governance Advisor | CISSP, CISA, AAIA™

Learn how to govern agentic AI systems before they create risk, fail audits, or get your org in trouble.

Cohort 1

AI agents are not chatbots. A chatbot answers a question. An AI agent retrieves customer records, calls tools, triggers workflows, issues refunds, sends emails, and acts sometimes before a human ever reviews the output.That means the question landing on YOUR desk is different:Can this agent leak PII? → Can it be prompt-injected through a poisoned document? → Can it call the wrong tool or the right tool with the wrong authority? → Can it issue an unauthorized refund? → Who owns the risk? And what evidence proves the controls actually work?Most professionals freeze at this question because they've only ever seen AI governance as policy slides and framework acronyms. They've never actually run an agent risk review end to end.In this one-day sprint, you will. You'll take a realistic enterprise AI agent through the complete practitioner loop scope → risk → control → test → evidence → decision and finish the day having written and defended a real deployment recommendation.You will watch an AI agent get broken live. Then you'll learn exactly how to make sure it never happens on your watch.

In one day, you'll run a real AI agent risk review start to finish and leave able to do it again at work, with the templates to prove it.

Scope a real enterprise AI agen a support agent that can access customer data

Break it: Run a live red-team session against the agent (no coding required)

Turn your findings into a one-page Trust Snapshot leadership can act on

Make the call and defend it in a live mock review board

You do not need to be an engineer. You do not need to know how to code. You do not need advanced AI expertise.

There are no strict prerequisites for this course

If you are curious about AI agents and want a practical way to think about oversight, this workshop will meet you where you are and move you

What you do need is an interest in understanding how AI is changing governance

This workshop is for professionals who know AI agents are becoming important and do not want to get left behind.

It is built for GRC, IT, cyber, privacy, legal &amp; consultants who need to understand how agentic AI changes the governance conversation

You should join this workshop if you want to understand how AI agents create risk &amp; how to contribute meaningful value inside your org

Govern the HR AI tools you already use. Know what's high-risk, build a governance map, and question vendors before you sign anything.

Head of Talent , ex- Novartis | NYU Alum

Novartis

New York University

Aarti Mudaliar

Head of Talent, Novartis | NYU Alumni | Microsoft, AWS & Google certified

Founder, INTLDA | AI Governance Advisor

Pfizer

Accenture

S&P Global

Cornell University

Uvika Sharma 

Founder, INTLDA | AI Governance Advisor | Keynote Speaker | Cornell Alum

You didn't build the AI. You're still liable for it.McDonald's exposed 64 million applicant records. Samsung lost 270,000 customer records through a vendor's unrotated credentials. Air Canada was held liable for what its own chatbot promised a customer. None of these were AI failures. They were governance failures, and the company answered for it, not the vendor.Under the EU AI Act, NYC Local Law 144, and Colorado's new AI law, you're the deployer of record for every HR tool your team runs, whether you built it or bought it. Most HR leaders confuse responsible AI with AI governance. Responsible AI is the standard: fair, explainable, accountable. Governance is the proof: who signs off, what gets logged, what you hand a regulator when a decision is challenged.This course builds that proof live, on tools your team already uses. You leave with three real artifacts: a risk-tiered inventory, a one-page governance map, a vendor accountability assessment. Built by you, in the room, led by two practitioners with 38 combined years in HR operations and AI governance. Not a certificate. Not a template. A file you can hand your CHRO or General Counsel on Monday.

You didn't build the AI. You're still liable for it. Here's the proof you need.

Know which of your HR AI tools are high-risk, and which aren't

Understand the difference between Responsible AI and AI Governance

Build a one-page governance map for one tool you actually use

Know what your vendors aren't telling you

Understand what the law actually requires of HR right now

Leave with three connected artifacts for one real tool

You're a Senior HR Leader who has been handed an AI mandate with no roadmap and you're expected to have a governance point of view on AI

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

The Problem with Traditional Application-Based Access Control

The Three-Layer Architecture for Secure Database Access

Understanding the Limitations of "Build Time" Security

The Advantages of "Invocation Time" Security

Layer 1: The Application Layer and Session Context

Layer 2: The Policy Gateway and Access Control

Layer 3: The Database Layer and AI Tools

A Walkthrough of the App's Implementation and Documentation

Live Demo: Testing the System as a Doctor

Live Demo: Testing the System as a Receptionist

Live Demo: Testing the System as a Nurse

Conclusion: The Importance of Preventing Direct AI Access to Databases

Executives and senior leaders who set strategy, lead teams, and drive organizational outcomes.

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Securing software systems — application security, threat modeling, authentication, and infrastructure protection.

Establishing policies, ethics frameworks, and oversight for responsible AI use in organizations.

Runtime Access Control for AI

What you'll learn

Why this topic matters

You'll learn from

Robert Yenji