Sandboxed AI for Pharma: Accelerating Clinical Trials Under FDA Controls

Pharmaceutical companies spend an average of $2.6 billion and 10–15 years to bring a single drug to market. AI can compress that timeline — analyzing trial data, identifying adverse events, and optimizing patient cohorts. The problem: clinical trial data contains the most sensitive health information imaginable, and the FDA demands complete reproducibility.

AKIOS solves this with the Security Cage: an ephemeral, sandboxed runtime where AI processes trial data under strict, code-defined policies.

The Problem

Clinical trials generate enormous datasets: adverse event reports, lab results, patient-reported outcomes, imaging data. Human reviewers take weeks to identify safety signals that AI can spot in minutes. But connecting AI to trial data creates a fundamental tension: the FDA requires complete reproducibility and auditability for every analysis, while privacy regulations demand that patient identifiers never be exposed.

AKIOS gives you AI-powered signal detection with FDA-grade auditability and zero patient data exposure.

The Regulatory Landscape

Pharma in the US operates under a tightly interlocking regulatory stack:

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. For pharma, the key feature is deterministic reproducibility: the same dataset processed in the same cage always produces the identical output, satisfying FDA requirements for analysis validation.

The Workflow: Clinical Trial Data Analysis

Architecture

graph LR
    CTMS["Clinical Trial\nDatabase (EDC)"] -->|"subject data\n(encrypted)"| FS["filesystem agent\nread-only"]

    subgraph CAGE["AKIOS Security Cage"]
        FS --> PII["De-identification Engine\n«SUBJECT_ID» «SITE» «PI_NAME»"]
        PII --> LLM["llm agent\nsafety signal detection"]
        LLM --> TE["tool_executor\nstatistical validation"]
        TE --> VALID["Output Validation\nno raw patient data"]
        VALID --> MERKLE["Merkle Chain\n21 CFR Part 11 signed"]
        MERKLE --> COST["Cost Kill-Switch\n$1.00 / analysis"]
    end

    COST -->|"safety report\n(de-identified)"| Report["Regulatory\nSubmission"]
    Report --> Medical["Medical Officer\n/ Safety Board"]
    MERKLE -->|"audit export\n(immutable)"| QA["Regulatory Affairs"]
    QA --> FDA["FDA / EMA\nInspection"]

Policy Configuration

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

# pharma-clinical-trial-policy.yml
security:
  sandbox: strict
  network: isolated
  allowed_endpoints: []  # zero network access
  pii_redaction:
    enabled: true
    patterns: [subject_id, site_code, investigator_name, ssn, dob, mrn]
    mode: aggressive
  budget:
    max_cost_per_run: 1.00
    currency: USD
  audit:
    merkle_chain: true
    export_format: jsonl
    retention_days: 5475  # 15 years — FDA clinical trial retention
    cfr_part_11: true  # electronic signature compliance
  constraints:
    deterministic: true  # same input always produces same output
    trial_record_modification: disabled

What the Medical Officer Sees

At the end of the workflow, the medical safety board receives a structured report:

No patient names. No site identifiers. No investigator details. Just clinically actionable safety intelligence with an FDA-grade audit chain.

Why It Matters

• Zero Data Leakage: Patient identifiers are stripped before any AI processing. The model never sees who a patient is — only de-identified clinical outcomes.
• 21 CFR Part 11 Compliance: Every AI output includes electronic signatures and is stored in tamper-evident audit logs that satisfy FDA inspection requirements.
• Reproducible Analysis: The Security Cage's deterministic runtime means any analysis can be exactly reproduced months or years later for an FDA audit.
• Accelerated Timelines: AI can scan thousands of adverse event reports in minutes, flagging safety signals that would take human reviewers weeks to identify.
• Multi-Site Trial Support: De-identification ensures GDPR compliance for EU trial sites. Data sovereignty is enforced by infrastructure, not policy.

Try It Yourself

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

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