Governing AI and human actions across the tools your teams already use
- Claude
- ChatGPT
- Slack
- Microsoft Teams
- Microsoft 365
- Gmail
- GitHub
- GitLab
What it does
- Regulatory Response — Continuous, cited evidence for FCRA, ECOA, SR 11-7, DORA and MAS requirements — with the gaps named rather than written around.
- Evidence — Reconstruct any credit decision or trading recommendation: the inputs, the rule that applied, and the person who owned it.
- One rule set — Model risk, lending and resilience stop answering differently. Every run adds what it settled back to the same policy.
What gets in the way
Fair lending compliance across systems
Automated credit decisions — from legacy scoring to AI models — face intense scrutiny under ECOA and FCRA. You need to prove your systems don't discriminate — continuously, not just at validation.
Model risk management gaps
SR 11-7 requires robust model governance, but traditional MRM frameworks weren't built for systems that learn and adapt. The gap between policy and practice is growing.
Regulatory exam readiness
When examiners ask how a decision was made — by any system — you need an answer in minutes, not weeks. Current documentation processes can't keep up.
Cross-border compliance complexity
Operating across jurisdictions means navigating DORA, MAS, Basel, NYDFS, and more — simultaneously. Each has different technology governance expectations.
Start with one service.
A 30-minute scoping session with your risk owner and your IT contact.