AI Security, AI Cloud Ops & Infrastructure | ClearTech Loop

The conversation around AI is shifting from what agents can do to how enterprises actually govern and secure them. 

In this episode of ClearTech Loop, Jo Peterson sits down with cybersecurity professional Marcus Cylar to talk about least-privilege access for AI agents, shadow AI, security awareness and what CISOs should prioritize as agentic AI becomes part of the enterprise. 

Marcus challenges the idea that AI automatically requires an entirely new security playbook. His argument: before organizations rush toward new controls and platforms, they need to make sure the cybersecurity fundamentals are actually working. 

The conversation covers why least privilege, role-based access, system inventory and clear ownership still matter; why shadow AI can reveal unmet employee needs; and why creating a culture where employees can honestly disclose the tools they are using is critical to effective governance. 

For CISOs starting from zero, Marcus offers a practical first step: know what you have. 

Before you can govern AI agents, you need visibility into the systems, permissions, ownership and AI tools already operating inside your organization. 

In this episode: 

• Why AI agents do not eliminate traditional cybersecurity fundamentals 
• Least-privilege access in an agentic environment 
• Why security awareness matters even more with AI 
• Shadow AI and the “Department of No” 
• The importance of system and AI inventory 
• What CISOs should prioritize in the next 30 days 
• Why trust and transparency are part of AI governance 

Featured Guest: 
Marcus Cylar, DMin 
Cybersecurity professional focused on GRC, security culture, awareness training and program development. 

Hosted by: 
Jo Peterson 
CIO, Clarify360 | Chief Analyst, ClearTech Research 

Episode Quote: 
“That path starts with a passionate return to the fundamentals of cybersecurity.” — Marcus Cylar 

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What if an AI agent had to earn—and keep—its access based on how it behaved? 

In this episode of ClearTech Loop, Jo Peterson sits down with Elliott Mattice, founder of Exprima, to examine trust as the missing operating layer between AI security and AI governance. 

Traditional controls can define an agent’s identity and permissions. Governance frameworks can establish policies and accountability. Elliott argues that organizations still need something in the middle: continuous behavioral trust that can raise, lower or revoke an agent’s access based on what it actually does. 

Jo and Elliott discuss why accountability must still land with a human, how organizations can balance useful autonomy against unrestrained risk and why an MCP server could function as an enforcement point—not merely a bridge to enterprise tools and data. 

The agent does not need to feel guilty when it crosses a boundary. The systems around it need the authority to say no. 

What We Cover 

• Why policies and technical guardrails are not enough to operationalize AI governance 
• How behavioral trust could be continuously measured and tied to access 
• Why human accountability remains necessary when an agent takes an unauthorized action 
• How MCP servers could evaluate identity, permissions and current trust before granting access 
• Why autonomy is both the value of an AI agent and the source of its risk 
• What an agent may need to do to rebuild trust after crossing a boundary 

Featured Soundbite 

“We can give AI enough room to be independent, to be autonomous, as long as we hold it accountable for its outputs.”
— Elliott Mattice 

Guest 

Elliott Mattice is the founder of Exprima, an advisory and consulting firm focused on cybersecurity compliance, federal procurement risk and decision realism. He has more than 25 years of experience across federal IT operations, cybersecurity, compliance and regulated environments. 

Guest website: https://elliottmattice.work/ 

Host 

Jo Peterson is the CIO of Clarify360 and Chief Analyst at ClearTech Research.   

Full episode webpage: https://cleartechresearch.com/cleartech-loop-elliot-mattice-on-ai-governance-missing-trust-layer/

Subscribe to ClearTech Loop: https://www.linkedin.com/newsletters/7346174860760416256/ 

Watch on YouTube: https://www.youtube.com/@ClearTechResearch 

Topics 

AI governance, agentic AI, behavioral trust, AI agent accountability, MCP security, non-human identity, AI access control, defense in depth, AI risk management 

Tags / Keywords 

AI governance; agentic AI security; behavioral trust; AI agents; MCP servers; AI accountability; non-human identity; cybersecurity governance; autonomous agents; ClearTech Loop; Elliott Mattice; Jo Peterson 

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What happens when an AI agent takes an action no one authorized? 

The answer is not, “The model did it.” 

In this episode of ClearTech Loop, Jo Peterson sits down with cybersecurity and technology executive Billy Spears to unpack the gap between AI policy and actual AI control. 

They discuss: 

• Who is accountable when an AI agent makes an unauthorized decision 
• Why agents should never inherit broad permissions by default 
• How identity and authorization must work at runtime 
• Why third-party MCP servers should be treated as untrusted 
• What organizations need to prove when something goes wrong 

Billy’s warning is simple: 

“AI is not eliminating risk; it’s amplifying the consequence of weak controls.” 

If your AI governance lives in a PDF while your agents operate with broad access, this episode is for you. 

Listen now to learn what real AI governance looks like when systems begin to act. 

About Billy Spears 

Billy Spears is a technology and cybersecurity executive with more than 25 years of experience across security, IT, privacy and business operations. He has held executive roles at Dell, Hyundai and loanDepot and currently advises executives and boards while building a stealth cybersecurity startup. 

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AI agents are becoming part of the enterprise workforce, but many organizations still do not know where those agents are, what they can access, or what they are allowed to do. 

In this ClearTech Loop Special Edition sponsored by Okta, Jo Peterson speaks with Matthew Hansen, Regional Chief Security Officer and Head of Customer Audit at Okta, about the identity challenge behind agentic AI. 

They discuss Okta’s AI Blueprint, Okta for AI Agents, and why enterprises need to treat AI agents as first-class, non-human identities with clear ownership, lifecycle management, runtime enforcement, and a way to revoke access fast when something goes wrong. 

EPISODE DESCRIPTION: 
Agentic AI is creating a new security problem: identity sprawl. 

AI agents can connect to systems, access data, trigger workflows, and act on behalf of users. But if organizations cannot see those agents, govern their access, or understand what they are doing, productivity gains can quickly turn into security risk. 

In this episode, Jo Peterson talks with Matthew Hansen from Okta about how organizations can move from Shadow AI and unmanaged agent activity toward verified, governed AI environments. 

The conversation covers: 

• Why every AI agent needs an identity 
• How Shadow AI extends beyond employee chatbot use 
• The three questions organizations need to answer: where agents are, what they connect to, and what they can do 
• Why static credentials and broad permissions create risk 
• How runtime enforcement and human-in-the-loop controls help govern agent behavior 
• Why an AI kill switch may become a critical backstop for enterprise AI 

This ClearTech Loop Special Edition is sponsored by Okta. 

Learn more about Okta for AI Agents platform: https://bit.ly/4dZ5FkU 

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AI governance is no longer just a policy conversation. As AI moves into business workflows, sanctioned platforms, employee tools, local models, agents, and third-party services, organizations need to understand where AI is being used, what it can access, who approved it, and who owns the outcome when something goes wrong. 

In this episode of ClearTech Loop, Jo Peterson speaks with Derek Fisher, founder of Securely Built, cybersecurity educator, author, and Director of Temple University’s Cyber Defense and Information Assurance Program. 

Derek brings a practical security lens to AI governance, AI agents, and third-party MCP risk. The conversation covers why governance needs clear ownership, how organizations should think about AI agents as non-human actors with access and authority, and why MCP servers and AI-enabled services should be evaluated through a third-party risk management lens. 

This episode is especially relevant for security leaders, technology leaders, compliance teams, and business executives trying to move AI from experimentation into controlled, accountable use. 

In This Episode:

• Why many organizations still do not know where AI is being used 
• Why AI governance needs executive ownership, cross-functional standards, and business accountability 
• How AI agents create new access control and auditability challenges 
• Why agents should be treated more like privileged non-human actors than simple tools 
• What organizations should ask before adopting third-party MCP servers or AI-enabled services 
• Why AI governance is not about slowing the business down, but making the approved path usable enough that people follow it 

Key Questions:

1. How do we operationalize AI governance, and who is legally accountable when an AI agent makes an unauthorized decision? 
2. How do we prevent agents from executing actions the user should not be allowed to perform? 
3. How do organizations verify the authenticity and security of third-party MCP servers and services? 

Featured Guest:
Derek Fisher 
Founder, Securely Built 
Director, Cyber Defense and Information Assurance Program, Temple University 

Derek Fisher is a cybersecurity leader, educator, author, and speaker with experience across product security, secure software development, governance, risk management, regulatory compliance, incident response, and cybersecurity education. 

Host:
Jo Peterson 
CIO, Clarify360 
Chief Analyst, ClearTech Research 

Additional Resources: 

• Securely Built 
  https://securelybuilt.substack.com/ 
• The Application Security Program Handbook https://www.manning.com/books/application-security-program-handbook 
• Derek Fisher on SecureWorld News 
  https://www.secureworld.io/industry-news/author/derek-fisher 
• Your AI Coding Assistant Has Root Access—and That Should Terrify You https://www.secureworld.io/industry-news/your-ai-coding-assistant-has-root-access 

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AI agents are becoming part of the enterprise workforce, but many organizations still do not know where those agents are, what they can access, or what they are allowed to do. 

In this ClearTech Loop Special Edition sponsored by Okta, Jo Peterson speaks with Matthew Hansen, Regional Chief Security Officer and Head of Customer Audit at Okta, about the identity challenge behind agentic AI. 

They discuss Okta’s AI Blueprint, Okta for AI Agents, and why enterprises need to treat AI agents as first-class, non-human identities with clear ownership, lifecycle management, runtime enforcement, and a way to revoke access fast when something goes wrong. 

EPISODE DESCRIPTION: 
Agentic AI is creating a new security problem: identity sprawl. 

AI agents can connect to systems, access data, trigger workflows, and act on behalf of users. But if organizations cannot see those agents, govern their access, or understand what they are doing, productivity gains can quickly turn into security risk. 

In this episode, Jo Peterson talks with Matthew Hansen from Okta about how organizations can move from Shadow AI and unmanaged agent activity toward verified, governed AI environments. 

The conversation covers: 

• Why every AI agent needs an identity 
• How Shadow AI extends beyond employee chatbot use 
• The three questions organizations need to answer: where agents are, what they connect to, and what they can do 
• Why static credentials and broad permissions create risk 
• How runtime enforcement and human-in-the-loop controls help govern agent behavior 
• Why an AI kill switch may become a critical backstop for enterprise AI 

This ClearTech Loop Special Edition is sponsored by Okta. 

Learn more about Okta for AI Agents: https://www.okta.com/products/govern-ai-agent-identity/

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In this ClearTech Loop Special Edition from AWS Summit New York, Jo Peterson speaks with Himanshu Verma, Worldwide Leader for Security, Identity, and Governance Specialists at AWS, about how agentic AI is changing the identity risk conversation. 

They discuss why AI agents need to be treated as non-human identities, how AWS Security Hub helps centralize identity risk management, and why multi-Region resilience matters for enterprise security leaders. 

Episode Description 

AI agents are moving from assistive chatbots to autonomous, task-executing digital employees. That creates new identity risks around permissions, visibility, accountability, and resilience. 

In this special episode, Jo Peterson and AWS’s Himanshu Verma discuss: 

• Why agentic AI changes identity risk 
• The problem of over-permissioned agents 
• How AWS Security Hub centralizes identity and access visibility 
• Why Security Hub helps correlate findings across core AWS security services 
• How multi-Region resilience supports enterprise identity and continuity 

Guest 

Himanshu Verma 
Worldwide Leader for Security, Identity, and Governance Specialists, AWS 

Host 

Jo Peterson 
CIO, Clarify360 
Chief Analyst, ClearTech Research 

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Short Description 

Season 3 of ClearTech Loop kicks off with AI security architect Phil Stafford in a practical conversation about AI governance, agent permissions, fractional identity, and why MCP servers may be the next software supply chain risk hiding in plain sight. 

Episode Description 

AI agents are moving from interesting experiments into real business environments. That means they are not just answering questions anymore. They are calling tools, touching systems, inheriting permissions, and creating a new layer of operational risk that technology and security leaders need to understand. 

In the Season 3 kickoff of ClearTech Loop, Jo Peterson sits down with Phil Stafford, AI security architect, security researcher, and cybersecurity professional, to talk about what happens when agentic AI stops being theoretical and starts acting inside the enterprise. 

This conversation gets into the practical questions leaders should be asking now: How do we govern agents when the legal system is still catching up? How do we limit what agents can actually do? What happens when an agent inherits a user’s full permissions? And are MCP servers becoming the next software supply chain problem? 

Phil puts it plainly: MCP has been described as the USB for AI. That is useful, but also a little terrifying if organizations treat every new connector like it belongs in the enterprise by default. No one would pick up a random USB stick in a parking lot and plug it into a company system. And yet, that is not a bad description of how some AI tooling is being adopted right now. 

This episode is for anyone thinking about AI governance, AI security, agentic AI, MCP servers, identity, permissions, supply chain risk, or what due diligence needs to look like when AI systems are allowed to take action. 

In This Episode 

Jo and Phil discuss: 

• Why AI governance has to move beyond policy language and into operational controls 
• Why measurement is the first step in governing AI agents 
• Who may be accountable when an AI agent makes an unauthorized decision 
• How the confused deputy problem shows up in agentic AI 
• Why agents should not automatically inherit full user permissions 
• What fractional identity means and why it matters 
• How sub-agents can create another layer of access risk 
• Why MCP servers need to be treated like part of the enterprise stack 
• How MCP security connects to software supply chain security 
• Why AI SBOM-style thinking may become increasingly important 

Featured Quote 

“MCP was sold to us as the USB for AI… You would not pick up a USB stick in your parking lot and put it into your enterprise environment. That’s what people are doing right now.” 

— Phil Stafford 

Why Listen 

Because AI governance is no longer just a strategy conversation. Once agents begin acting inside workflows, systems, and business processes, the risk becomes operational. This episode helps leaders think more clearly about what needs to be measured, limited, validated, monitored, and documented before agent behavior becomes tomorrow morning’s problem. 

Chapters 

00:00 — Introduction to Season 3 of ClearTech Loop 
00:28 — Meet Phil Stafford 
01:00 — Operationalizing AI governance 
01:14 — Why measurement comes first 
01:58 — Legal accountability and due diligence 
02:43 — The confused deputy problem 
03:39 — Why agent permissions need to be scoped 
04:05 — What fractional identity means 
05:45 — Time-bound permissions and agent behavior 
06:48 — Sub-agents and inherited access 
08:17 — MCP servers and the AI security lifecycle 
08:35 — MCP as the USB for AI 
09:53 — Allow lists, detection, and unapproved servers 
10:35 — MCP as a software supply chain issue 
11:32 — AI SBOMs and applying existing controls 
12:18 — Closing thoughts 

Guest Bio 

Phil Stafford is an AI security architect, security researcher, and cybersecurity professional. He advises organizations on AI security infrastructure, cybersecurity foundations, AI transformation strategy, and secure implementation practices. His work focuses on practical approaches to AI security, MCP risk, agent reliability, and the infrastructure needed to support safer AI adoption. 

Resources 

• Singularity Systems
  https://securingthesingularity.com/ 
• The Adversarial Trust Layer: Why the MCP Ecosystem Needs Cryptographic Attestation and Multi-Agent Verification
  https://credence.securingthesingularity.com/papers/adversarial-trust-layer.html 
• Phil Stafford on Medium
  https://medium.com/@pe.stafford 
• Watch ClearTech Loop on YouTube
  https://www.youtube.com/@ClearTechResearch 
• Subscribe to the ClearTech Loop LinkedIn Newsletter
  https://www.linkedin.com/newsletters/7346174860760416256/ 

Follow ClearTech Loop 

ClearTech Loop is hosted by Jo Peterson, CIO of Clarify360 and Chief Analyst at ClearTech Research. Subscribe for more Season 3 conversations on AI security, governance, infrastructure, cloud, cybersecurity, and the technology decisions shaping enterprise strategy. 

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AI is moving faster than policy, training, and many traditional controls were designed to handle. 

In this episode of ClearTech Loop, Jo Peterson talks with Gerald Auger, Chief Content Creator of Simply Cyber, about shadow AI, non human identities, over-permissioned agents, and what AI defense means when AI systems can act at machine speed. 

Gerald brings the educator, GRC, and practitioner-community lens to the conversation. His take is practical: organizations probably cannot put AI back in the bottle, so they need to educate users, provide approved tools, bring agents into identity governance, and start treating AI governance like a real security discipline.  

What You’ll Hear in This Episode 

Jo and Gerald discuss: 

• Why shadow AI is a problem for IT, security, and the organization  
• How AI is becoming easier to use inside everyday SaaS tools  
• Why sensitive data in public AI tools creates a visibility gap  
• Why user education has to be part of AI security  
• How non human identities and AI agents create new permissioning risks  
• Why Gerald thinks organizations may need a “manager in the loop”  
• What AI defense means when AI systems can act quickly and at scale  

Key Insight 

AI governance is becoming its own discipline. 

Gerald’s point is not that organizations can stop AI adoption. It is that they need to build around it with education, approved tools, segmented environments, identity controls, better detection, and practical guardrails before “just let it run” becomes the strategy. Which, respectfully, is not a strategy. It is a group project with consequences. 

Timestamps 

00:00 Introduction to Gerald Auger 
00:30 Gerald’s background in cybersecurity, education, and Simply Cyber 
01:38 Shadow AI as an IT, security, and organizational issue 
03:00 Why public AI tools create data visibility risk 
04:40 Why organizations have to “ride the lightning” 
06:46 Jo on the missing layer of AI security training 
07:13 AI inside everyday tools and emerging attacker behavior 
08:58 Non human identities and over-permissioned agents 
12:30 AI Wrangler or Manager in the Loop? 
13:12 What AI defense means in practice 
15:46 AI Gone Wild and closing thoughts 

Guest Bio 

Gerald Auger, PhD, is Chief Content Creator of Simply Cyber. He is a cybersecurity educator, GRC practitioner, community builder, and creator of the Simply Cyber Daily Cyber Threat Brief. 

He has a PhD in Cyber Operations from Dakota State University and teaches cybersecurity at The Citadel. Through Simply Cyber, Gerald helps cybersecurity professionals build careers through practical education, daily threat briefings, and practitioner-first community content.  

Resources 

• Simply Cyber Academy: The Definitive GRC Analyst Program https://academy.simplycyber.io/p/the-definitive-grc-analyst-program 
• Flashlight in a Dark Room: A Grounded Theory Study on Information Security Management at Small Healthcare Provider Organizations by Gerald Auger
  https://scholar.dsu.edu/theses/329/ 
• Subscribe to ClearTech Loop on YouTube: https://www.youtube.com/@ClearTechResearch/  

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Your AI policy does not matter much if no one understands how to follow it. 

In this episode of ClearTech Loop, Jo Peterson talks with Maybelyn Plecic, Manager of Training and Adoption at Network to Code, about shadow AI, non human identities, and what AI defense actually means when people are already using AI to get work done. 

Maybelyn brings a security, compliance, training, and adoption lens to the conversation. She is CISSP certified, AWS certified, and has spent her career helping teams strengthen security posture, drive compliance initiatives, and make technical change usable. 

Why This Matters 

AI adoption is already happening inside organizations. 

The challenge is that governance, policy, training, and approved tools are not always keeping pace. 

That creates risk, but not always because people are acting recklessly. In many cases, employees are trying to move faster, automate boring work, and solve problems the official process has not solved yet. 

Maybelyn frames shadow AI as an IT issue, a security issue, and a trust issue. Her point is clear: if leaders want people to use AI safely, they have to make the safe path understandable, practical, and easier than the workaround. 

What You’ll Hear in This Episode 

• Why shadow AI starts with trust, not blame 
• How protected proof of concept environments and AI sandboxes can reduce risk 
• Why shared language matters when AI systems, agents, and workflows touch data 
• How prompt injection, AI training defaults, and history tracking create new security concerns 
• Why AI defense is not just a tooling conversation 
• How leaders can create AI guidance that teams will actually follow 

Key Insight 

AI security is not only about tools and controls. 

It is about whether people understand the rules, whether the approved process works, and whether organizations are willing to meet teams where the work actually happens. 

As Maybelyn says in the episode: “how do you expect someone to be compliant if they don't even know the rules, right?” 

Timestamps 

00:00 Introduction to ClearTech Loop 
00:26 Meet Maybelyn Plecic 
01:29 Shadow AI: IT problem, security problem, or both? 
01:54 Why shadow AI starts with trust 
03:00 AI is moving faster than governance 
04:47 AI generated content, visibility, and accountability 
06:35 How language around AI is changing 
08:43 Using AI to automate the boring work 
10:40 How AI changes the CISO conversation 
12:33 Non human identities and the importance of shared language 
13:05 Workflow questions become security questions 
14:26 Prompt injection, AI defaults, and training gaps 
15:47 What AI defense means beyond tools 
17:30 Why AI guidance has to match each team 
18:45 Closing thoughts 

Guest Bio 

Maybelyn Plecic is the Manager of Training and Adoption at Network to Code. She specializes in helping teams make technical change practical, secure, and usable. 

Her work spans security posture, compliance initiatives, technical enablement, training strategy, and customer adoption. She brings a builder’s perspective to AI security, with a focus on making complex technology easier for people to understand and use responsibly. 

Additional Resources 

• Maybelyn Plecic website: https://www.maybelynplecic.com/ 
• Network to Code Resource Center: https://networktocode.com/resources/resource-center/ 
• NIST AI Risk Management Framework:
  https://www.nist.gov/itl/ai-risk-management-framework 
• Season 1 ClearTech Loop: https://www.buzzsprout.com/2248577 

Follow ClearTech Loop for more conversations on AI security, cybersecurity leadership, AI governance, shadow AI, non human identities, and enterprise technology strategy. 

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