ON DEMAND • Podcast Episode

Bridging the Gap in AI Governance: Trust, Risk, and Scale

A candid conversation with Katharina Koerner, Senior Principal Consultant in AI Governance at Trace3, on AI governance maturity, shadow AI, risk management, red teaming, privacy-enhancing technologies, and how organizations can move from AI principles to operational controls.

FEATURED GUEST

Katharina Koerner

Our featured guest

Katharina Koerner

Senior Principal Consultant, AI Governance, Trace3

In this episode, Katharina Koerner shares a practical view on AI governance, AI risk, and AI security. As Senior Principal Consultant in AI Governance at Trace3, she works with organizations on AI strategy, governance, solutioning, risk management, and security.

She also covers:

  • Why AI governance is more than regulation or policy
  • How organizations can close the AI governance maturity gap
  • Where shadow AI creates hidden risk across the business
  • Why AI risk needs to shift left into product and procurement workflows
  • How responsible AI depends on people, processes, tools, and continuous monitoring

Why this conversation matters now

This episode addresses the questions many leaders are asking as AI adoption moves faster than governance, including risk ownership, shadow AI, monitoring, and responsible implementation.

01.

How do we turn AI principles into enforceable practices?

02.

Who owns AI risk across legal, security, privacy, product, IT, and business teams?

03.

How do we detect and manage shadow AI?

04.

When does an AI use case need deeper risk assessment?

05.

How do we monitor AI systems after deployment?

06.

What does responsible AI actually mean in practice?

What you'll learn in this episode

After unlocking the full podcast, you’ll get expert insights on AI governance, risk, and responsible AI adoption.
01.

What AI governance includes across law, policy, ethics, and operational risk

02.

Why organizations struggle to move from high-level principles to real controls

03.

How AI use case inventories and model inventories support governance at scale

04.

Why risk thresholds matter when deciding which use cases need deeper review

05.

How shadow AI detection tools can improve visibility across the organization

06.

Why AI governance should support AI strategy, not just restrict usage

07.

How red teaming helps test AI systems against realistic failure scenarios

Who this episode is for

CEOs and business leaders CTOs and CIOs AI and data leaders Security leaders Privacy, legal, and compliance leaders Product and procurement leaders Teams building AI governance programs

Our host

JocelynHoule

Jocelyn Byrne Houle

AI and data product leader

Jocelyn Byrne Houle is an AI and data product leader, entrepreneur, and investor who has built and scaled transformative technology initiatives across finance, cloud governance, and AI security. She founded Deploy Forward AI to help enterprises adopt AI with strong governance, operational discipline, and long-term organizational ownership.

A preview of the key takeaways

01.

AI governance needs operational discipline

Many organizations have high-level AI principles, but the real maturity gap is turning those principles into enforceable, monitored practices.

02.

Shadow AI is now a visibility problem

Employees are already using AI tools, often without formal approval. Shadow AI detection helps organizations see which tools are being used and where sensitive data may be exposed.

03.

Risk thresholds help governance scale

Not every AI use case needs the same level of review. Clear thresholds help teams decide when deeper assessment is required.

04.

AI risk needs to shift left

AI governance needs to move earlier into product development, procurement, and solution design instead of waiting until deployment.

05.

Responsible AI goes beyond checklists

Responsible AI includes privacy, security, transparency, explainability, safety, robustness, accountability, and ethical judgment.

Why tkxel is sharing this conversation 

 At tkxel, we work with growing businesses navigating AI adoption, cybersecurity, data readiness, software modernization, and responsible AI implementation.

This episode is especially relevant for leaders trying to move from AI experimentation to controlled, scalable adoption. It is not just a policy conversation. It is a business conversation about trust, visibility, accountability, and risk.

WHERE TKXEL BRINGS CONTEXT

1 AI governance and responsible adoption 2 Data readiness and security alignment 3 AI risk and workflow design 4 AI implementation at scale

RECOGNIZED BY

Key concepts covered in this podcast

1

AI governance maturity

Explains how organizations can move from broad AI principles to enforceable controls.

2

Shadow AI

Covers how unsanctioned AI usage creates visibility, data, security, and compliance risks.

3

Risk thresholds

Shows why teams need clear criteria for deciding which AI use cases need deeper review.

4

Use case and model inventories

Explores how inventories help teams track AI usage, ownership, and risk.

5

Shift-left AI risk

Looks at why AI risk checks should happen earlier in product and procurement workflows.

6

Red teaming

Covers how teams can test AI systems against realistic failure and misuse scenarios.

7

Privacy-enhancing technologies

Explains why data protection techniques matter for responsible AI adoption.

Frequently Asked Questions (FAQs)

Is this episode only for compliance teams? Expand FAQ Collapse FAQ
No. While the conversation covers governance and regulation, it is also relevant for CEOs, CTOs, CIOs, data leaders, product teams, security leaders, procurement teams, and AI transformation teams.
What do I get after filling out the form? Expand FAQ Collapse FAQ
You get access to the full podcast episode and transcript.
What is the core focus of the episode? Expand FAQ Collapse FAQ
The core focus is AI governance maturity, shadow AI, AI risk management, red teaming, responsible AI, privacy-enhancing technologies, and operationalizing AI controls.
Why is this podcast gated? Expand FAQ Collapse FAQ
It is positioned as a premium resource for leaders actively researching AI governance, AI risk, responsible AI, and secure AI adoption.
Can this help teams planning AI governance programs? Expand FAQ Collapse FAQ
Yes. The conversation is useful for teams thinking about use case inventories, model inventories, risk thresholds, shadow AI detection, AI security, and continuous monitoring.

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