A production implementation of the architecture I described in The Secure Intelligence Framework: Architecting AI Systems for a Data-Driven World, published in CIO Magazine, April 2026.
Six months into a large-scale ML deployment, my team discovered that one of our inference pipelines was leaking sensitive customer fields into downstream systems that had no business seeing them. No external breach, but the internal exposure was real and the remediation cost was painful. That incident forced a hard look at how we'd sequenced things: model first, security later. Classic mistake.
This repository is the practical output of rebuilding that architecture the right way. The framework treats every layer of an AI system — data ingestion, model interaction, governance — as a potential failure point. It applies zero-trust thinking to pipelines, not just networks.
If you're building AI in healthcare, insurance, financial services, or any domain where data misuse has regulatory or ethical consequences, this framework gives you a starting point that isn't naive.
Most AI security guidance stops at "encrypt your data" and "use RBAC." That's table stakes. The real problems I kept running into were:
- Data pipelines with no lineage. When something went wrong, no one could answer "what data trained that model?" That's an audit and debugging problem simultaneously.
- Model endpoints treated like internal microservices. No rate limiting, no output inspection, no adversarial testing. One prompt injection away from a bad day.
- Governance that lived in a spreadsheet. Who owns this model? Who approved it? When was it last reviewed? Nobody knew.
SIF addresses all three with a layered approach: secure the data before it touches a model, harden the model interface, then put a governance wrapper around the whole thing that actually gets used.
┌─────────────────────────────────────────────────────────────────┐
│ SECURE INTELLIGENCE FRAMEWORK │
│ │
│ ┌─────────────┐ ┌──────────────┐ ┌───────────────────┐ │
│ │ DATA LAYER │───▶│ MODEL LAYER │───▶│ GOVERNANCE LAYER │ │
│ └─────────────┘ └──────────────┘ └───────────────────┘ │
│ │ │ │ │
│ Trust Score Security Policy Model Registry │
│ Lineage Track Output Filter Audit Log │
│ PII Scanner Rate Limiter Review Cycles │
│ Schema Valid. Inject Detect. Owner Assignment │
└─────────────────────────────────────────────────────────────────┘
See /architecture for full Mermaid diagrams and layer-by-layer breakdowns.
secure-intelligence-framework/
├── src/sif/
│ ├── trust_scoring_engine.py # Core trust scoring for data sources
│ ├── data_validation_pipeline.py # Schema, PII, and quality validation
│ ├── security_policy_enforcer.py # Endpoint hardening, output filtering
│ ├── model_gateway.py # Unified inference entrypoint
│ ├── lineage_tracker.py # Data provenance and audit trail
│ ├── audit_logger.py # Structured event logging
│ └── governance/
│ ├── model_registry.py # Model catalog with ownership metadata
│ └── policy_manager.py # Policy definitions and enforcement
├── tests/
│ ├── test_trust_scoring.py
│ ├── test_data_validation.py
│ ├── test_security_enforcer.py
│ └── test_model_gateway.py
├── examples/
│ ├── healthcare_pipeline.py # End-to-end: patient data → model → audit
│ ├── insurance_claims_pipeline.py # Claims triage with trust scoring
│ └── sample_data/
│ ├── patient_records.json
│ └── claims_data.json
├── architecture/
│ ├── overview.md
│ ├── data_layer.md
│ ├── model_layer.md
│ ├── governance_layer.md
│ └── diagrams/ # Mermaid source files
├── docs/
│ ├── design_decisions.md
│ ├── security_considerations.md
│ ├── scalability.md
│ └── trade_offs.md
├── artifacts/
│ ├── model_cards/ # Model card template + filled examples
│ └── audit_reports/ # Sample audit output (JSON)
├── Dockerfile
├── docker-compose.yml
├── pyproject.toml
└── requirements.txt
Every vulnerability I've seen in production ML starts here. The data layer enforces:
- Least-privilege access: each pipeline component gets credentials scoped to exactly what it needs. Nothing more.
- Trust scoring: incoming data gets a composite score based on source reputation, schema conformance, completeness, recency, and PII risk. Low-scoring data is flagged or quarantined, not silently passed through.
- Lineage tracking: every dataset that touches a model gets a provenance record. Training run, feature extraction job, inference batch — all logged with timestamps, user context, and data hashes.
We ran this layer first in a real deployment and surfaced three data access issues in the first week. Two internal teams were querying datasets they'd never been authorized to use. Not malicious — just nobody had ever put a gate there before.
The model layer treats inference endpoints like sensitive APIs, because that's what they are. It adds:
- Authentication and rate limiting on every endpoint, even internal ones
- Prompt injection detection for LLM-backed systems (pattern matching + embedding similarity)
- Output filtering: responses are scanned for PII before they leave the system. We added this to an NLP knowledge management tool; it added ~40ms of latency and caught 11 real PII exposures in the first month.
- Adversarial testing baked into CI/CD — model deployments that fail security checks don't get promoted to production
Governance is what keeps the other two layers working six months from now when the original team has moved on. The implementation here covers:
- Model registry with mandatory ownership fields, approval status, and review schedules
- Policy manager that enforces governance rules programmatically (not just in a wiki)
- Audit logging in structured JSON, queryable by model, dataset, user, or time window
- Model cards — not the Google template copy-pasted into a doc, but actual filled-out cards with known limitations, performance on subgroups, and sign-off history
A regional health network uses a variant of this framework to route patient records through diagnostic ML models. The trust scoring module flags any record with missing required fields or unusual value distributions before it reaches inference. The output filter strips any fields not explicitly in the response schema before results are returned to the clinical application. Lineage records every model version that touched a patient record, which matters enormously for FDA audit purposes.
See examples/healthcare_pipeline.py.
A P&C insurer uses the framework for claims routing. Incoming claims data is scored for trust (source system reliability, data completeness, time since event), then passed through a classification model. The security enforcer ensures that the model can only access the fields it was trained on — adjuster IDs, payment amounts, and claimant SSNs are stripped at the gateway before the model ever sees the request.
See examples/insurance_claims_pipeline.py.
- Python 3.11+
- Docker (optional, for containerized runs)
git clone https://github.com/sunilkumarmudusu/secure-intelligence-framework.git
cd secure-intelligence-framework
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -e ".[dev]"python examples/healthcare_pipeline.py
# Expected output:
# [2026-04-15 09:12:03] INFO | Pipeline started | dataset=patient_records_Q1_2026
# [2026-04-15 09:12:03] INFO | Trust score: 0.84 | source=EHR_SYSTEM_01 | records=247
# [2026-04-15 09:12:04] WARN | 3 records quarantined | reason=schema_violation
# [2026-04-15 09:12:04] INFO | PII scan complete | fields_stripped=['ssn','dob'] | clean=244
# [2026-04-15 09:12:05] INFO | Inference complete | model=diagnostic_v2.3 | latency_ms=312
# [2026-04-15 09:12:05] INFO | Audit record written | run_id=a3f8b1c2pytest tests/ -v
# Full output in tests/SAMPLE_OUTPUT.mddocker-compose up --build
# The framework exposes:
# :8080 - Model gateway (inference endpoint)
# :8081 - Audit log API
# :8082 - Governance dashboard (basic)All runtime configuration lives in config.yaml. The module reads from environment variables first, then falls back to the config file. Nothing sensitive goes in the config file — credentials are expected in environment variables or a secrets manager.
# config.yaml (excerpt)
trust_scoring:
minimum_acceptable_score: 0.65
quarantine_below: 0.40
pii_field_patterns:
- "ssn"
- "date_of_birth"
- "dob"
- "credit_card"
- "member_id"
security:
rate_limit_rpm: 120
output_filter_enabled: true
prompt_injection_threshold: 0.78
governance:
require_model_owner: true
review_cycle_days: 90
min_approval_tier: "senior_engineer"You don't have to implement everything at once. In practice, I've rolled this out over three quarters:
| Quarter | Focus | Key Activities |
|---|---|---|
| Q1 | Data Layer | Pipeline audit, RBAC implementation, lineage tracking setup |
| Q2 | Model Layer | Endpoint hardening, output filtering, adversarial testing in CI/CD |
| Q3 | Governance | Model registry population, owner assignment, review cycle automation |
Teams that try to do all three simultaneously usually end up doing none of them properly.
This is a working framework, not a finished product. Issues and PRs are welcome, especially around:
- Additional PII detection patterns for non-US jurisdictions
- Integration examples for specific model serving platforms (Triton, vLLM, SageMaker)
- Governance workflow integrations (Jira, ServiceNow, etc.)
- IBM Cost of a Data Breach Report 2024
- NIST AI Risk Management Framework
- OWASP Top 10 for Large Language Model Applications
- Microsoft Responsible AI Standard
- Google Model Cards
- Original CIO Magazine Article
MIT. Use it, adapt it, just don't strip the attribution if you're building on the framework directly.
Sunil Kumar Mudusu — AI & Data Engineering Leader
LinkedIn