CFOs want AI copilots to answer questions like “What are the top 10 drivers of our quarterly spend variance?” But unrestricted access risks exposing payroll, sensitive cost centers, or regional ledgers.
Finance leaders gain real-time AI insights into cash flow and spend analysis, without risking exposure of sensitive financial records.
Finance and Treasury teams depend on AI apps and agents to analyze transactions, monitor liquidity, and automate reporting. But without guardrails, AI queries can expose sensitive GL accounts, reveal regional cost centers, or let junior staff and automation agents see data they’re not authorized to touch.
With LLMac, your enterprise defines access policies once — and they are enforced everywhere. Finance teams get the insights they need, Treasury leaders stay within their scope, and automation agents run only within approved boundaries.
At the same time, Audit and Compliance teams gain a real-time assurance layer. Every access — whether by a user in an AI app or an autonomous agent — is logged with full traceability. This gives regulators (SEC, FINRA, SOX, GDPR) the evidence they demand and helps internal audit prove that data separation of duties is continuously enforced.
The result: Finance moves faster with AI, while Audit and Compliance sleep at night knowing every query is policy-compliant and fully observable.
Below you’ll see LLMac applied to a Treasury dataset (GL transactions). The same 6-step flow applies to portfolio teams, compliance, audit, and more.
IT and data governance teams connect the Finance and Treasury databases. LLMac automatically detects key fields like department, cost_center, metadata.region, and gl_account.name. This makes it effortless to enforce policies that align with the organization’s structure — ensuring Finance and Treasury users only work with the data they’re cleared to see.
LLMac lets enterprises set rules that mirror their org structure. Team members using enterprise AI apps — and the AI agents working alongside them — each get precise access controls, ensuring sensitive data is only available where it should be. This is powered by flexible modes — strict, partial, hybrid, or custom — with controls down to the field level.
Assign both team members and AI agents to the right groups in minutes. Finance, Legal, Audit, or regional teams can be mapped through HR systems, identity providers, or CSV imports — while AI agents are automatically aligned to the same rules. This ensures policies follow everyone and every agent at runtime, without manual upkeep.
Your vector database becomes policy-aware. LLMac attaches metadata like owner, department, or region to every record — so whether the query comes from a user in an AI app or an AI agent, policies are enforced automatically at query time. Works across Qdrant, Pinecone, Weaviate, FAISS, and more.
Every query — from team members using enterprise AI apps or from AI agents — is checked in real time before results are returned. Only data that complies with your enterprise rules is ever delivered. Enforcement happens at runtime, with filters injected directly into the vector search.
Every access — by users of enterprise AI apps and by AI agents — is fully traceable. You’ll know who queried, what was retrieved, and why. Logs export to Splunk, Datadog, or your SIEM for continuous monitoring — giving you the compliance proof regulators and auditors demand.
Let’s chat about your data, your teams, and the AI apps or agents you want to deploy. We’ll show you how LLMac adapts to your exact needs.