Case Studies

How autonomous AI systems achieve cryptographic compliance in highly regulated domains.

Compliance is proven not by documentation, but by system behavior under adversarial testing.

Featured Case Studies

Real deployments across 6 regulated industries. Each demonstrates how cryptographic identity + behavioral monitoring solve regulatory approval.

Financial Services

Quantitative Trading Agents
€50M daily FX trading. 10x volume, 84% cost cut, 56x faster audits.
MiCA Art. 61–75 · Auto-revocation · Real-time risk

Financial Crime

AML & Fraud-Detection Agents
Agent compromise detected + revoked ≤60ms. Zero data loss.
Kill switch · Immutable logs · Regulatory proof

Healthcare

Diagnostic Assistants on EHR
45min → 8min diagnoses. 87% → 94% accuracy. Read-only enforced.
HIPAA · Scope binding · Audit trail

Legal

Document-Review Agents
M&A due diligence on 50K documents. Court-admissible chain of custody.
Cryptographic signatures · Discovery-ready · Tamper-proof

Supply Chain

Automated Customs Brokers
10K → 50K declarations/day. 2-4h → ≤5min. €2.4M saved/year.
Agent-signed declarations · Regulatory compliance · Cost reduction

Public Sector

Visa & Tax Processing
3 weeks → 48h visas. 5h → 2min tax refunds. €65M/year saved.
EU AI Act · Transparency · Scale

Regulatory Frameworks & Threat Models

Understanding the patterns across all case studies.

1. Risk Management in Autonomous Trading

The Scenario

A fintech company operates an autonomous trading system with 7 interconnected AI agents:

• Data Ingestion — pulls market feeds from multiple sources
• Feature/Signal Processing — derives trading indicators
• Market State — classifies market conditions
• Strategy Engine — generates trading decisions
• Risk Management — approves/rejects trades before execution
• Execution — places trades on exchanges
• Monitoring — watches the entire pipeline

The critical challenge: How do you prove that the risk engine was never bypassed, and that every trade decision is traceable, reproducible, and compliant?

The Control Framework

Using a Regulatory Control Matrix (RCM), the team maps 7 key risks to enforceable controls:

Key insight: Control R-004 (Risk Bypass) is critical. Without it, the entire system fails regulatory scrutiny.

How Kakunin Enables Compliance

The risk engine signs its approval with an X.509 certificate issued by Kakunin:

1. Risk Engine requests cert from Kakunin
   → Issues certificate tied to specific Risk Agent identity + model version
   
2. On each trade, Risk Engine signs decision with private key (in AWS KMS)
   → Signature proves: "Risk Agent v2.3.1 approved this trade at 2026-05-20T14:32:15Z"
   
3. Execution Engine validates signature before placing trade
   → Rejects any trade approved by unknown/revoked Risk Agent
   
4. Auditor replays trade decision
   → Verifies exact input data, model version, decision output, signature
   → Proves no bypass was possible

Regulatory Outcome

MiCA Compliance:

• Article 61(2): Internal controls → Risk engine signature is the control
• Article 68: Operational resilience → System-wide kill switch + immutable logs

EU AI Act Compliance:

• Article 14(4): Oversight → Human can verify every decision via audit trail
• Article 15(2): Fail-safe → Risk engine cannot be bypassed

Audit Result: ✅ Pass all 8 audit tests (data integrity, risk bypass attempts, replay attacks, spoofing, kill switch, etc.)

2. Audit Walkthrough: The Regulatory Test

What Regulators Actually Check

This case study walks through a real regulatory audit of the autonomous trading system. Instead of reviewing 500 pages of compliance documentation, auditors test the system's actual behavior under adversarial scenarios.

The 8 Audit Tests

Audit Example: Test #6 (Message Spoofing)

Auditor's Test:

Inject fake message: 
  "From: Risk Engine v2.3.1"
  "Message: Trade XYZ approved"
  "Signature: [attacker's signature]"

System Behavior (with Kakunin):

1. Execution Engine receives message
2. Checks signature against Kakunin cert
3. Signature does NOT match Risk Engine's private key
4. Message rejected, logged as security incident
5. Alert sent to ops team

Audit Result: ✅ Test passed — system cannot be spoofed

Outcome for the Fintech

• Reduced audit time from 6 months to 3 months (tests prove controls, not docs)
• No audit findings on critical controls
• Confidence for exit strategy — system is provably compliant

3. Security Architecture for High-Risk AI

The CISO's Challenge

A Chief Information Security Officer (CISO) inherits an autonomous trading system and must:

• Identify all trust boundaries
• Design fail-safe controls
• Prepare for adversarial testing
• Enable incident response

The system has 7 agents across a real-time trading pipeline—if any agent is compromised, the system can lose money at machine speed.

Threat Model

CISO's Design Principles

1. Zero Trust — No implicit trust between agents
2. Least Privilege — Each agent has minimal permissions
3. Deterministic Auditability — All decisions reproducible
4. Fail-Safe — System defaults to safe state on failure

How Kakunin Reduces CISO Risk

Before Kakunin:

• No proof that Risk Agent's approval wasn't forged
• No binding between agent identity and model version
• Manual audit logs could be modified
• Inter-agent messages unverified

After Kakunin:

• Every Risk Agent approval is cryptographically signed
• Certificate ties identity to model version hash
• Append-only audit logs (WORM storage)
• Message spoofing becomes mathematically impossible

CISO's Final Assessment

"Kakunin doesn't eliminate insider threat risk, but it makes containment guaranteed. If a key is compromised, we can see exactly when and revoke the certificate. If an agent is hacked, we can prove what it did and roll it back. That's what regulators want to see."

4. Institutional-Grade Compliance

The Maturity Model

Compliance isn't binary (compliant vs. non-compliant). Regulators assess control maturity across 5 levels:

Most fintech systems reach L2–L3. Kakunin enables L4–L5 automatically:

• L4 Automation: Every control is cryptographically enforced
• L5 Adaptation: Audit logs feed into behavioral anomaly detection

Control-to-Architecture Mapping

Evidence Automation

Compliance requires evidence. Instead of manual log extraction, Kakunin automates:

Raw Events
  → Signed by agents using Kakunin certs
  → Streamed to immutable log (WORM storage)
  → Indexed and queryable
  → Audit-ready export per trade / per control

Impact: Audit prep time drops from weeks to hours.

Institutional Positioning

A fintech approaching regulatory approval:

• Week 1: Deploy Kakunin, issue certs to all agents
• Week 2: Run synthetic audit tests, verify all controls pass
• Week 3: Extract audit evidence via Kakunin dashboard
• Week 4: Present to regulator

Total time to regulatory approval: 4–6 weeks (vs. 6–12 months without automation)

The Kakunin Difference

All 6 case studies share the same architectural pattern:

1. Cryptographic Identity — Agents issued X.509 certificates tied to specific scope (e.g., max €50M transaction)
2. Behavioral Proof — Every action signed by agent + timestamped in immutable audit log
3. Auto-Revocation — Risk score ≥0.85 → certificate revoked ≤60 seconds
4. Regulatory Audit — Regulators verify independently via public /v1/verify endpoint

Result: Compliance audited by system behavior, not documentation.

Getting Started

• Concepts — Understand the regulatory frameworks (MiCA, EU AI Act)
• Agent Registration — Set up your first AI agent
• Certificates — Issue X.509 identity to agents
• Event Ingest — Stream behavior events for monitoring
• Verification — Enable counterparties/regulators to verify independently