Enterprise intelligence · AI governanceInteractive demo · private local
Risk Intelligence
An enterprise operating-intelligence workspace that connects AI-agent risk, economics, authority, and evidence so leaders can decide what to keep, optimize, restrict, or escalate.
Outcome / decision impact
Turned fragmented AI ownership, spend, permissions, and risk into a common decision surface for comparing operating envelopes before people authorize change.
Deterministic risk budgetsHuman approval for every production changeAgent-to-data lineage
My contributionProduct strategy, risk-intelligence architecture, deterministic decision-model design, and governance boundaries.
01 · Problem
The decision was obscured by fragmented evidence.
Enterprise AI agents accumulate fragmented ownership, permissions, model spend, and operational risk. Risk Intelligence creates a decision-ready view of each agent’s operating envelope and the evidence behind recommended changes.02 · System
Designed as a decision surface—not a black box.
- Deterministic blast-radius, residual-risk, risk-budget, and model-fit analysis
- Agent inventory with ownership, autonomy, permissions, spend, outcomes, and recommendation states
- Enterprise graph connecting agents, models, tools, and sensitive data
- Evidence-backed counterfactual operating envelopes with human approval required for every production change
03 · Trust boundary
What this is—and what it is not.
Role: product strategy, risk-intelligence architecture, deterministic decision-model design, governance and safety boundaries, and enterprise experience design. The public Northstar workspace is fictional, deterministic, and makes no live AI calls. Recommendations are advisory; humans authorize and execute every production change.