Building a PCI-DSS Compliant AI Security Cage for Banking in 15 Seconds

The biggest barrier to adopting AI in banking isn't model capability — it's compliance. How do you use powerful LLMs like Grok or GPT-4 on customer transaction data without triggering a PCI-DSS or BSA/AML violation?

The answer isn't to avoid AI, but to wrap it in a Security Cage.

The Problem

Every compliance officer in banking knows the dilemma: AI models need data to be useful, but the data they need — account numbers, routing numbers, transaction histories — is exactly what regulations say you must protect. Traditional approaches force a choice: use AI and accept risk, or stay compliant and stay manual.

AKIOS eliminates that tradeoff.

The Regulatory Landscape

Financial institutions operate under some of the most demanding regulatory frameworks in the world:

AKIOS enforces these at the runtime level — the AI agent never operates outside the compliance boundary.

The Concept: Policy as Code

AKIOS introduces the concept of a "Security Cage" — an ephemeral, sandboxed runtime environment where data is processed under strict, code-defined policies. Unlike traditional compliance that relies on documentation and trust, the Security Cage makes violations physically impossible at the infrastructure level.

The Workflow: Automated AML Risk Analysis

Architecture

graph LR
    CBS["Core Banking\nSystem"] -->|"transaction batch\n(encrypted)"| FS["filesystem agent\nread-only ingest"]

    subgraph CAGE["AKIOS Security Cage"]
        FS --> PII["PII Redaction Engine\n«CC» «SSN» «ACCT» «RTN»"]
        PII --> LLM["llm agent\nAML pattern analysis"]
        LLM --> TE["tool_executor\nrisk scoring engine"]
        TE --> VALID["Output Validation\nno-raw-data check"]
        VALID --> MERKLE["Merkle Audit Chain\nSHA-256 signed"]
        MERKLE --> COST["Cost Kill-Switch\n$1.00 / batch budget"]
    end

    COST -->|"flagged transactions\n(redacted)"| Alert["Alert System\nSAR Dashboard"]
    Alert --> Analyst["BSA/AML\nAnalyst"]
    MERKLE -->|"audit export\n(immutable)"| Compliance["Compliance\nAudit Trail"]
    Compliance --> OCC["OCC / FFIEC\nExaminers"]

Policy Configuration

The entire compliance posture is defined in a single YAML file:

# banking-aml-policy.yml
security:
  sandbox: strict
  network: isolated
  allowed_endpoints:
    - core-banking-api.internal:443
  pii_redaction:
    enabled: true
    patterns: [credit_card, ssn, account_number, routing_number, iban]
    mode: aggressive
  budget:
    max_cost_per_run: 1.00
    currency: USD
  audit:
    merkle_chain: true
    export_format: jsonl
    retention_days: 2555  # 7 years — BSA requirement

What the Analyst Sees

When the workflow completes, the BSA/AML analyst receives a clean report:

No SSNs. No account numbers. No raw data. Just actionable intelligence with a cryptographic proof chain.

Why It Matters

• Zero Data Exposure: Account numbers and PII are redacted before the AI processes anything. Even if the model is compromised, there's nothing to leak.
• Auditable Decisions: Every fraud flag includes a cryptographic proof chain. OCC examiners can trace exactly how a decision was made.
• Cost Containment: Hard budget limits per batch prevent runaway API costs — a real concern when scanning millions of transactions.
• Model Risk Management: The sandbox prevents the AI from taking actions beyond its scope. It can flag, but it cannot move money, close accounts, or modify records.
• 7-Year Audit Retention: Merkle chain logs are exportable in JSONL format, meeting BSA's 7-year retention requirement out of the box.

Try It Yourself

AKIOS is open-source. You can run this exact workflow today:

pip install akios
akios init my-project
akios run templates/file_analysis.yml

Secure your AI. Build with AKIOS.