Build the Control Tower 
​for Enterprise AI

AI adoption is moving fast. Governance, access control, compliance evidence, and performance oversight 

need to move with it.

Join the AI Control Tower Masterclass, a three-part educational webinar series for enterprise leaders 

responsible for making AI visible, secure, governed, and measurable across the organization.

YOU CANNOT GOVERN AI YOU CANNOT SEE

Date: June 16, 2026

Webinar Timings: 2-3 PM ET

The webinar recording is now available. Click below to watch it on demand.

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AI is scaling. Oversight is fragmenting.

Enterprise AI is no longer limited to a few approved models or isolated pilots. Organizations now face a rapidly growing landscape of AI agents, models, copilots, prompts, datasets, integrations, MCP servers, non-human identities, and third-party AI services.


That creates four urgent gaps:

Visibility gap


Teams cannot confidently answer what AI systems exist, where they run, who owns them, or what business processes they support. 


Access gap


AI agents and non-human identities may hold permissions that are difficult to understand, review, or reduce.


Control evidence gap


Risk, compliance, privacy, and audit teams need defensible workflows, documentation, approvals, attestations, and policy evidence.

Performance oversight gap


AI systems need ongoing measurement for quality, drift, accuracy, adoption, value, and operational reliability.

The answer is not another disconnected checklist. It is a control tower model for enterprise AI.

What The Series Covers

The AI Control Tower Masterclass is designed as a working blueprint for enterprise AI governance.

Across three sessions, you will learn how to:

Discover AI systems, models, agents, datasets, and related assets.

Establish ownership, lifecycle governance, and risk classification.

Connect AI governance to control evidence and compliance workflows.

Map AI access across human, machine, and agentic identities.

Identify excessive permissions and reduce AI-related blast radius.

Monitor AI performance, telemetry, accuracy, adoption, and drift.

Measure AI value in terms executives, risk leaders, and platform owners can use.

Establish ownership, lifecycle governance, and risk classification.

Build an operating model that connects AI strategy to day-to-day governance.

Discover And Govern Enterprise AI

Build the system of record and control model for enterprise AI.

Part 1: Discover and Govern Enterprise AI

Foundation session


Build the AI governance baseline. Learn how to create an enterprise AI inventory, classify AI risk, define lifecycle workflows, assign ownership, and connect AI governance to control and compliance evidence.

Best for: AI governance, risk, compliance, ServiceNow platform owners, CIO teams, AI program owners, and data governance leaders.


SAVE MY SEAT FOR PART 1

Part 2: Secure AI Access with Veza

Identity and access session


AI agents are becoming a new class of privileged workforce. Learn how to understand who — or what — can access AI systems, models, tools, and sensitive data, and how to apply least privilege across human, machine, and AI agent identities.

Best for: CISOs, identity leaders, security architects, cloud security, IAM, data security, and AI security teams.


SAVE MY SEAT FOR PART 2

Part 3: Measure Performance, Drift, and Accuracy

Measurement and assurance session


Governance does not end when AI is approved. Learn how to monitor AI performance, detect drift, evaluate accuracy, track adoption, and measure business value over time.

Best for: AI program owners, model risk leaders, CIO teams, data science leaders, platform owners, risk teams, and business technology leaders.



SAVE MY SEAT FOR PART 3

​​Detailed Agenda
​Part 1: Discover And Govern Enterprise AI

Sessions theme: Build the system of record and control model for enterprise AI

AGENDA

The new AI governance challenge

Why AI inventories are breaking down.

How agents, models, datasets, prompts, and integrations expand the governance surface.

Why AI governance needs to connect to business services, workflows, and risk ownership.

Building an enterprise AI inventory

What belongs in an AI asset inventory.

How to think about AI systems, models, datasets, agents, prompts, and supporting infrastructure.

How to structure metadata for owners, purpose, business process, data sensitivity, model provider, lifecycle stage, and risk tier.

The new AI governance challenge

How to classify AI systems by risk.

How to route reviews, approvals, exceptions, and escalations.

How to move from one-time review to lifecycle governance.

Controls and compliance evidence

How to connect AI controls to authority documents, policies, assessments, and audit evidence.

How to operationalize control checkpoints without creating spreadsheet sprawl.

How to prepare for executive, risk, audit, and regulatory conversations.

Operating model takeaway

The roles, workflows, and decision points required to stand up an AI governance control tower.

Part 2: Secure AI Access with Veza

Session theme: Bring identity governance and least privilege into the AI control model.

AGENDA

Why AI access is different

How AI agents, non-human identities, and model-connected workflows change the access governance problem.

Why “who has access?” is no longer enough.

Why teams need to understand effective permissions, delegated authority, and agent-to-human accountability.

Mapping access across humans, machines, agents, and data

How to think about the access graph for AI.

How to identify relationships between users, groups, agents, apps, models, tools, and sensitive resources.

How to expose hidden access paths and overprivileged relationships.

Reducing AI blast radius

How excessive permissions create operational, security, compliance, and data exposure risk.

How to prioritize remediation based on sensitivity, privilege, usage, and business context.

How least privilege applies to agentic AI.

Access reviews and governance workflows

How to bring AI agents and non-human identities into certification and review processes.

How to assign human ownership for agentic access.

How to produce access evidence for audit, compliance, and risk teams.

Operating model takeaway

A practical approach for integrating identity security into enterprise AI governance.

​ Part 3: Measure Performance,
Drift, And Accuracy

Session theme: Move from AI approval to continuous assurance and value measurement.

AGENDA

Why AI measurement belongs in governance

Why approved AI systems still need ongoing oversight.

How performance, accuracy, drift, hallucination risk, adoption, and value affect enterprise trust.

Why technical telemetry and executive metrics need to connect.

Defining the right AI performance metrics

Accuracy, relevance, consistency, latency, quality, user satisfaction, and task completion.

Agent and model behavior monitoring.

Evaluation metrics for business-critical workflows.

Monitoring drift and degradation

What drift means in enterprise AI operations.

How to detect changes in output quality, user behavior, data patterns, or agent performance.

How to trigger review, retraining, rollback, or escalation workflows.

Measuring adoption and value

How to track usage, adoption, realized value, productivity impact, risk reduction, and ROI.

How to avoid vanity metrics.

How to build dashboards that work for CIOs, CISOs, risk leaders, and AI program owners.

Operating model takeaway

A practical measurement framework for AI performance, assurance, and executive reporting.

What You’ll Learn

By the end of the series, attendees will be able to:

Define the core components of an enterprise AI inventory.

Identify where shadow AI, unmanaged agents, and unowned models create risk.

Establish lifecycle governance for AI systems, models, datasets, prompts, and agents.

Connect AI governance workflows to risk, compliance, audit, and control evidence.

Apply access governance principles to human, machine, and AI agent identities.

Use effective permissions and access graph thinking to reduce AI-related blast radius.

Define performance, drift, accuracy, adoption, and value metrics for AI oversight.

Build an AI governance operating model that scales beyond pilots and policy documents.

Who Should Attend

This series is built for enterprise leaders and practitioners responsible for AI adoption, risk, security, compliance, and operations, including:

RECOMMENDED ATTENDEES:

CISOs and security executives

CIOs and digital transformation leaders

AI governance leaders

Risk and compliance leaders

Data governance teams

ServiceNow platform owners

Security architects

Identity and access management leaders

Model risk leaders

AI program owners

Cloud security teams

Privacy, legal, and audit stakeholders

Identity and access management leaders

Business technology leaders responsible for

AI-enabled workflows

Why This Matters Now

AI has moved from experimentation to execution.

The next wave of enterprise AI will not just summarize, recommend, or assist. It will take action across workflows, systems, data, and business processes. That makes governance more urgent and more operational.

Organizations need a way to answer:

What AI exists across the enterprise?

Who owns each system, model, agent, and dataset?

What risks and controls apply?

Who and what can access sensitive systems and data?

Which AI systems are compliant, approved, and monitored?

Are models and agents performing accurately?

Where is drift emerging?

What value is AI actually delivering?

The organizations that answer these questions early will be able to scale AI faster, with stronger trust, clearer accountability, and better risk control.

Featured Speakers

Nicholas Friedman

​President & Founder 

​Templar Shield / RICC IQ

Tommy LaMonte​

Certified Master Architect

​Templar Shield

Brady Maguire

SR Solution Consultant - Risk Management

ServiceNow

Reserve Your Seat For
The AI Control Tower Masterclass

Register once to access the full three-part series. Attend the sessions most relevant to your role, or join all three for the complete AI governance operating model.

Date: June 16, 2026

Webinar Timings: 2-3 PM ET

Watch On-Demand Webinar

Frequently Asked Questions

Yes. Registration gives you access to the full series, and you can attend the sessions most relevant to your role. For the complete blueprint, we recommend attending all three

Yes. Registrants will receive access to recordings after the live sessions, subject to availability and registration confirmation.

The series is designed for CISOs, CIOs, AI governance leaders, risk and compliance teams, data governance leaders, ServiceNow platform owners, security architects, identity leaders, model risk teams, and AI program owners.

No technical prerequisites are required. The series is designed for enterprise leaders and practitioners who need a practical operating model for governing AI at scale.

The series balances executive strategy with practical implementation. Expect operating model guidance, governance workflows, access control concepts, measurement frameworks, and platform-oriented examples rather than deep coding or data science instruction.

The series is most relevant for organizations evaluating or using ServiceNow AI Control Tower, Veza, or adjacent enterprise AI governance capabilities. The operating model concepts will also be useful for teams building broader AI governance programs.

Yes. The series will address how to connect AI governance to risk classification, control evidence, policy alignment, assessments, access reviews, and compliance workflows.

Yes. AI agents are a central theme, especially in Part 2, where we discuss access governance, agent ownership, effective permissions, and least privilege for human, machine, and agentic identities.

Turn AI visibility into AI control

AI adoption will keep accelerating. The question is whether your governance model can keep up.

Join the AI Control Tower Masterclass to learn how to discover, govern, secure, and measure enterprise AI with a practical operating model built for scale.

Watch On-Demand Webinar
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