What Agentic AI Means for Risk, Governance, and Compliance Leaders 

Agentic AI is no longer experimental. It’s operational. 

Across financial services, agentic AI is moving from isolated pilots to embedded intelligence—reshaping how compliance teams detect risk, manage communications, and uphold regulatory accountability. 

This isn’t an incremental change. It’s a structural shift. 

But agentic AI doesn’t just analyze. It reasons. It connects signals across channels. It recommends actions. And that evolution raises critical questions for risk and governance leaders: 

• What does explainability mean when AI operates across voice, chat, email, and video simultaneously? 
• How should compliance roles evolve when AI shifts from assistant to decision partner? 
• And most importantly, how do we scale intelligence without compromising trust? 

At a recent industry discussion featuring Katrina Helde Cook (Head of Financial Crime Compliance & MLRO at Handelsbanken Wealth & Asset Management), Vall Herard (CEO & Co-founder, Saifr.ai at Fidelity Labs), Stacey English (Regulatory Lead at Symphony), and Justin Shu (Head of Quant and AI Strategy at MillTech), one theme was unmistakable: Agentic AI must be built with governance first. 

Where Agentic AI Is Creating Real Compliance Impact 

A clear example is communications surveillance. 

Regulatory mandates require firms to monitor market abuse, misconduct, bribery, insider trading, and data leakage across every channel—not just email, but voice, chat, meetings, and screen sharing. 

The numbers add up quickly. As Stacey English, Regulatory Lead at Symphony, shared: “If 10 employees each have 10 meetings a week, that quickly balloons to 100 hours of video and audio with in-meeting chat, screen shares, and email follow-up.  Trying to detect the risks within those conversations just doesn’t scale and isn’t possible to do manually.” 

It doesn’t scale. But agentic AI does. 

Instead of sampling, it reviews comprehensively. Instead of reacting days later, it surfaces contextual risk signals in minutes. Instead of drowning teams in alerts, it prioritizes what truly matters. 

High-Impact Use Cases Expanding Today: 

• Communications Surveillance – Contextual risk detection across multi-modal interactions 
• Adverse Media Screening – Rapid synthesis of external signals to identify reputational risk 
• Transaction Surveillance – Reducing false positives by adding behavioral and conversational context 
• KYC Refresh Automation – Comparing documents, flagging inconsistencies, accelerating due diligence 

The next leap forward? Integration. 

Rather than siloed monitoring systems, the future lies in a unified, intelligent risk view — where transactional, behavioral, and communications data converge. That’s where real risk visibility begins. 

Infrastructure: The Hidden Advantage 

Agentic AI is only as strong as the foundation beneath it. Financial institutions operate across structured, semi-structured, and unstructured data. Bringing those together isn’t optional — it’s essential. 

As Justin Shu, Head of Quant and AI Strategy, emphasized, strong metadata practices and clean architecture determine whether AI becomes transformative or chaotic. 

Privacy and security must also lead design decisions. Many forward-thinking firms are prioritizing smaller, task-specific models deployed locally, maintaining control over sensitive data while leveraging AI capabilities responsibly. 

In surveillance environments where data is noisy and fragmented, AI must not just work. 

It must work transparently. It must work accountably. It must work in alignment with regulatory expectations. 

Rethinking Explainability: From Transparency to Traceability 

Explainability remains one of the most debated issues in AI governance. 

Vall Herard, CEO & Co-founder at Saifir.ai (Fidelity Labs) challenged the pursuit of perfect transparency: 

“If you put the top 100 doctors in the world in this room and asked them, can you fully explain how the brain interacts with every part of your body? They would fail that test. However, that does not stop us from making medicine in a practical, sensible way that saves people’s lives.” 

The parallel is powerful. The goal isn’t theoretical perfection—it’s contextual traceability. 

Katrina Helde Cook’s, Head of Financial Crime Compliance & MLRO at Handelsbanken Wealth & Asset Management, approach was pragmatic: Know your processes, test your outcomes, and “be very clear on what it is that we don’t know.” 

And Stacey English offered a governance shortcut that resonated: “Treat AI like an employee. AI is an overenthusiastic toddler eager to please. It will make things up if left unsupervised.” 

Oversight is therefore foundational. 

The Evolution of Compliance Leadership 

Regulators, including the FCA and FINRA, have made it clear: No new AI-specific frameworks are coming. Existing accountability structures still apply. But roles are evolving. Compliance professionals are shifting from reactive alert reviewers to: 

• Model oversight leaders 
• Technology-literate risk strategists 
• Governance partners in system design 

This shift demands: 

• Stronger tech fluency 
• Model risk management expertise 
• Early involvement in AI deployment decisions 

As Vall Herard reminded the group, “This is just a different surface form of what we’ve been doing for 30 years.” From scenario models to Monte Carlo simulations, finance has always adapted to new layers of model risk. 

Agentic AI doesn’t introduce a completely new category of risk — it expands the scope and complexity of the models firms must govern.

Start Now. Scale Intelligently. 

You don’t need a full AI transformation roadmap to begin. 

The smartest strategy is: 

1. Choose a high-volume, lower-risk use case 
2. Benchmark AI against historical case data 
3. Prototype with compliance, technology, and governance teams aligned from day one 
4. Complete end-to-end eComms compliance visibility — unifying data capture, surveillance, investigation workflows, and reporting into a single, defensible source of truth

Justin Shue offered a clear warning: Waiting for perfect readiness is itself a risk.

“If you keep waiting…you lose the opportunity of building knowledge and understanding as the ecosystem evolves.”  

Firms that experiment responsibly today build institutional knowledge that compounds tomorrow. 

From Pilot to Production 

You don’t need to build everything from scratch. You need to start smart and scale with confidence. 

The most effective path forward isn’t experimentation without direction. It’s partnering with a validated, production-ready solution that already delivers a complete, end-to-end view of eComms compliance. 

Why? Because fragmented tools create fragmented risk visibility, siloed monitoring leads to missed context, and disconnected systems slow down decision-making when speed matters most. 

A proven, governance-first platform should provide: 

• Unified surveillance across all communication channels — voice, video, chat, email, mobile, and collaboration tools 
• Contextual risk detection — connecting conversations, behaviors, and transactions 
• Built-in explainability and auditability — aligned with regulatory expectations 
• Human-in-the-loop workflows — empowering compliance teams, not replacing them 
• Scalable architecture — ready for growing data volumes and evolving regulations 

Starting with a trusted, field-tested solution allows compliance leaders to: 

• Accelerate deployment 
• Reduce implementation risk 
• Benchmark AI performance against historical data 
• Deliver measurable impact from day one 

AI adoption in compliance isn’t just a technology decision—it’s an operational and governance decision. The focus should be on deploying intelligence that strengthens oversight, aligns with regulatory expectations, and keeps human judgment central. 

With the right foundation, scaling AI becomes a controlled advantage—enabling compliance teams to move from reactive monitoring toward more proactive risk management. 

Shield’s AI capabilities embody the very principles this discussion surfaced: Governance-first design, contextual reasoning, and human oversight. Utilizing agentic AI should empower compliance teams to navigate complexity, cut through noise, and act decisively in a world where trust is non-negotiable. 

If you’d like to speak to an expert to explore how Shield can support your compliance needs with robust, purpose-built AI functionality, please contact us.