AI governance starts with complete capture
Generative AI is being rapidly woven into the fabric of regulated financial services. Employees are already using tools like Claude Enterprise, Microsoft 365 Copilot, and ChatGPT Enterprise to streamline workflows, accelerate decisions, deepen research, and improve customer experience.
Every AI interaction also creates a new layer of business communications data, including prompts, responses, uploaded files, summaries, metadata, and audit events. As adoption accelerates, firms face a growing challenge: enabling AI innovation without losing visibility, context, or control.
In many organizations, AI usage is already outpacing governance programs, increasing the risk of fragmented visibility and unsanctioned AI activity.
The firms best positioned for what comes next will be the ones that can capture AI interactions in full context across LLM, collaboration, voice, mobile, social, and web channels. Complete capture creates the foundation for defensible oversight, stronger supervision, and a clearer understanding of how generative AI is reshaping work across the enterprise.
Key takeaways
- AI interactions are becoming part of the regulated communications record alongside traditional business communications.
- FINRA Regulatory Notice 24-09 notes that firms’ existing supervision, recordkeeping, and governance obligations apply to the use of AI.
- Complete capture can provide visibility and context that support oversight, retention, discovery, and audit readiness.
- Captured AI interaction data may reveal adoption trends, workflow efficiencies, customer friction points, and emerging business risks.
- Governed AI adoption is often strongest when organizations take a connected approach across AI, collaboration, voice, social, email, mobile/text, and web channels.
Generative AI is reshaping the communications landscape
AI is quickly becoming part of how regulated business gets done.
Employees are using generative AI to draft client communications, summarize meetings, generate research, automate operational tasks, and support customer engagement. These interactions create business context in much the same way email, messaging, voice, and collaboration platforms always have.
For regulated firms, AI is becoming less a separate category of technology and more a new layer of business communications data — and increasingly, one of the most valuable.
As Kamesh Tumsi, Chief Product Officer at Smarsh, explains:
“AI is not a separate governance problem. It is the newest expression of the same communications challenge regulated firms have always had — capturing the full record, preserving context, and making that data usable for oversight.”
That visibility can support more than supervision. It can also help organizations better understand how AI is influencing employee behavior, operational efficiency, customer experience, and business outcomes across the enterprise.
FINRA guidance reinforces that existing obligations still apply
Regulators are moving quickly to clarify how existing rules apply to generative AI.
FINRA Regulatory Notice 24-09 reinforces that FINRA rules remain technology-neutral and continue to apply when firms use generative AI, large language models, or similar technologies in the course of business.
The guidance makes clear that firms remain responsible for compliance obligations regardless of whether communications are generated by a person or a technology tool. FINRA also emphasizes that these obligations apply whether firms use internally developed AI systems or third-party platforms with embedded AI capabilities.
That includes obligations tied to:
- Communications supervision under FINRA Rule 2210
- Supervisory systems and procedures under FINRA Rule 3110
- Recordkeeping and retention
- Governance and oversight
FINRA also notes that communications standards apply regardless of whether content is generated by a person or a technology tool.
For regulated firms, this may include the need to retain AI-assisted communications in a manner that supports supervision, auditability, eDiscovery, and defensible reconstruction of business activity.
The challenge for many regulated organizations is that AI adoption is accelerating faster than traditional governance programs can adapt.
AI governance extends across internal and external workflows
Many firms begin AI governance efforts by focusing on internal employee usage. That is an important starting point, but it is only one dimension of the challenge.
Internally, AI interactions may include prompts, uploaded files, summaries, recommendations, workflow metadata, and operational research activities. Externally, firms are increasingly using AI to support customer communications, chatbot interactions, marketing content, reports, and service workflows.
As generative AI becomes embedded across enterprise applications and third-party platforms, organizations may also need visibility into how external AI services are influencing communications, workflows, and business decisions.
Together, these interactions form a growing body of communications data that may fall under supervision, retention, discovery, and audit obligations.
Organizations often benefit from visibility across both internal and customer-facing AI workflows, particularly as AI-generated content becomes more deeply embedded in everyday business operations.
Capture is what makes AI governable
AI governance begins with visibility.
Organizations may find it difficult to supervise, retain, investigate, or learn from communications they can’t capture.
AI interactions are rarely isolated events. Prompts evolve through iterative exchanges, uploaded files, referenced sources, and follow-on actions across multiple channels. Without full conversational context and metadata, organizations may struggle to reconstruct how decisions were made, how content was generated, or whether outputs complied with internal policies and regulatory expectations.
That is why complete capture is increasingly becoming central to governed AI adoption.
Capturing AI interactions in their original format, with full conversational context, can support a broader governance lifecycle:
- Capture interactions
- Preserve context
- Classify records
- Supervise communications
- Investigate activity
- Generate actionable intelligence
As AI workflows become more deeply integrated across collaboration and communications platforms, many firms are moving toward a more unified approach to communications data rather than isolated oversight across disconnected tools.
Smarsh helps organizations capture 100+ communication channels, while archiving, governing, and analyzing communications data to create a centralized intelligence layer for oversight, retention, analytics, and operational insight.
As organizations expand their use of AI-powered tools, Smarsh delivers compliant capture capabilities across leading enterprise AI platforms, including:
Claude Enterprise
Smarsh Capture for Claude Enterprise, using the Claude Compliance API, captures chats, files, projects, and activity events across Claude Enterprise chat.
Microsoft 365 Copilot
Smarsh Capture for Microsoft 365 Copilot preserves prompts, responses, attachments, and metadata across Microsoft 365, Teams, web, and Copilot agents while maintaining formatting and conversational context.
ChatGPT Enterprise
Smarsh Capture for the ChatGPT Enterprise Compliance API captures prompts, responses, text, images, and related interactions.
Governed AI benefits from a connected communications strategy
AI governance often extends beyond AI-only tools.
Employees move fluidly across channels throughout the workday. A customer interaction may begin in email, continue in Teams, involve an AI-generated summary, and end in a voice or mobile conversation.
When organizations capture only isolated AI interactions, the broader business context behind decisions, workflows, and customer experiences can become harder to understand.
A connected communications capture strategy can help organizations maintain a clearer line of sight across:
- Mobile/text
- Social
- Voice
- IM and collaboration
- Web communications
- Claude Enterprise
- Microsoft 365 Copilot
- ChatGPT Enterprise
The opportunity is not simply to retain records. It is to create a more connected understanding of how communications, decisions, and workflows move across the enterprise.
Captured AI data becomes actionable intelligence
The value of complete capture extends beyond compliance readiness.
Once AI interactions become part of the communications data layer, organizations can begin turning unstructured communications into actionable intelligence.
AI interactions may quickly become one of the richest sources of operational intelligence inside the enterprise, revealing how employees work, where friction emerges, how customer experiences evolve, and how institutional knowledge is created and shared.
Captured AI interaction data may help firms:
- Identify adoption trends
- Surface workflow inefficiencies
- Detect customer friction points
- Improve response consistency
- Understand productivity patterns
- Strengthen governance policy oversight
- Refine responsible AI best practices
This is where AI governance begins to evolve from a defensive exercise into a source of operational insight.
Organizations with visibility into AI-assisted workflows can better understand how work is changing across teams, where employees are finding value, and which practices may be worth scaling across the business.
As Kamesh Tumsi, Chief Product Officer at Smarsh, notes:
“Capture gives firms the record they need for compliance. Analytics gives them the intelligence to understand how generative AI is changing work, improving customer experiences, and shaping the practices that should scale.”
The greater risk is invisible AI adoption
Many firms still approach generative AI primarily through the lens of restriction and risk mitigation.
But efforts to block AI usage entirely can sometimes drive employees toward unsanctioned tools that operate outside approved governance frameworks, reducing visibility across the organization.
A more sustainable approach is governed enablement.
Organizations that support approved AI usage with capture, retention, analytics, and oversight gain more than control. They gain a clearer understanding of how AI is influencing communication patterns, workflows, customer experiences, and operational performance in real time.
The conversation is increasingly shifting away from whether employees are using AI and toward whether organizations can see that activity to guide it responsibly.
A practical AI governance framework for regulated firms
As AI adoption accelerates across regulated industries, organizations need governance strategies that balance innovation with oversight. Effective AI governance requires more than policy alone — it depends on the ability to capture, preserve, supervise, and analyze AI-driven communications and workflows in context.
A modern governance framework helps firms reduce compliance risk, support defensible recordkeeping, strengthen operational visibility, and scale AI use responsibly across the enterprise. Together, these pillars can support a governance model that balances oversight, operational learning, and AI innovation.
| Governance Pillar | Purpose |
|---|---|
| Policy | Define AI rules and ownership |
| Capture | Preserve AI interactions |
| Context | Retain metadata and context |
| Supervision | Monitor for risk and misuse |
| Retention | Support audits and discovery |
| Analytics | Track usage and workflow trends |
| Intelligence | Turn data into governance insight |
AI governance can create smarter business outcomes
The firms likely to lead in the next phase of AI adoption may not simply be the firms deploying the most tools. They may be the firms best able to learn from the communications data those tools generate.
Trusted AI adoption increasingly depends on visibility across the full communications ecosystem, but the longer-term opportunity extends beyond governance alone.
When organizations capture AI interactions in full context, they gain more than defensible records. They gain a clearer understanding of how work is evolving, where customer experiences improve, which workflows create friction, and how responsible AI practices can scale across the enterprise.
Put simply, AI governance starts with complete capture. But the next advantage belongs to the firms that can turn everyday communications into insight.
Smarsh is where AI meets regulatory readiness. Get started with Smarsh for Claude Enterprise now.
Frequently asked questions
AI governance in regulated financial services refers to the policies, oversight processes, and technologies organizations use to supervise, retain, monitor, and manage AI-generated communications and workflows. This includes capturing prompts, responses, metadata, and AI-assisted business communications to support compliance, supervision, and audit readiness.
Complete capture helps organizations preserve AI interactions in full context, including prompts, responses, files, and metadata. This visibility supports recordkeeping, supervision, eDiscovery, investigations, and compliance with regulations such as FINRA Rule 2210 and Rule 3110.
Organizations can govern generative AI adoption by implementing approved AI platforms with compliant capture, retention, supervision, and analytics capabilities. A connected communications governance strategy helps firms monitor AI usage across email, collaboration, voice, mobile, social, and AI platforms while maintaining operational visibility and regulatory oversight.
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