Next month, data compliance regulations move from policy to technical reality for wealth management firms.
Institutions face a series of converging deadlines, creating a compliance cliff for many.
SEC Regulation S-P takes effect for smaller advisers in early June, followed weeks later by Colorado’s new AI discrimination law. At the same time, the Treasury’s AI Risk Management Framework is beginning to formalize how firms are expected to govern and monitor AI.
These regulations assume firms can pull together cohesive records, detect and trace activity, and demonstrate how information is used across systems.
Most aren’t set up to do that today, as their underlying infrastructure wasn’t designed to support such a high level of visibility or coordination.
In this article, we’ll look at what’s changing in 2026, where firms are most exposed to data compliance risk, and what it takes to support these requirements in practice.
A New Era of Data Compliance Enforcement
The new regulations reflect a broader shift in how data compliance is enforced.
For years, compliance was largely driven by policies, documentation, and periodic reviews. Firms defined controls, documented their processes, and demonstrated that they had procedures in place.
Regulators are now evaluating whether firms can actually execute those controls across their systems: detect issues, trace activity, and demonstrate how data is used in day-to-day operations.
The convergence of Regulation S-P and Colorado’s AI mandate introduces data compliance requirements that depend on how data is handled across systems, not just how it’s documented.
Regulators are also starting to scrutinize the platforms firms rely on to manage and govern data as part of their compliance assessment.
In other words, enforcement is headed toward systems, not just processes.
In many institutions, the data needed to support these requirements is not integrated across different parts of the organization, making it difficult—sometimes impossible—to produce a complete and reliable view when required.
Similar expectations are already in place internationally.
Data compliance regulations like the EU’s Digital Operational Resilience Act (DORA) and the proposed Financial Data Access (FIDA) framework require firms to demonstrate operational resilience and coordinated data access across systems.
For institutions with global operations—or global technology stacks—those standards are already relevant, and are likely to influence U.S. data compliance regulations over time.
SEC Regulation S-P for Smaller Investment Advisers
The amended SEC Regulation S-P introduces more specific, time-bound requirements around how wealth management firms detect and respond to unauthorized access to customer information.
For smaller registered investment advisers—those with less than $1.5 billion in assets under management—the compliance deadline is June 3, 2026.
At a high level, the rule requires these firms to:
- Notify affected individuals within 30 days of discovering unauthorized access or use of customer information.
- Establish and maintain an incident response program to detect, assess, and respond to breaches.
- Maintain ongoing oversight of third-party vendors with access to customer data, including monitoring and due diligence.
In many institutions, customer data is distributed across multiple platforms, business lines, and vendors. When something goes wrong, determining what happened, what data was affected, who had access to it, and who needs to be notified means pulling information from multiple systems, often manually, and reconciling it.
That work takes time, and often painstaking attention to detail. What’s more, without a unified client record, there is no single, consistent way to pull that information together, or to assemble a complete, reliable account of a breach across systems within the required 30-day window.
That puts many firms at risk of falling out of data compliance—and facing increased regulatory scrutiny or fines—when they can’t produce a cohesive record of events and impact within the required timeframe.
Colorado SB 24-205 and the Emerging U.S. AI Standard
Colorado’s AI law, which takes effect June 30, 2026, introduces a different set of requirements, focused on how firms use AI systems in consequential financial decisions.
The law requires firms to exercise reasonable care to prevent algorithmic discrimination in areas like lending and wealth advice.
It also introduces new expectations around transparency and accountability. Firms must:
- Conduct annual impact assessments for high-risk AI systems, including disclosures around data governance and training data.
- Notify consumers when an AI system is a substantial factor in a financial outcome.
- Provide an explanation of how that system influenced the decision.
Meeting those requirements depends on a firm’s ability to show how an AI system was used in a specific case: what data informed it, how the system was applied, and how the outcome was reached.
For many, that level of detail is not easy to produce, because the data required is often pulled from different sources that are not always tied together.
Without clear data provenance, firms cannot show how a decision was made or support that it meets the standard of reasonable care the law introduces.
When asked to justify an AI-driven decision, firms often have to piece together the underlying data before they can show how the outcome was reached. That makes it challenging to comply with data compliance regulations like Colorado’s AI law.
While the law applies to Colorado specifically, it has farther-reaching implications. National firms, or those using shared technology platforms, are already preparing to meet similar data compliance expectations both domestically and globally.
Navigating Data Compliance in 2026
The shift in data compliance isn’t limited to a handful of new regulations.
A broader set of rules, frameworks, and examination priorities are reinforcing the same expectations: greater visibility into data, tighter control over how it’s used, and clearer accountability for outcomes.
A few are worth noting.
Financial Services AI Risk Management Framework (FS AI RMF)
The Treasury’s AI Risk Management Framework, released in March 2026, outlines a detailed set of controls for governing AI in financial services.
While voluntary for now, it points to where expectations are heading.
The framework strongly emphasizes data provenance, which means understanding where training data originates, how it’s used, and how outputs can be traced back to underlying inputs.
That level of visibility depends on having a clear understanding of how data moves through the organization. It shifts the focus from model performance alone to whether wealth management firms can account for the data behind it.
SEC’s 2026 Focus on the Marketing Rule
The SEC’s 2026 examination priorities place increased scrutiny on how firms represent their capabilities, particularly when it comes to AI and performance.
Inconsistent disclosures across client portals, reports, and marketing materials—for example, performance figures or AI-driven insights presented differently in each—are a common trigger for review.
When different systems surface different versions of the same data, those inconsistencies are harder to catch and fix before they show up in a review.
Maintaining a single, consistent source of truth for client-facing data helps reduce that risk and ensures disclosures hold up when reviewed.
International Benchmarks: DORA and FIDA
International data compliance regulations are pushing in a similar direction.
The EU’s Digital Operational Resilience Act (DORA) focuses on system reliability and recovery, requiring firms to maintain operations through ICT disruptions.
The proposed Financial Data Access (FIDA) framework centers on client control, giving individuals more visibility into and authority over how their financial data is accessed and shared.
While these are not U.S. data compliance regulations, they reflect a broader shift toward more open, connected financial data environments, where data can move securely, be accessed in real time, and remain under clear control.
For firms operating across borders—or relying on global technology platforms—these expectations are already shaping how data environments are designed and managed.
That raises the bar for how systems handle availability, access, and data movement—not just how they store information.
What Data Compliance Regulations Now Demand from Your Infrastructure
The data compliance regulations taking shape in 2026 all point in the same direction, even if they approach it from different angles.
Some emphasize response time. Others focus on explainability, disclosure, or operational resilience. But they all assume the same underlying capability: that firms can access, connect, and account for their data in a consistent way institution-wide.
When it comes to meeting these technical requirements, many institutions are still catching up.
For years, compliance has been treated as an add-on, with institutions implementing policies, procedures, and documentation built around existing systems. That way of thinking is becoming less sustainable as regulatory expectations move closer to how data is actually stored, used, and shared.
Adhering to the new data compliance regulations isn’t just about updating policies or adding new controls.
It’s about making sure the underlying infrastructure can support them—so data is connected, traceable, and usable when institutions have to justify decisions or explain outcomes.
That’s the role of a connected intelligence platform. By creating a unified, governed layer on top of existing systems, it allows firms to work from the same set of data—whether they’re responding to a regulator, explaining a decision, or ensuring consistency across client-facing materials.
For firms that want to understand how their current environment stacks up when it comes to meeting new data compliance regulations, more detail is available at Wealth Access, including the option to take a closer look at how a connected data layer can work within your existing technology stack.
