Jupiter AI

Trust Your AI Before It Reaches Production.

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Jupiter AI — Trust Your AI Before It Reaches Production

💡 Inspiration

Engineers solved LLM indeterminism. The actual blocker is governance.

PERCEIVED BARRIER              REAL BARRIER
──────────────────             ──────────────────────────────
Hallucinations          →      Data leaks & policy violations
Formatting errors       →      No data lineage or access limits
Response variability    →      Audits are manual & retrospective
                        →      Zero real-time enforcement

AI agents now have read/write access to everything:

                    ┌──────────────────┐
                    │   AI Agent / RAG │
                    └────────┬─────────┘
                             │
          ┌──────────────────┼──────────────────┐
          ▼                  ▼                  ▼
   ┌─────────────┐   ┌──────────────┐   ┌─────────────┐
   │ Customer PII│   │  Financial   │   │  Internal   │
   │  & PHI      │   │  Records &   │   │  Policies & │
   │             │   │  Payments    │   │  IP / Docs  │
   └─────────────┘   └──────────────┘   └─────────────┘
          ▼                  ▼                  ▼
   ┌─────────────┐   ┌──────────────┐   ┌─────────────┐
   │  CRM / ERP  │   │  Gov Data    │   │  Proprietary│
   │  Platforms  │   │  Sources     │   │  Documents  │
   └─────────────┘   └──────────────┘   └─────────────┘

Yet governance audits remain manual, slow, and reactive — teams inspect < 1% of agent traces, and only after a breach.

⚙️ What It Does

Jupiter autonomously discovers, models, tests, and audits enterprise AI systems.

CONNECT AI SYSTEM
       │
       ▼
┌─────────────────────┐
│  1. DISCOVERY       │  Conversational agent interviews stakeholders
│  Purpose · Models   │  Extracts: architecture, tools, APIs, data
│  Tools · APIs · Data│
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  2. KNOWLEDGE GRAPH │  Maps system entities → governance ontology
│  Risks · Controls   │  CRM Tool → CRM Type · Email → PII Asset
│  Regs · Policies    │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  3. DIGITAL TWIN    │  Governance-aware model of the AI system
│  Tools · Assets     │  Covers: data flows, controls, dependencies
│  Flows · Risks      │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  4. POLICY PARSING  │  Converts policies → machine-testable rules
│  Policy → Control   │
│  → Asset → Risk     │  "No customer data disclosure"
└────────┬────────────┘  → Control · Protected Asset · Risk
         │
         ▼
┌─────────────────────┐
│  5. TEST GENERATION │  Every test traced to a policy
│  Policy-driven,     │  Policy → Control → Risk → Objective → Prompt
│  not generic        │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  6. TEST EXECUTION  │  Systematic, repeated adversarial testing
│  PII Leakage        │
│  Prompt Injection   │
│  Tool Abuse         │
│  Data Exfiltration  │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  7. OBSERVABILITY   │  Integrates with platforms (e.g. Arize Phoenix)
│  Tool calls         │  Captures: traces, outputs, docs, metadata
│  Reasoning traces   │
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  8. ANALYSIS        │  Root cause for every finding
│  Which policy failed│  Which tool exposed · Which doc leaked
│  Which control broke│
└────────┬────────────┘
         │
         ▼
┌─────────────────────┐
│  9. REPORT          │  Auditor-ready, evidence-backed output
│  Risk · Violations  │  Root cause + remediation included
│  Controls · Fixes   │
└─────────────────────┘

🏗️ How We Built It

┌─────────────────────────────────────────────────────────────┐
│                      JUPITER STACK                          │
├──────────────────┬──────────────────┬───────────────────────┤
│  DISCOVERY LAYER │  REASONING LAYER │   EXECUTION LAYER     │
│                  │                  │                       │
│  Conversational  │  Governance      │  Test Runner          │
│  Discovery Agent │  Knowledge Graph │  Adversarial Engine   │
│                  │                  │                       │
│  Stakeholder     │  Digital Twin    │  Arize Phoenix        │
│  Interview Flow  │  Generator       │  Observability        │
└──────────────────┴──────────────────┴───────────────────────┘
                             │
                             ▼
                   ┌──────────────────┐
                   │  REPORT ENGINE   │
                   │  Policy → Finding│
                   │  → Evidence      │
                   │  → Remediation   │
                   └──────────────────┘

Key design choices:

  • Discovery-first before any test generation — no generic scans
  • Ontology-based mapping for cross-customer governance reasoning
  • Every test traceable: Policy → Control → Risk → Prompt
  • Integrated observability for why, not just what

🚧 Challenges We Ran Into

CHALLENGE                          HOW WE NAVIGATED IT
──────────────────────────────     ──────────────────────────────────────
Modeling diverse AI architectures  Built flexible ontology with generic
across customers                   entity types that map to any system

Converting policy language into    Structured Policy → Control → Asset
machine-executable tests           → Risk pipeline with LLM parsing

Avoiding false positives in        Governance-aware context filtering
governance violation detection     tied to digital twin, not raw output

Keeping audit tests relevant as    Continuous discovery refresh tied
AI systems evolve rapidly          to system change signals

🏆 Accomplishments We're Proud Of

┌──────────────────────────────────────────────────────────┐
│  ✅  Built end-to-end: Discovery → Twin → Test → Report  │
│  ✅  Policies auto-converted into executable test cases   │
│  ✅  Every finding linked to Policy → Control → Evidence  │
│  ✅  Observability integration capturing full traces      │
│  ✅  Governance reports ready for real compliance reviews  │
└──────────────────────────────────────────────────────────┘

Transformed governance from a static checklist into a living, continuous audit layer.

📚 What We Learned

ASSUMPTION                         REALITY
────────────────────────────       ──────────────────────────────────
Governance = security scanning  →  Governance = reasoning over policy,
                                   risk, assets, and controls together

Generic red team prompts work   →  Tests must be traceable to policy
                                   or findings are meaningless

Observability = monitoring      →  Observability must answer *why*,
                                   not just log *what* happened

One-time audit is sufficient    →  AI systems evolve daily; governance
                                   must be continuous, not periodic

🚀 What's Next for Jupiter AI

┌────────────────────────────────────────────────────────────────┐
│                     ROADMAP                                    │
├────────────────────┬───────────────────────────────────────────┤
│  NEAR TERM         │  Expand regulation coverage               │
│                    │  GDPR · HIPAA · SOC2 · EU AI Act          │
├────────────────────┼───────────────────────────────────────────┤
│  MID TERM          │  Real-time guardrail enforcement           │
│                    │  Block violations before they complete     │
├────────────────────┼───────────────────────────────────────────┤
│  LONG TERM         │  Governance OS for enterprise AI           │
│                    │  Continuous assurance across all agents    │
└────────────────────┴───────────────────────────────────────────┘

Not replacing governance teams — giving them an autonomous auditor that continuously understands, tests, and validates AI at scale.

NOTE: Currently It is fune tuned for fintech agents only.

Jupiter AI — From AI Observability to Autonomous AI Governance

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