In this episode, Jo Peterson, of Cleartech Research, discusses AI security challenges with Maribel Lopez. Her commentary explores how traditional security tools fall short for AI agents and what strategies enterprises can adopt to manage AI risks effectively.

Topics covered

• AI security challenges and solutions
• Multi-layered approach to AI agent security
• Token cryptographic delegation and OBO tokens
• Externalized policy as code and micro-segmentation
• Contextual and data-aware guardrails
• Continuous auditing and behavioral baselines
• Role of Chief AI Officer in security governance
• AI risk management and operational strategies

Key takeaways

• Traditional security tools are not designed for AI agents and their non-deterministic behavior.
• Implementing least privilege for AI agents involves multi-layered strategies including token delegation and policy enforcement.
• Externalizing authorization to decoupled policy decision points enhances security for AI workflows.
• Real-time observability and kill switches are critical for managing AI agent behavior.
• Many organizations claim to have AI governance but lack technical implementation and operational ownership.

Chapters

00:00 Introduction to AI security challenges
02:04 Non-deterministic nature of AI and security implications
04:32 Externalized policy as code and micro-segmentation
07:12 Real-time observability and kill switches
09:11 Effectiveness of AI governance in organizations
12:13 Immediate actions for AI security in 30 days
13:45 Centralized AI traffic interception and inventory
14:29 Role of Chief AI Officer in security and risk
16:19 The evolving role of AI leadership in organizations
17:19 Talent acquisition and upskilling for AI security

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Neeraj Verma of NiCE on why AI pilots stall, building agents as small reusable units, and the scale problem personal agents are about to create.

Full show notes
This one was recorded live at NiCE World 2026 in Orlando, and Neeraj Verma, Head of AI at NiCE, didn't dodge the hard parts. We started where most enterprise AI conversations should start and rarely do: the outcome. If you can't name what the technology is supposed to pay you back for, you're experimenting for experimentation's sake, and that's the pattern I see stalling pilots everywhere.

From there we got into the fast-moving stuff — what an agent actually is, where skills fit, and why the smart move is building small, reusable units of work rather than monolithic agents. Neeraj made a point I keep thinking about: agents aren't humans, they're context engines, and they need small context to execute well. We also dug into guardrails and observability, and the real tension there — you have to have them, but not in a way that doubles your cost or ruins the experience.

If you're a technology leader trying to move from pilot to production, or a CX leader watching personal agents start to change what "scale" even means, this is worth your time. The honest through-line: this wave will move faster than any before it, and it still takes real infrastructure and a continuous-innovation mindset to get right.

What we cover

• Why an outcome has to come before the AI build
• What an AI agent is, and how skills package tools and processes
• Building small, reusable, modular units of work for agents
• Context as compressed enterprise data — and why compression is the hard problem
• Harness engineering, and "agentic whack-a-mole"
• Compounding intelligence and what self-learning really means today
• Personal agents, agent-to-agent communication, and the coming scale problem
• Guardrails, observability, and keeping shadow AI in check

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Dell's CTO built a 4-category agent framework from real production deployments. Most enterprises are ignoring two of the categories that matter most.

Full Show Notes

Enterprise leaders are mapping AI agents to org charts — building digital employees, agentic teams, AI workers — and then wondering why the results fall short. Dell's Global CTO John Roese has been running agents in production long enough to know exactly why that framing fails, and what to do instead.

In this episode, Roese shares a framework Dell developed from actual production deployments, not pilots. It identifies four categories of AI agents defined by two dimensions: how much autonomy you grant the agent, and how complex the underlying process is. Most enterprises are focused on one category. Two of the four are widely overlooked — and they may represent the fastest path to measurable ROI.

This is a practical, grounded conversation about where agents are actually delivering value today, how to think about infrastructure cost in the context of agent economics, and why the sequence in which you deploy agents matters as much as which agents you build. If your organization is trying to move from AI experimentation to production, this episode is required listening.

3. Chapter titles:

• [00:00] — Introduction: Dell's dual role as tech vendor and enterprise AI user
• [01:38] — Why the org chart model for agents fails
• [03:12] — Decoupling human capacity from work capacity for the first time
• [04:23] — The two-by-two framework: autonomy vs. process complexity
• [06:14] — Productivity agents: what most enterprises already have
• [07:00] — Hygiene agents: the overlooked category that fixes foundational data problems
• [08:01] — The CRM data example: why every CRM is inaccurate and how agents fix it
• [10:05] — Latent infrastructure capacity: running agents in GPU white space to cut costs to cents
• [13:53] — Facilitation agents: removing entropy from complex cross-functional workflows
• [17:30] — The sequencing insight: hygiene and facilitation as the path to expert agents
• [19:24] — Why coordination agents aren't agentic bosses — and where human control actually lives
• [22:21] — Roese's closing advice: become literate, pick a few, get them into production

4. Guest Bio

John Roese is the Global Chief Technology Officer and Chief AI Officer at Dell Technologies, where he is responsible for technology strategy, AI deployment, and research and development across the company. He has held senior technology leadership roles at Nortel, Enterasys Networks, Broadcom, and EMC. At Dell, he operates at a rare intersection: leading AI strategy for a major technology vendor while also deploying AI internally at enterprise scale — which means his frameworks are tested against real production constraints, not just market positioning.

• LinkedIn: linkedin.com/in/johnroese
• Dell Technologies: dell.com

About This Podcast

AI with Maribel Lopez is a podcast for enterprise technology leaders navigating AI adoption, agentic systems, AI infrastructure, and AI governance. Host Maribel Lopez covers enterprise technology and advises CIOs, CDOs, CMOs, and technology vendors on how to move from AI experimentation to measurable business outcomes. New episodes published bi-weekly.

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Picking a use case, proving value, and expanding has been the standard starting point for enterprise AI. For organizations early in their AI journey, that advice still holds. But for large enterprises that are past the pilot stage and trying to scale across business units, geographies, and brands, it isn't enough.

At NVIDIA GTC, Cameron Davies, Chief Data Officer of Yum Brands, shared how his team is thinking about AI differently — and why they had to. With 63,000 restaurant locations, 100 million daily transactions, and 1,500 franchisees across 155 countries, Yum operates at a scale where a single bad AI decision can fail loudly, repeatedly, and fast.

In this episode, Maribel breaks down Davies' framework and what it means for how enterprise leaders should be thinking about AI in 2026 and beyond.

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**What you'll learn**

- Why the use case as a unit of AI planning has a structural limitation at enterprise scale
- What "scalable AI skills" means and why it's different from building agents for specific use cases
- Why governance has to come before deployment, not after — and what happens when it doesn't
- How measurement functions as operational discipline, not just a reporting obligation
- What Yum's AI flywheel looks like and why it only works if measurement is continuous
- What this framework means for organizations that aren't Yum-sized


About Cameron Davies

Cameron Davies is the Chief Data Officer at Yum Brands, the parent company of KFC, Taco Bell, Pizza Hut, and The Habit Burger Grill. He leads the company's corporate data and analytics strategy and oversees the development and adoption of advanced data capabilities. He previously spent seven years as SVP at NBCUniversal and over 18 years at The Walt Disney Company, where he led the Corporate Center of Excellence for AI and machine learning.

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**Resources and references mentioned**

-NVIDIA GTC session: "Scaling AI Agents Globally Across Brands, Use Cases, and Restaurants" (S81755) — Cameron Davies, Yum Brands
- Responsible AI Institute — chaired by Manoj Saxena
- Trustwise — AI trust startup founded by Manoj Saxena
- Byte — Yum Brands' proprietary e-commerce, point-of-sale, and menu platform
- Lopez Research blog: The Rules for Scaling AI Have Changed. Yum Brands Proved It. — [LINK]

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Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/
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Not every AI problem is a language problem. I talk with Vinci CEO Hardik Kabaria about what changes when AI has to reason about the physical world.

Full show notes

Most of the AI conversation in enterprise circles is about large language models — text, code, maybe images. This episode is about something different: what happens when AI has to reason about physical systems where the laws of physics don't negotiate and a wrong answer can't be patched after the product ships.

I talked with Hardik Kabaria, CEO of Vinci, about how physics-based AI models are built differently from generative models, why determinism is a requirement rather than a preference in hardware design, and what it means for organizations manufacturing physical products to think carefully about where AI fits in their workflow. The conversation covers data security, scalability, and the practical question of how to evaluate new AI tools when the cost of a mistake is measured in product recalls rather than content edits.

This episode is most relevant for technology leaders at companies that design or manufacture physical products. But the underlying insight — that deterministic and probabilistic AI serve different purposes and require different evaluation criteria — applies to any organization building a portfolio of AI tools.

What we cover:

• Why physics-based AI is a different modality than large language models, and what that means for how you build and evaluate it
• The case for determinism in AI: why hardware design requires the same answer every time, regardless of who asks
• How AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talent
• Why data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address that
• How to think about AI across the full product lifecycle, from early concept to manufacturing sign-off
• What "trust but verify" looks like in practice: building benchmarks before deploying AI in high-stakes design workflows

Timestamps:

Chapters:
00:00 Introduction to AI and Vinci
02:04 Understanding Physics Intelligence Layer
04:20 The Role of Physics in AI Models
07:04 Digital Twins and AI Scalability
09:35 Misconceptions in AI for Physical Systems
12:15 Determinism vs. Non-Determinism in AI
15:01 Deployment Challenges for Physics-Based AI
17:41 Signals of Success in AI Implementation
20:20 The Future of AI in Hardware Design
23:01 Preparing for the Shift to AI in Physical Systems

Guest bio Hardik Kabaria is CEO and co-founder of Vinci, an AI company building foundation models for the physical world. His background is in physics and geometry software for hardware engineering, with experience across the tools mechanical and electrical engineers use to design, simulate, and manufacture physical components. Vinci was founded two and a half years ago and is focused on making physics-based analysis accessible at the speed and scale of AI inference.

• Company: Vinci

Resources mentioned:

• Vinci: https://www.getvinci.ai
• Lopez Research blog: https://www.lopezresearch.com/research/

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NVIDIA’s NemoClaw adds enterprise security to OpenClaw. What it does, what it doesn’t, and what CIOs should do before deploying.

FULL SHOW NOTES

OpenClaw became the fastest-growing open-source project in history. Enterprise buyers watched from the sidelines — not because the technology wasn’t useful, but because an autonomous agent with access to corporate file systems, credentials, and external communication channels is a governance and security problem that no one had solved at the enterprise level.

At NVIDIA’s GTC 2026 conference, Jensen Huang announced NemoClaw: a reference stack that adds enterprise security controls to OpenClaw. In this solo episode, Maribel Lopez breaks down what NemoClaw actually does, why the SaaS partner ecosystem matters as much as the technology itself, and where the hype is running ahead of the reality.

WHAT WE COVER

• Why OpenClaw created a shadow IT problem before NemoClaw existed

• What OpenShell, the Privacy Router, and Nemotron models actually do for enterprise buyers

• Why Salesforce, ServiceNow, SAP, Cisco, and CrowdStrike being in the ecosystem matters

• The hardware dependency NVIDIA’s marketing glosses over

• Why “working with NVIDIA” and “ready to deploy” are not the same thing

• The three questions every CIO should answer before touching any of this

TIMESTAMPS

00:00 — Why enterprise IT teams were watching OpenClaw from the sidelines

01:45 — What OpenClaw is and why it created an enterprise security problem

04:00 — What NemoClaw actually does: OpenShell, Privacy Router, Nemotron

06:30 — The SaaS ecosystem: Salesforce, ServiceNow, SAP, Cisco, CrowdStrike

08:30 — Where the hype is ahead of the reality

10:15 — Three questions CIOs should answer before deploying

RESOURCES MENTIONED

• NemoClaw announcement and NVIDIA Agent Toolkit: build.nvidia.com

• Full written analysis: NemoClaw Brings Enterprise-Grade Security Controls to OpenClaw — lopezresearch.com

• NVIDIA GTC 2026 Jensen Huang keynote

ABOUT THIS PODCAST

AI with Maribel Lopez covers enterprise AI adoption, agentic systems, AI governance, and AI-driven customer experience. Maribel Lopez is founder and principal analyst at Lopez Research, a technology research and strategy firm.

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KEYWORDS

enterprise AI agents, agentic AI security, NemoClaw NVIDIA, OpenClaw enterprise deployment, AI agent governance, enterprise AI strategy, AI governance enterprise, agentic AI risks

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