Field Notes from FLLR: AI Governance

Welcome to the fourth of Field Notes from FLLR, from Nik Fuller and Dan Harms.

Each month, we'll break down the latest in privacy tech, enforcement actions, and practical fixes. Our team will cut through the noise and give you actionable insights you can actually use. We’ll also fill you in on upcoming privacy events we’ll be at!

In this inaugural issue, we examine AI Governance.

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Upcoming Webinars & Events 🚀

Privado Webinar: Privacy Risk Monitoring for CMPs
August 28 | Nik Fuller (FLLR) and Privado Partnership

• Essential insights on continual privacy risk monitoring for consent management platforms. 

• Perfect conversation starter for prospects evaluating their current CMP effectiveness.

Register: https://www.privado.ai/webinars/why-cmps-need-continual-privacy-risk-monitoring

OneTrust Webinar: From compliance to scale: Real-world lessons in privacy automation
September 3 | Dan Harms (FLLR) and OneTrust

• Real-world implementation lessons transitioning from compliance to scalable privacy operations. 

• Panel with other partners along with Ryan Edge, Director of Strategy, OneTrust

Register: https://www.onetrust.com/resources/from-compliance-to-scale-real-world-lessons-in-privacy-automation-webinar/

TrustWeek - The Premier Privacy Leadership Event
Join the most influential privacy professionals in the industry for two days of strategic insights, exclusive networking, and forward-thinking sessions you won't find anywhere else. This isn't just another conference—it's where privacy leaders come to stay ahead of the curve.

What You'll Experience:

• Exclusive Access: Deep-dive sessions with privacy innovators and regulatory experts

• Strategic Networking: Connect with Chief Privacy Officers, compliance leaders, and technology experts from Fortune 500 companies

• Cutting-Edge Content: First-hand insights on AI governance, emerging regulations, and privacy technology trends

• Industry Intelligence: Learn what top-tier organizations are actually implementing (not just talking about)

• NYC: September 9

• SF: September 30

Spaces are limited and filling fast | Register: https://www.onetrust.com/trustweek/ | Use code TW25FLLR for $100 off

This is where privacy strategy gets shaped for the year ahead. Your team's competitive advantage starts here.

AI Governance: From Strategy to Implementation

The conversation around AI governance has shifted from "if" to "how." With the EU AI Act enforcement beginning in 2025 and regulations emerging globally, organizations need practical frameworks that balance innovation with accountability. Here's what privacy and compliance leaders need to consider when building AI governance that actually works.

5 Strategic Considerations for AI Governance Implementation

1. Address the Full Spectrum of AI Risk

The Reality: Organizations often focus on technical AI capabilities while underestimating governance and compliance risks.

What This Means for You:

• AI systems introduce risks across privacy, ethics, compliance, and operations

• Model drift, bias, fairness, and accuracy issues can emerge after deployment

• Regulatory violations can result in significant fines and reputational damage

Implementation Approach: Start with comprehensive risk inventory. Many organizations benefit from beginning with AI project mapping using existing data governance tools, then expanding to specialized AI risk management capabilities. This phased approach builds on established privacy processes while creating visibility into AI initiatives across the organization.

Strategic Question: How does your organization currently track AI models and datasets across business units, and do you have visibility into potential risk issues before they impact operations?

2. Navigate the Evolving Regulatory Landscape

The Regulatory Reality: AI compliance requirements are materializing rapidly across multiple jurisdictions.

What This Means for You:

• EU AI Act creates mandatory requirements for high-risk AI systems starting in 2025

• US federal guidelines and state-level regulations are emerging

• Sector-specific requirements (financial services, healthcare) add additional layers

• Existing data protection laws (GDPR, state privacy laws) apply to AI systems

Implementation Approach: Successful compliance strategies address both current data protection obligations and emerging AI-specific requirements through integrated assessment frameworks. Organizations should leverage existing privacy compliance capabilities while building AI-specific governance controls.

Strategic Question: How is your organization preparing for the intersection of current privacy regulations and emerging AI compliance requirements?

3. Leverage Existing Privacy and Compliance Infrastructure

The Strategic Opportunity: The most successful AI governance programs build on existing privacy and risk management capabilities rather than starting from scratch.

What This Means for You:

• Extend current data mapping and assessment processes to include AI projects

• Build relationships between AI initiatives and existing privacy inventories

• Leverage established risk management frameworks for AI-specific risks

• Integrate AI governance workflows with existing compliance processes

Implementation Approach: Organizations can maximize existing platform investments by starting with data mapping for AI project inventory, then progressively expanding to specialized AI governance capabilities. This creates a foundation that supports both current compliance needs and future AI oversight requirements.

Strategic Question: How can your current privacy and GRC programs be extended to address AI governance requirements without creating parallel systems?

4. Design Governance That Enables Innovation

The Balance Challenge: Effective AI governance should accelerate responsible AI deployment, not create bureaucratic obstacles.

What This Means for You:

• Governance processes should integrate naturally with development workflows

• Lightweight intake assessments can surface risks early without slowing innovation

• Automated monitoring and reporting reduce manual oversight burden

• Clear approval processes and policy guidance enable faster decision-making

Implementation Approach: Focus on governance frameworks that provide clear guardrails while enabling experimentation. The most effective approaches emphasize standardized intake processes, automated risk monitoring, and integration with existing MLOps and development tooling.

Strategic Question: How can your organization deploy AI initiatives faster while maintaining confidence in compliance and risk management?

5. Ensure Data Integrity from Consent to Model

The Foundation Issue: The biggest risk to AI model longevity and compliance isn't technical—it's data governance and consent management.

What This Means for You:

• AI models must only use properly consented data to ensure regulatory compliance

• Consent withdrawals and preference changes can impact model validity

• Data lineage from consent collection to model training becomes critical

• Integrated consent and AI governance strategies prevent costly model retraining

Implementation Approach: Next-generation AI strategies require clean data foundations that connect consent management with AI governance. This integrated approach ensures data integrity throughout the AI lifecycle and prevents compliance issues when privacy preferences change or regulations evolve.

Strategic Question: How confident is your organization that AI models are using properly consented data, and what happens when individuals exercise privacy rights or regulations change?

Practical Implementation Framework

Phase 1: Foundation Building

• Establish AI project inventory using existing data governance tools

• Create lightweight AI risk assessment templates

• Build relationships between AI projects and existing privacy inventories

• Develop basic intake processes for AI initiatives

Phase 2: Specialized Governance

• Deploy comprehensive AI governance capabilities

• Implement model and dataset monitoring

• Formalize AI risk management policies and workflows

• Establish compliance frameworks for regulatory requirements

Phase 3: Integrated Operations

• Integrate governance controls with MLOps and development environments

• Automate policy enforcement and compliance monitoring

• Enable real-time risk visibility and reporting

• Achieve seamless governance that supports innovation

Key Takeaways

Start with What You Have: Leverage existing privacy and compliance capabilities as the foundation for AI governance rather than building parallel systems.

Think Holistically: AI governance isn't just about model monitoring—it requires integration across data governance, consent management, risk assessment, and compliance workflows.

Balance Innovation and Control: The most successful implementations enable faster AI deployment through clear processes and automated governance rather than creating bureaucratic obstacles.

Prepare for Evolution: Build governance frameworks that address current compliance requirements while remaining flexible for emerging regulations and business needs.

Next Steps

Building effective AI governance requires understanding both the regulatory landscape and practical implementation challenges. Organizations that start with proven frameworks and leverage existing privacy capabilities while building toward comprehensive oversight position themselves for long-term success.

The key is taking a strategic approach that balances immediate compliance needs with scalable governance that supports AI innovation across the organization.

For strategic guidance on AI governance implementation and privacy technology architecture, connect with specialists who understand both regulatory requirements and practical implementation challenges.

Have any privacy tech questions?Reach out to us.

Nik Fuller: [email protected] | (404) 731-7814

Dan Harms: [email protected] | (770) 337-6719

This newsletter is for informational purposes only and does not constitute legal advice. For questions about specific compliance needs, please contact the FLLR Privacy Team.

Visit our website to learn more - www.fllrconsulting.com. 

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