By Martina Rejsjö, Head of Product Strategy at Eventus
What was once an experiment or an edge case is now an expectation – artificial intelligence is rapidly becoming a core capability in trade surveillance and compliance, reshaping how firms detect, investigate and mitigate risk. The drivers are clear: improving efficiency, reallocating human talent away from monotonous, low-value tasks and reducing costs. There’s also the competitive advantage that comes from being seen as a technology-forward and innovative organization.
Early adopters are already demonstrating these benefits, using AI to accelerate surveillance alert resolution, reduce false positives and surface patterns that traditional methods often overlook. But these gains come with a caveat. Without the right foundation, AI can just as easily lead to blind spots, fragmented practices and regulatory scrutiny. The question is no longer whether AI has a role to play, but whether organizations are prepared to adopt it responsibly, sustainably and defensibly. To answer that, firms must take stock to ensure their AI adoption roadmap will drive meaningful progress.
Start Small, Build Responsibly
The safest path begins with tightly scoped use cases for AI. In surveillance, one logical starting point is applying AI to post-alert triage rather than altering thresholds within the program itself. By keeping detection parameters intact, firms can maintain auditability and avoid difficult questions from regulators while still achieving measurable efficiencies in how low-value alerts are processed. The results can be quantified through time saved, noise reduced and consistency of outcomes when compared to human reviews. These early wins create both data and confidence to support more ambitious deployments.
A Foundation of Data Quality
No aspect of AI readiness is more critical than the state of an organization’s data. Surveillance systems depend on comprehensive, normalized and well-governed inputs to generate accurate insights. Inconsistent data leads to incomplete or misleading results, creating an illusion of control while leaving real risks undetected.
Data readiness is multi-dimensional. Quality and completeness must be enforced to ensure that all relevant transactions, orders and events flow into the system. Normalization is essential so that identifiers, timestamps and asset classes are consistently represented across disparate feeds. Governance provides the final layer of assurance: ownership structures, version control and lineage tracking are needed so that every model decision can be traced back to its inputs and transformations. Without these foundations, AI adoption is not only ineffective but dangerous, offering a false sense of security that undermines the credibility of the entire compliance program.
Guarding Against AI Fragmentation
Even without an official AI adoption program in place, employees are increasingly experimenting with general use generative AI tools in their daily workflows. While well-intentioned, this creates the risk of AI fragmentation: isolated instances of unsanctioned use that expose sensitive data, disrupt standard operating procedures and dilute supervisory controls. The result is an inconsistent and potentially vulnerable compliance environment.
To avoid fragmentation, leadership must set a clear strategy from the top. That means defining which tools are permissible, the contexts where they may be applied and the safeguards that must be observed. Without this clarity, the efficiency gains offered by AI risk being outweighed by operational and regulatory vulnerabilities.
Humans in the Loop
AI in compliance should be understood as a co-pilot, not a replacement. Human oversight remains indispensable, particularly in validating outputs and exercising judgment in complex or ambiguous cases. At the same time, the skillset of compliance professionals must evolve to meet the moment. Analysts need to develop greater fluency in working with data, from interpreting model outputs to understanding the assumptions behind them. They must also adapt to new ways of interacting with systems, learning how to frame queries and prompts precisely to generate relevant, reliable results.
Equally important is the need to rigorously review AI-generated insights. Outputs must be tested, validated and challenged, not accepted at face value. By strengthening both their technical literacy and critical thinking, compliance teams can remain central to the surveillance process and protect its integrity while allowing AI to handle the repetitive, non-additive tasks.
The Eventus Perspective: Secure, Practical Applications Powered by Frank AI
At Eventus, AI readiness is not an abstract concept but a practical design principle. Within the Validus platform, analysts have transparent access to underlying data, enabling them to construct their own queries and automations on a governed foundation. Frank, our new natural-language AI tool, extends that capability by enabling users to interrogate data directly, uncover deeper context, accelerate investigations and explain all the logic involved – without sacrificing traceability or control.
Security is central to this approach. With Frank, client data never leaves its environment. Queries are executed locally, ensuring sensitive information remains protected while still enabling powerful analytics. Accessible yet impactful use cases like these help firms advance their compliance capabilities incrementally, with confidence that every step remains defensible.
Defining True AI Readiness
Regulators are becoming more receptive to measured, explainable uses of AI in compliance, but the burden of proof lies with the firms deploying it. Those with robust data governance, consistent processes and strong human oversight will be best positioned to demonstrate not only that their systems work, but that they can be trusted. Conversely, organizations that adopt hastily, without addressing data quality issues or guarding against fragmented use, risk undermining both performance and credibility.
True AI readiness is about more than having the right tools. It requires cultural alignment, technical discipline and governance structures that reinforce accountability. Firms that start small, prioritize their data, establish clear guardrails and invest in upskilling their teams will be best prepared to translate the promise of AI into lasting proof of its value — to regulators, clients and the market as a whole.
Learn more about how your firm can take control of your trade surveillance with Validus, now featuring Frank AI.