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FSI
Katara for financial services teams that need trusted AI assistance, approved actions, and clear records of how AI is used.
When a regulator asks what your AI touched, you need an answer — not a reconstruction.
Financial services teams operate under decades of accumulated control: information barriers between research and deal teams, access tiers between front office and compliance, audit trails regulators can request on demand. AI assistance didn't arrive with any of that built in. Katara sits between your teams, your systems, and the models they use — so the controls you already live under apply to AI the same way they apply to everyone else.
The gap AI opened
Information barriers were built for people. Most AI wasn't.
Your research analysts and your deal teams have never been allowed to see each other's data. Your retail advisors don't get institutional trading commentary. These boundaries exist because a regulator requires them, not because anyone trusts the honor system. The AI assistant your team adopted this year doesn't know any of these walls exist — it answers from whatever it can reach, and in most deployments, that's everything.
This isn't a hypothetical gap. A 2026 survey of 307 CTOs, CIOs, and CISOs found that 91% rank security and data access controls as their top priority for governing AI-powered tools — by a wide margin over every other concern, including reliability and cost. Only 5% said they were highly confident they had full visibility into what was actually running against their data. And only 7% believed access controls should be configured inside each individual AI tool, app, or workflow — the rest want it enforced underneath, in one place, regardless of what built the tool or who's using it.
Katara is that place.
The Katara Governance Framework for FSI
Three components. One enforcement point. Built for the controls you already answer to.
AI Knowledge Base — trusted retrieval, enforced at the boundary
Equity research can't cite a deal team's confidential model. A retail advisor can't surface institutional desk commentary. Katara enforces that separation at the moment of retrieval — the same query returns different answers depending on who's asking, because the access rule is applied before the model ever sees the data. This is information barrier enforcement, not a content filter applied after the fact.
AI Gateway — every model call, checked against policy
Every request to a model — whatever the model, whatever the workflow — passes through a single governed layer for sensitive-data checks, cost controls, and access rules. You get one place that knows what was asked, by whom, against what, and whether it was permitted. That answer doesn't depend on which tool, vendor, or internally-built app made the request.
MCP Registry — only the actions you've authorized
Define exactly what an AI agent is allowed to do, not just what it's allowed to see. A credit-decisioning assistant can query a scoring model but cannot initiate a transfer. A client-service agent can pull account history but cannot modify a record. Automation only reaches the systems and actions you've explicitly approved — nothing is reachable by default.
Built for the regulatory environment you already operate in
Katara doesn't ask you to translate your obligations into a new framework. It maps to the ones you already report against:
- DORA (Articles 28–30): ICT third-party risk requires a register of providers and continuous monitoring of how those systems are used. Katara's audit log is built to produce that record — what was accessed, by which system, under whose authorization — in a format ready for ICT risk reporting, including for Katara itself as a third-party provider in your own register.
- GDPR (Article 22): Automated decisions affecting customers — credit, account actions, risk flags — require a documented basis and the ability to explain the outcome. Katara's chunk-level access log ties every AI-assisted output back to the specific data it drew on.
- EU AI Act (Annex III, high-risk classification): Credit scoring, creditworthiness assessment, and other classified use cases require risk management documentation, human oversight provisions, and an audit trail available on request. Katara provides the underlying access and logging substrate this documentation depends on — and that's the substrate, not a substitute for the legal classification work itself.
- MiFID II / internal information barriers: Where regulatory or internal policy requires separation between functions — research, advisory, trading, deal teams — Katara enforces that separation at the data layer for AI systems the same way it has always been enforced for people.
Where a claim requires legal interpretation specific to your institution and jurisdiction, that's a conversation for your compliance and legal teams — Katara gives them the evidence to have it.
Three buyers, one answer
CISOs and DPOs: You're not just defending decisions after the fact — you see what's running before it becomes the thing you find out about too late.
CTOs and engineering leads: Governance that sits underneath what you've already built, without re-architecting it. AI tools get added; the control plane doesn't change.
Business and desk leaders: Stop waiting for IT and compliance to agree before a team can use AI at all. Katara is the reason the answer can be yes.
The institutions that move fastest on AI in the next two years won't be the ones with the least oversight. They'll be the ones who can prove, on demand, exactly what their systems did and why.