White paper
Weighing the Build vs. Buy Decision for AI Surveillance
This white paper helps compliance and risk leaders evaluate whether to build or buy AI-driven communications surveillance.
Generative AI has pushed some financial institutions to consider building proprietary surveillance models instead of buying an established platform. Cost overruns, model governance gaps, and multi-jurisdictional data rules can turn a promising build into a long-term liability.
Compliance, risk, and technology leaders can explore the five risks of building in-house surveillance and where purpose-built platforms may deliver a more scalable and defensible path.
What you'll learn
In this white paper, you'll learn how to:
- Identify the five risks firms take on when building proprietary surveillance models
- Compare the long-term costs of building versus buying, backed by Gartner research
- Understand what regulators expect under FINRA Rule 3110, SEC Rule 17a-4, MiFID II, and the EU AI Act
- Assess how your organization can sustain model governance and regulatory documentation over the long term
- Evaluate when a hybrid buy-and-build approach offers greater flexibility without increasing risk
Why this matters
Cost, governance, and operational risk tend to compound over time, not surface all at once.
- Generative AI project costs often exceed initial budgets at production scale
- Regulators expect surveillance systems to be explainable and auditable
- Training data across the U.S., EU, and U.K. raises separate data governance obligations
- Key-person dependency can put an entire surveillance program at risk
- Purpose-built platforms can help reduce operational burden while supporting evolving regulatory expectations
Know the full cost before you build
Download the white paper for a practical framework to evaluate cost, governance, scalability, and regulatory risk before deciding whether to build or buy.
