Purpose-built deployments and governance frameworks for Multi-Family Offices, Registered Investment Advisors, and Private Banks.
Full production deployment of large language models and retrieval systems inside your controlled perimeter. VPC, on-premises, or air-gapped configurations with no client data leaving your environment.
Complete governance programs including model documentation, audit trails, data lineage, supervisory procedures, and evidence packages aligned with current FINRA, SEC, and FFIEC expectations.
Detection and response systems tailored to your specific data flows, hardened against AI-driven attack techniques including prompt injection, model extraction, and synthetic media threats.
Structured adversarial testing against realistic threat scenarios relevant to wealth management. Findings are delivered with clear remediation guidance suitable for risk and compliance committees.
Custom agents and automated processes for onboarding support, portfolio review, compliance monitoring, and internal research — all operating within your security boundary with appropriate human oversight and complete audit trails.
Briefings and supporting materials developed specifically for investment committees, risk oversight groups, and boards. Technical realities translated into the language required for fiduciary decision-making.
A significant portion of wealth management organizations continue to treat generative AI primarily as a productivity experiment. Regulators and sophisticated clients increasingly view it as a source of material risk that requires documented controls, supervision, and demonstrable resilience.
For organizations serving ultra-high-net-worth families, the reputational and legal consequences of client information entering public training pipelines or uncontrolled third-party systems are substantial. Leading institutions now require clear data residency and provenance controls as a baseline expectation.
FINRA’s 2026 guidance confirms that supervision, recordkeeping, communications, and fair dealing obligations extend to generative AI usage. Firms are expected to maintain inventories of AI applications, written policies, and evidence of appropriate oversight — including oversight of vendors and service providers.
Deepfake-enabled social engineering, automated reconnaissance of AI systems, and prompt-based attacks have moved from theoretical to observed. Organizations that implemented AI capabilities without corresponding investment in detection and hardening now carry material unrecognized exposure.
Our methodology is built for environments in which failure carries regulatory, reputational, and fiduciary consequences. We proceed deliberately, document rigorously, and remain accountable following deployment.