SIGNALBALL AI COMPLIANCE LAYER
Agentic Governance & Execution Control
The Signalball AI Compliance Layer is the governance, observability, command & control, token-cost and RBAC layer for every AI agent, tool call and workflow your institution ships to production. A controlled execution boundary sits between your AI applications and your enterprise systems — and every agentic decision passes through it.
Diagram: an AI agent requests an action — exporting customer data. The request passes through the Signalball Compliance Layer, a controlled execution boundary enforcing policy checks, role-based access control and token budgets. Three outcomes are possible: executed within policy, held for human approval, or blocked outside policy. Every decision is written to a SOC-ready audit trail.
In plain terms
A supervisor for every AI agent you put into production
Companies are deploying AI agents that answer customers, move data and trigger real operations — often with no limits on what they can do, no record of what they did, and no way to stop them instantly. For a regulated business, that is an unacceptable risk.
The AI Compliance Layer is the control room for your AI workforce: every agent gets defined permissions, sensitive actions require human approval, every decision is written to an audit trail, spending is capped by budget — and a kill switch can stop any agent instantly. You get the productivity of AI with the accountability regulators expect.
Six capabilities — six places agents go wrong without control
Agent Policy Control
Define what each AI agent can access, execute, approve, escalate or reject — reducing unauthorized or unsafe behavior.
Tool Usage Governance
Control APIs, plugins, files, databases and external services per agent — preventing uncontrolled execution and data exposure.
Workflow Tracing
Track multi-step automation, handoffs, retries, failures and agent-to-agent activity for full auditability.
Human Approval Workflows
Require approval before sensitive actions: payments, external messages, exports or production changes.
Audit Trail
Capture prompt summaries, model responses, tool calls, user actions, decisions and system events.
Cost Controls
Track and limit LLM usage by user, agent, app, workflow, model or department — preventing runaway automation cost.
Five steps to ship an accountable agent
Step 01
Register Agent
Add every AI agent, workflow, automation task and tool to the registry.
Step 02
Attach Policy
Assign permissions, model limits, allowed tools, escalation rules and approval gates.
Step 03
Trace Execution
Capture prompts, responses, tool calls, token usage, latency and workflow status.
Step 04
Control Actions
Allow, deny, pause, resume or stop actions based on role, policy and risk level.
Step 05
Report & Optimize
Analyze performance, cost, compliance, exceptions and optimization opportunities.
Three operating modes, one control plane
Observability
Trace, debug, measure, audit — agent steps, workflow paths, tool calls and system events with latency, success rate, token usage and full audit trails.
Command
Registry, live status, kill switch — pause, resume, escalate or stop any agent, workflow, tool or automation instantly, with action-level controls per role.
Cost
Usage, budgets, models, alerts — track tokens by user, agent, app, workflow, model or department; set budgets and trigger alerts on anomalies.
Where the AI Compliance Layer delivers value
Wherever AI agents touch money, customers or regulated data, they need provable control.
Banking
Prove to auditors and regulators that every AI decision is bounded, approved and traceable — with human sign-off enforced on payments, customer communications and data exports.
Fintech & Wallets
Ship AI features fast without losing control: policies and token budgets keep experimentation safe, and runaway automation costs are stopped before they happen.
Gaming & iGaming
Govern the AI agents that touch player data, purchases and moderation decisions — with full audit trails ready for licensing bodies and dispute handling.
Brokers & Trading
Keep AI tools operating near trading systems inside strict guardrails: approval gates fire before orders, data exports or client communications ever leave the boundary.
Telecom
Run AI at call-center and network-operations scale while keeping every agent inside policy — observable, budget-controlled and stoppable in one click.
