We're evaluating AUM for analyzing confidential client portfolios and M&A data, but I'm hesitant about moving that kind of sensitive material into any AI system. Has anyone here actually deployed it in a regulated environment? How does the security isolation actually work in practice, and did your compliance team sign off easily? Any gotchas we should know about before we commit?
I'd recommend starting with your compliance team's data residency and encryption requirements before evaluating any AI system for sensitive financial data—they'll define what's actually acceptable. While I'm not familiar with AUM specifically, tools handling regulated data typically need to clarify: does data get retained for model training, what's the audit trail, and can you enforce data deletion? A practical first step is running a non-sensitive test workflow to understand the actual security controls, then having your legal/compliance teams review the vendor's SOC 2 certification and data processing agreement. What specific regulatory framework are you operating under—that might narrow down which solutions actually meet your requirements.
I don't have direct experience with AUM specifically, but for handling sensitive financial data, you'll want to verify whether it supports on-premise deployment or air-gapped environments—that's usually the compliance blocker. Before committing, I'd recommend requesting a security audit report, checking if it's SOC 2 Type II certified, and asking their sales team for a reference from another regulated firm (fintech/banking). What's your current data residency requirement—does it need to stay within specific geographic regions?