AI Governance, Compliance & Data Privacy

The 5-Phase Compliant AI Data Lifecycle

import re
import uuid
import hashlib
from typing import Dict, Tuple

class ProductionPIIVault:
    """
    High-throughput DLP pipeline utilizing regex fast-paths and 
    reversible in-memory cryptographic tokenization for LLM payloads.
    """
    def __init__(self, salt: str):
        self.salt = salt
        # Compile high-performance regex patterns
        self.patterns = {
            "EMAIL": re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'),
            "PHONE": re.compile(r'\b(?:\+?1[-. ]?)?\(?([0-9]{3})\)?[-. ]?([0-9]{3})[-. ]?([0-9]{4})\b'),
            "SSN": re.compile(r'\b\d{3}-\d{2}-\d{4}\b'),
            "CREDIT_CARD": re.compile(r'\b(?:\d{4}[- ]?){3}\d{4}\b'),
            "API_KEY": re.compile(r'\b(?:sk-[a-zA-Z0-9]{32,48}|ghp_[a-zA-Z0-9]{36})\b')
        }

    def tokenize_payload(self, text: str) -> Tuple[str, Dict[str, str]]:
        """
        Replaces sensitive entities with isolated opaque tokens.
        Returns: (sanitized_prompt, reverse_lookup_vault)
        """
        vault: Dict[str, str] = {}
        sanitized = text

        for entity_type, pattern in self.patterns.items():
            matches = list(set(pattern.findall(sanitized)))
            for match in matches:
                # Handle regex tuples from grouped patterns
                raw_val = match if isinstance(match, str) else "".join(match)
                if not raw_val:
                    continue

                # Generate deterministic short token hash with salt
                token_hash = hashlib.sha256(f"{self.salt}:{raw_val}".encode()).hexdigest()[:8]
                token = f"<TOKEN_{entity_type}_{token_hash}>"
                
                vault[token] = raw_val
                sanitized = sanitized.replace(raw_val, token)

        return sanitized, vault

    def rehydrate_payload(self, text: str, vault: Dict[str, str]) -> str:
        """
        Reverses tokenization on model output before presenting response to user.
        """
        hydrated = text
        for token, raw_val in vault.items():
            hydrated = hydrated.replace(token, raw_val)
        return hydrated

# Demonstration
vault = ProductionPIIVault(salt="enterprise_secret_salt_2026")
prompt = "Hi, my name is John Doe, email is john.doe@fintech.io, SSN is 123-45-6789. Can you review my loan status?"
sanitized, lookup = vault.tokenize_payload(prompt)
print(f"Sanitized Prompt for LLM:\n{sanitized}")
# <TOKEN_EMAIL_3a9f1b2c> and <TOKEN_SSN_98e1f04a> sent to model; 0 PII leaves corporate boundary.

The AI Deletion Complexity Matrix

Multi-Region Sovereign Inference Routing

The AI Gateway must inspect tenant residency metadata and route inference strictly to geographically compliant GPU clusters:

from typing import Dict, Any

class ABACGatekeeper:
    """
    Evaluates fine-grained contextual policies before permitting 
    an AI Agent to invoke destructive or high-liability tools.
    """
    def authorize_tool_call(self, user_ctx: Dict[str, Any], tool_name: str, tool_args: Dict[str, Any]) -> Dict[str, Any]:
        # Rule 1: Bulk data extraction requires executive permission
        if tool_name in ["sql_query", "export_table"]:
            query = tool_args.get("query", "").lower()
            if "select *" in query or "drop" in query or "delete" in query:
                return {"decision": "DENY", "reason": "Unbounded wildcard or destructive SQL forbidden"}
            if user_ctx.get("role") != "DATA_ENGINEER":
                return {"decision": "DENY", "reason": "Insufficient role for raw database querying"}

        # Rule 2: Financial transactions exceed threshold -> Require Human-in-the-Loop
        if tool_name == "process_refund":
            amount = float(tool_args.get("amount", 0.0))
            if amount > 500.0:
                return {
                    "decision": "REQUIRE_APPROVAL",
                    "approver_role": "FINANCE_MANAGER",
                    "reason": f"Refund amount ${amount} exceeds automated $500 ceiling"
                }

        # Rule 3: Tenant Boundary Isolation
        if tool_args.get("tenant_id") != user_ctx.get("tenant_id"):
            return {"decision": "DENY", "reason": "Cross-tenant data access violation"}

        return {"decision": "ALLOW", "reason": "All ABAC predicates satisfied"}

Cryptographic Audit Ledger Block Schema

If any historical record is modified or deleted, the downstream cryptographic hashes break immediately, providing indisputable mathematical evidence of log tampering during forensic audits.

package ai.governance

default allow = false
default require_human_approval = false

# Allow inference only if model is in approved enterprise registry
allow {
    input.model_id == data.approved_models[_]
    input.user.tenant_id == input.target_tenant_id
    not pii_unredacted_detected
}

# Require Human Approval for high-liability agent tool executions
require_human_approval {
    input.tool_call.name == "execute_funds_transfer"
    input.tool_call.arguments.amount > 1000
}

# Enforce zero unredacted PII in production payloads
pii_unredacted_detected {
    count(input.detected_pii_entities) > 0
    input.pii_handling_mode != "TOKENIZED"
}

from fastapi import FastAPI, HTTPException, Header, Depends, status
from pydantic import BaseModel, Field
from typing import Dict, List, Optional
import time, uuid, hashlib, hmac

app = FastAPI(title="Enterprise AI Governance Gateway", version="2.4.0")

class InferenceRequest(BaseModel):
    model_id: str
    prompt: str
    tenant_id: str
    user_id: str
    user_role: str
    tool_calls_requested: Optional[List[str]] = []

class InferenceResponse(BaseModel):
    transaction_id: str
    status: str
    response_text: str
    audit_block_hash: str
    execution_time_ms: float

# Global Cryptographic Audit Chain State (In-Memory for demonstration)
AUDIT_CHAIN: List[Dict] = []
SECRET_AUDIT_KEY = b"enterprise_super_secret_audit_hmac_2026"

def get_prev_block_hash() -> str:
    if not AUDIT_CHAIN:
        return "0" * 64
    return AUDIT_CHAIN[-1]["block_hash"]

@app.post("/v1/governed-infer", response_model=InferenceResponse)
async def governed_inference(req: InferenceRequest):
    t_start = time.time()
    tx_id = f"tx_{uuid.uuid4().hex[:12]}"

    # Step 1: Model Registry Authorization
    APPROVED_MODELS = {"claude-3-5-sonnet-v2", "gpt-4o-enterprise-zdr", "llama-3-70b-frankfurt"}
    if req.model_id not in APPROVED_MODELS:
        raise HTTPException(
            status_code=status.HTTP_403_FORBIDDEN,
            detail=f"Model '{req.model_id}' is not in the Enterprise Approved Registry"
        )

    # Step 2: In-line PII Tokenization & DLP Inspection
    vault = ProductionPIIVault(salt=req.tenant_id)
    sanitized_prompt, pii_lookup = vault.tokenize_payload(req.prompt)

    # Step 3: ABAC Tool Policy Evaluation
    gatekeeper = ABACGatekeeper()
    for tool in req.tool_calls_requested:
        decision = gatekeeper.authorize_tool_call(
            user_ctx={"tenant_id": req.tenant_id, "role": req.user_role},
            tool_name=tool,
            tool_args={"tenant_id": req.tenant_id}
        )
        if decision["decision"] == "DENY":
            raise HTTPException(
                status_code=status.HTTP_403_FORBIDDEN,
                detail=f"Tool '{tool}' authorization denied: {decision['reason']}"
            )

    # Step 4: Dispatch to Compliant Model Provider (Simulated)
    # In production: invokes model endpoint with sanitized_prompt
    mock_model_raw = f"Processed sanitized request successfully with {len(pii_lookup)} protected entities."
    
    # Step 5: Output Rehydration (Safe presentation)
    final_output = vault.rehydrate_payload(mock_model_raw, pii_lookup)

    # Step 6: Append to Cryptographic Audit Ledger
    prev_hash = get_prev_block_hash()
    prompt_hash = hashlib.sha256(sanitized_prompt.encode()).hexdigest()
    block_payload = f"{prev_hash}:{tx_id}:{req.tenant_id}:{req.user_id}:{prompt_hash}:{req.model_id}"
    block_hash = hmac.new(SECRET_AUDIT_KEY, block_payload.encode(), hashlib.sha256).hexdigest()

    audit_entry = {
        "index": len(AUDIT_CHAIN) + 1,
        "transaction_id": tx_id,
        "timestamp": time.time(),
        "prev_hash": prev_hash,
        "block_hash": block_hash,
        "tenant_id": req.tenant_id,
        "user_id": req.user_id,
        "model_id": req.model_id,
        "pii_entity_count": len(pii_lookup)
    }
    AUDIT_CHAIN.append(audit_entry)

    elapsed_ms = round((time.time() - t_start) * 1000, 2)
    return InferenceResponse(
        transaction_id=tx_id,
        status="GOVERNED_SUCCESS",
        response_text=final_output,
        audit_block_hash=block_hash,
        execution_time_ms=elapsed_ms
    )